EPod: An Engineering Podcast

On this episode, Justin Kyle Bush talks with Ian McIntosh, who is a program director for UW-Madison, Office of Interdisciplinary Professional Programs (InterPro). He joined the InterPro Facilities and Building Systems team in December 2020 after being a frequent lecturer and course developer for the previous 20 years. Ian completed his PhD in Mechanical Engineering from the Solar Energy lab right here at UW-Madison. In this interview, he talks about the difference between open enrollment and custom courses, why professional development is important for retention and onboarding employees today, and his passion for project management.

Dr. Ian McIntosh is a program director for the University of Wisconsin-Madison, Office of Interdisciplinary Professional Programs (InterPro). He joined the InterPro Facilities and Building Systems team in December 2020 after being a frequent lecturer and course developer for the previous 20 years. His responsibilities include teaching, directing and business development for commissioning certification courses related to project management, existing buildings, and building enclosures. He is also the owner and founder of ianTEACH LLC, which provides innovative and strategic performance-based training and coaching to engineers, architects, construction and facilities management professionals in various sectors of the national building industry.

He is an Accredited Commissioning Process Manager (CxM) with over twenty years of dedicated experience in Total Building Commissioning (a quality process) and has managed numerous energy, commissioning, retro-commissioning, and LEED® commissioning projects throughout the nation. As a Commissioning Provider and Mechanical Engineer, Dr. McIntosh has prepared and led several Owner’s Project Requirements (OPR) workshops, written and integrated numerous commissioning specifications, co-invented and implemented electronic bar-coded checklists, system manuals and tracked owner cost savings. He has also executed hands-on in-depth field work, performed functional performance testing of complex HVAC systems/subsystems and coordinated full-scale operation & maintenance (O&M) and user training programs.

Ian holds Bachelor and Master’s degrees in Mechanical Engineering from the City College of the City University of New York (CCNY) and earned his Doctorate degree in Mechanical Engineering from the Solar Energy Lab at UW-Madison. He and his wife of 18 years like to spend quality time with their four children.

On this episode, Rebecca Jamieson talks with Ernest Nicolas, the senior vice president and chief supply chain officer for Rockwell Automation. Ernest speaks about how his many connections across UW-Madison contributed to his professional success, the value of lifelong learning, and the urgent need for the fields of engineering and the supply chain to increase diversity, equity, and inclusion.

Ernest Nicolas is senior vice president and chief supply chain officer for Rockwell Automation. In this global leadership role, Ernest has overall responsibility of the end-to-end supply chain for Rockwell. He joined the team in 2006 as a Lean, Six Sigma Project Manager and has held several roles of increased responsibility within the Integrated Supply Chain (ISC) functions, overseeing the process of planning, sourcing, producing and delivering Rockwell products and services to customers.

Prior to his current role, Ernest most recently served as Senior Vice President, Operations & Engineering Services (now known as ISC), and before that, he was Vice President, Global Supply Chain. Before joining Rockwell Automation, Ernest held supply chain, manufacturing engineering and manufacturing operations positions with the General Motors Corporation.

Ernest holds a Bachelor’s degree in Manufacturing Systems Engineering from Kettering University in Flint, Michigan, an MBA in Operations Management and Master’s degree in Manufacturing Systems Engineering, both from the University of Wisconsin-Madison. He is also a member of the UW E-Business Consortium. 

Ernest is a member of the Executive Leadership Council. In both 2019 and 2020, he was named to Empower’s 100 Ethnic Minority Executives Role Model List. In 2020, Ernest was named to Savoy Magazine’s Most Influential Black Executives in Corporate America. Ernest also serves on the Board of Directors for the Milwaukee Urban League. He is an Executive Advisory Board member for the University of Wisconsin-Madison’s Supply Chain Management program within the School of Business.

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On this episode, Susan Ottmann talks with Dr. Jeff Linderoth from the Department of Industrial Systems and Engineering about engineering data analytics and design optimization. Jeff discusses the fundamentals of optimization and how it fits into a variety of applications with global implications.

Jeff Linderoth is a professor and department chair of the Department of Industrial Systems and Engineering at UW-Madison and a fellow with the Wisconsin Institute for Discovery. His research focuses on modeling and solving real-world, large-scale optimization problems. Specific research areas within optimization include integer programming used for modeling yes/no decisions, and stochastic programming useful for decision making under uncertainty. Jeff’s research places a particular emphasis on developing high-performance, distributed optimization algorithms, and software. He has an MS in  Operations Research and a PhD in Industrial Engineering from the Georgia Institute of Technology. In his free time, he enjoys golf, tennis, cooking, and chess.

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Justin Kyle Bush: Welcome to Epod, a podcast from UW-Madison’s College of Engineering’s Office of Interdisciplinary Professional Programs. These podcasts are focused on big ideas and engineering and the people behind them. My name is Justin Kyle Bush, and I’m your host. On today’s episode, Susan Ottmann talks to Dr. Jeff Linderoth from the Department of Industrial Systems and Engineering about engineering data analytics and design optimization. Jeff discusses the fundamentals of optimization and how it fits into a variety of applications with global implications. Take it away, Susan.

Susan Ottmann: Welcome Jeff.

Jeff Linderoth: Well, it’s fabulous to be here. Thank you, Susan.

Susan Ottmann: Before we start, Jeff, please give us your view of the Engineering Data Analytics program at UW-Madison.

Jeff Linderoth: Well, what I think really sets the Engineering Data Analytics program apart is its engineering focus. If you look around there are many Master of Data Science or Master of Analytics programs in the marketplace today. And those are great and they are going to teach you tools and concepts to effectively analyze data. The MEDA program here at UW-Madison actually goes beyond that and shows you how to apply that data to solve engineering problems. I mean, the classes on engineering design systems, reliability, simulation modeling, just to name a few, really give the program an engineering focus that I believe sets it apart.

Susan Ottmann: Let’s dive a bit deeper and talk about design optimization and why this is important for engineers focused on data.

Jeff Linderoth: Well first, optimization is a mathematical discipline that’s all about finding the best solution to a problem, given constraints or scarce resources. Second, and I’m probably not going to surprise anyone with this observation, is that there really is a data revolution going on today.

More and more companies are marrying these two concepts of optimization and data. In fact, it’s led to a new term of art called data-driven optimization. And this is where the model of a process or a system is deduced or inferred solely from data or historical observations. Even more exciting is when this data can help inform an engineering-based model, maybe a math-based model,

and this is done via estimation of key parameters in the system. Once the parameters are estimated with data, that design and design optimization process can begin. So, I mean, if you’re an engineer focused on data, doing optimization, design optimization, really is one of the most important tools that you can have in your toolbox.

Susan Ottmann: I find the whole area of design optimization. Fascinating. We’ve got optimization courses available in mechanical and industrial systems. How do you see the applications differing across the courses in your department and in mechanical engineering?

Jeff Linderoth: Yeah. Well, one of the differences is that some of the courses anyway, in industrial and systems engineering, the courses are a little bit more focused on theory and algorithms.

But really, I believe the fundamental difference between optimization and mechanical engineering and an industrial and systems engineering is on the class of applications problems. In fact, you know, we added the “systems” name to our department about 15 years ago to acknowledge the important role that systems play in our discipline.

Maybe I can best, uh, answer your question with an example. So imagine two engineers working at the same factory. Mechanical engineers would potentially be employed analyzing, you know, optimizing a specific process within the factory, maybe a machining process to minimize component wear.

Industrial and systems engineers working at that same factory would be using optimization to lay out the shop floor holistically to maximize product throughput. Now, both of these are extremely important, both the finer level of detail to get the individual processes right and efficient, but then also how to connect these processes together in the most efficient way.

Susan Ottmann: I love process, but I’m also a mechanical engineer and that makes a lot of sense to me. What about those electrical engineers out there who are enrolled in our degree program? How do you see design optimization applying in their work?

Jeff Linderoth: Oh man, optimization is everywhere. I have many problems in signal processing can be converted to optimization problems. Actually one of the most classical applications of design optimization is in tuning and in the optimal design of circuits for performance and reliability. I myself actually have done optimization for the design and operation of electrical power grids, and I’ve used optimization in VLSI chip design.

I mean, honestly, there are optimization problems and every discipline of engineering. The world needs all types of engineers and, in my opinion anyway, all engineers should know about optimization.

Susan Ottmann: One of your research areas is global optimization. Please tell us more about your research and interest in this area.

Jeff Linderoth: Well, if I had to sum up my research, it would be that I like to try and solve the hardest classes of optimization problems and global optimization is one of these hardest classes. So, optimization is like trying to find the top of a mountain. And the design landscape can be very hilly. Many optimization methods look locally. So they might look at where they’re standing in the design space and they look around at the neighboring designs. And if they don’t see any, any close neighboring design, that’s say, better than they are. Then they declare themselves to be, “I am the best design. I am optimal.”

However, way out in the distance beyond their neighborhood, there may be a higher peak, there may be a better design. And global optimization is all about studying the mathematical properties of the optimization problem and designing algorithms that can find that higher peak. Not only that, but it can prove mathematically that no, a better solution exists—there doesn’t exist a higher peak.

So I also love it when my algorithms and analysis can really make a difference. So I love teaming with engineers and scientists to help solve the optimization problems in their domain that will make a difference.

Susan Ottmann: Jeff, I know you’ll be off on sabbatical next year. Can you give us a sneak preview into the research you’ll be pursuing?

Jeff Linderoth: Uh, well, besides trying to lower my golf handicap for one…actually, no, I have three really exciting new projects that I’m just sinking my teeth into that I think will take a lot of my energy during my sabbatical. The first one is a machine learning application. This is a little bit like the very well-known Netflix challenge where we are given us a subset of user ratings for movies and the goal or the task is to try to predict or complete the ratings for movies that the user hasn’t seen. However, in this application, a special twist is that the users may come from completely different classes of individuals. So it’s a double challenge of first trying to infer from the data which class a user falls into, and then given that, to try to complete his or her ratings of the movies. And actually, the actual application of this is actually in drug design, trying to predict drug disease, interactions, not user, uh, movie ratings.

A second application I’ve just started is in computational, or actually, evolutionary biology. So here though, we’re given a database of potential chemical reactions and the optimization problem is to find a small so-called auto-catalytic network, which is just a series or a set of reactions that could be self-sustaining to produce organic compounds that form the basis of life.

And then a third application I’ve just starting is on the optimal deployment of Naval assets to mitigate adversary threats. So given the potential, I’ve just started working on this project, given a potential red layout and potential red threats, we have to deploy our blue assets and in a way, in an optimal way, to mitigate those threats. So those are just three—and those are all optimization problems. So those are just three of the very diverse set of optimization, or applications, of optimization.

Susan Ottmann: All I can say is wow. I find your research so interesting and believe that you’re helping to move the world forward in the area of data, data, analytics, and optimization. And just those three projects are so diverse that optimization plays a role in so many parts of daily life and engineering. Well, thank you so much for your time today. We appreciate you talking to us and informing us. Very interesting topic.

Justin Kyle Bush: Thank you for listening to Epod. For more episodes, visit www.interpro.wisc.edu/podcasts. And if you enjoyed this, don’t forget to subscribe, rate, review, and share.

On this episode, Susan Ottmann talks with Craig Lee—a Principal Group Engineering Manager with Microsoft in Redmond, Washington. Craig completed his Master of Engineering in Engineering Management right here at UW-Madison. In this interview, he provides insights into his learnings from the MEM program, the Technical Project Management course where he serves as a thought-leader, and his experience as a leader at Microsoft. 

Craig Lee is a graduate of UW-Madison Master of Engineering Management program. He is an experienced leader with over twenty years of experience in the software industry. He has worked for both small to large organizations effectively combining strong technical know-how with the ability to create and communicate a strategic vision. He has proven himself by forming, managing, and leading cross-geo and high-throughput teams and delivering quality on both v1 and established products. Craig was recently promoted to Principal Group Engineering Manager at Microsoft. He has been with Microsoft since 2006, and one of his roles has been the Principle Engineering Manager for the Power PI Interactive Data Visualization platform. He’s also held leadership roles for Microsoft Outlook and Cloud AI Platforms. Craig holds six patents and is a thought leader providing input for current students in the UW-Madison Technical Project Management course that is part of the core UW-Madison Master of Engineering curriculum.

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Justin Kyle Bush: Welcome to Epod, a podcast from the UW-Madison’s College of Engineering’s Office of Interdisciplinary Professional Programs. These podcasts are focused on big ideas and engineering and the people behind them. My name is Justin Kyle Bush, and I am your host.  

On today’s episode, Susan Ottmann talks with Craig Lee, who is the principal group engineering manager with Microsoft in Redmond, Washington. Craig completed his Master of Engineering in Engineering Management right here at UW-Madison. He will be providing insights into his learnings from the technical project management course, his program, and his experience as a leader at Microsoft. Take it away, Susan. 

[00:00:55] Susan Ottmann: To start the discussion. Craig, please share a bit about your career.  

[00:01:02] Craig Lee: Well, first of all, thank you very much, Susan, for having me today. It’s exciting to have this opportunity. So I, I have kind of a roundabout path that I took into the role that I have today. Most, most students are most, uh, uh, colleagues of mine. 

[00:01:19] You know, went through the typical computer science degree and internships, and then into the industry. But, you know, I started as a really young age with, you know, fascinated with computers had, uh, our first, my first computer at age 12. And, you know, just spend a lot of time in early years in high school, uh, you know, just really kind of fascinated with that whole PC boom that happened in the, in the eighties. But in the, in the nineties, I decided that for some reason I was going to be a lawyer and kind of took the, got a BA in political science and it was going that route. 

[00:01:55] But, um, found myself in, in Seattle, in the mid-nineties and took some side jobs and, and essentially taught myself programming, um, and software engineering and, and started getting consulting jobs and, and other, uh, gigs that then turned into actually full-time software engineering work and work through that different small companies in the Seattle area. 

[00:02:23] And then finally made my big transition to Microsoft in 2006. And that was just a lot of, you know, the result of a lot of, a lot of reading and, and on the job training and, and, you know, hard work and self-teaching um, but since then, you know, it’s been a great experience. At Microsoft, you know, it’s such a large company and there’s such a variety of products and services that are offered that I’ve, I feel like even in the almost 15 years I’ve been there, it’s been almost four or five different distinct experiences from developer tools to, you know, working in, on the outlook product, uh, spent some time working on the machine, learning tooling and platforms in dynamics. And then, now I’m in a data governance team working on internal and external privacy, operations and products. Um, and in the, in the transition to Microsoft, I also started off as an individual contributor. 

[00:03:27] Um, I had some management experience and prior to that company, uh, but then throughout my career at Microsoft has been a steady growth from, um, you know, from the, in initially individual contributor to small team lead to engineering manager. And then I think to a large degree, based on my experience with men program, uh, over the last year have moved into a manager manager role. 

[00:03:56] Um, and that’s currently where I am today and enjoying that, um, these great experiences and challenge.  

[00:04:03] Susan Ottmann: Wow, what a fascinating career. And you’ve got a long way to go from here. Today’s podcast is focused on technical project management and its impact on your ability to think strategically with executing projects. What were your biggest takeaways learned from this course that you were able to apply at Microsoft? 

[00:04:23] Craig Lee: So one of the, one of the themes that I saw recurring not only in TPM, but also throughout the mem classes, is that how you start is really almost the important part of any endeavor or project. Um, the very specifically, over the last two or three teams that I’ve been on, I’ve actually used the project overview statement template. 

[00:04:56] And it’s amazingly clarifying to take that template work through all of the sections, think about the problem and the customers and the goals and the metrics and you know, all of those aspects of the project because it just brings an incredible amount of clarity. I’ve had a number of people as we go through this process, go, wow, this is great. 

[00:05:21] I can’t believe all this clarity we’re getting, you know, from actually going through this exercise. So, um, and I just talk all the time with my team about how important it is to have a clear scope and priorities. And we invest a lot of time in those activities and those discussions, because I think having that you know starting strong is so important to being successful because if you start off, uh, it’s really hard to recover. Think another, um, another artist, you know, with, with this recent time with COVID, you know, the, the articles and slides on B teams that we talk about in TPM, I’ve just been going back to those quite frequently and like picking up different aspects of it. 

[00:06:07] And, um, one thing that I went back to fairly early and really believe in strongly was the discussion about the benefits of face-to-face meetings. Um, and then how much, even zoom and teams, these technologies don’t fully replicate that experience. And so one thing I set up very early in COVID was a kind of a daily virtual water cooler meeting where we can just get together for 15-20 minutes. And just talk about, you know – the only rule is don’t talk about work.  

Uh, but then I’ve also set up what I called social distant lunches, where everybody brings along chair and their lunch, and we gather on campus and just sit in a big circle and eat our lunch and talk. And it’s great to see people, you know, in real space, and you can have side conversations and people can get to know each other and just ways that you just can’t in a large zoom meeting.  

So, you know, those two have definitely been super critical. But one thing that I also pulled out of it was the concept of reliable promises. Um, I think in the past, the planning that I’ve done at Microsoft, there’s just, there’s a variety of things that we talk about and if we really dig into it, different parts of our plan have different weight to them. 

[00:07:35] Some things we, we really need to get done, there is either strong dependencies or it’s super critical for the business or the customers. And some items are more kind of nice to have or wishful thinking. Um, but we bundle all these things together in a plan. And then it’s, you, you’re almost guaranteed now for the plan to fail because you’ve got all this mix of things. 

[00:07:56] And so I’ve started dividing our planning into a core aspect of what the team is going to actually promise to deliver. And we go a much deeper level of investigation and planning in order to come to that decision. And then there are aspects of the plan that aren’t, you know, aren’t to that level. Um, and we, we have them in there cause we want to try to get them done. 

[00:08:18] They’re important, but they’re not, they don’t rise to that level. And I think that’s really blot brought a lot of clarity. Especially to my management, when they look at the plans and they can really understand, okay, what are you really committing to versus what are you hoping to get done or thinking that you can get done based on some initial costing. 

[00:08:36] And then the final piece is, uh, you know, the discussions on risks and uncertainties, I think is just super critical. Uh, one of the things that I talk about when whenever a new engineer, you know, we hire a lot of engineers right out of, right out of college. And I try to make sure that I spend time drilling this into their brains, that, you know, you have to understand where, you know, when you put together any kind of plan either for your day or for the week for them. 

[00:09:08] You know, where are those risks and uncertainties, because there’s going to be aspects of what you’re trying to accomplish that you’ve done before. And maybe they’re, you know, you can completely estimate them to a high degree of confidence, but there’s going to be items of that work that, you know, you maybe have never done. 

[00:09:27] And, um, you have to do the work either prototyping or investigation or next level of costing to really identify manage and mitigate those items. Um, and if you’re not doing that, then any plan you build is going to be, you know, kind of a toss up. Uh, so I think I, I find myself going back to like really key, important parts, um, from this class. 

[00:09:52] And I think that it’s, it just forms this incredible foundation of knowledge and, and, um, awareness for any types of.  

[00:10:04] Susan Ottmann: Thank you. You’ve picked some of the, my favorite parts of the class, especially reliable, promising. I use that tool a lot. You talk about new engineers, joining your team. What about those who are become new technical project managers? What advice would you give them suggestions or tips for someone who is new at leading projects?  

[00:10:24] Craig Lee: I think that the, the key, the key thing I would say is back to the earlier discussion to just really make sure that you start strong. Um, and I think it comes from a couple of, I think we’ll talk about three different angles on this. 

[00:10:40] One. One is, you know, understand your business and the goals of your management to really make sure that the project aligns strategically with with those, um, the worst case scenario to be in, especially as a new project manager, is to be given a project that either your boss doesn’t think is important or your boss’s boss doesn’t think is important, or isn’t actually clearly aligned with strategy of the organization, because then you’re just, it just I’ve been in that situation. 

[00:11:14] And it just feels like you’re constantly rowing upriver. Um, so really make sure that you, as much as you can pick projects that are aligned. The second is to be very clear upfront what the scope, priority and scope and priority are. And then like we just discussed do the work to really understand the risks and uncertainties. 

[00:11:37] Um, because I think if you can get that clear picture upfront, uh, you can then target your energy because so much about being a good project manager is knowing what to spend your time on. Uh, and I think initially, really focusing and spending as much energy as you can on those items is super important. And the final piece is, is, you know, you’ve, you’ve got to build a great team. 

[00:12:03] Um, cause if you do the first two items amazingly well, but the team can’t execute, then you’re kind of stuck as a project manager. So, you know, and think of it both not only individuals that can deliver the business results you’re looking for, but also on and almost equally, or maybe even more importantly, make sure that those people work the way you want to work, you know, make sure that, that you all enjoy being around them, that they they’ve the culture of, of how they treat people and, and just make sure you, you think about both capability as well as. 

[00:12:39] Uh, you know, how they interact with people and how they work, because the worst thing that can happen with the team as you put this team together, and you’ve got one person that’s either very negative or, you know, not aligned and they can really, it’s amazing how one person can do so much damage to productivity. 

[00:13:00] So I think paying attention to those top three things is the, the key to get going.  

[00:13:06] Susan Ottmann: Well, congratulations on your recent promotion. So now you’re a step above a project manager and are looking at portfolio of projects that your team is managing. Do you have specific methods to deploy your different resources or how are your individuals dedicated to projects? Are they cycled through or are they using, um, subject matter experts? Can you talk just a little bit about that portfolio and how you use your resources in your team?  

[00:13:36] Craig Lee: Sure. Microsoft has been going through this fundamental shift in almost all aspects of their business from, you know, a company that was built on, you know, writing software and shipping it on disks and CDs to, you know, running top rating cloud services, um, and engineers at Microsoft. 

[00:13:57] Now today are, are expected to build, deploy, operate, and support our products. I mean, we are, we are offering these cloud services to companies and, you know, they expect them to work as, you know, 24/7, you know, with a great support and new functionality. Um, and so what we, the model that we’ve kind of moved to is we end up with one or more. 

[00:14:24] Engineering managers that get assigned to a given service. Um, and you know, those engineering managers have a very clear charter and ownership. It’s like they, they are, you know, there’s a very distinct, clear line between different services. Um, and they’re able to then, you know, do that full life cycle with when internally Microsoft is calling dev ups 

[00:14:49] Both development and operations, um, together. Um, from my team, we have, uh, we have a few internal services that manage the, the capabilities inside the company for privacy and regulation compliance of, of private data and personal data. And then we also have some, uh, external products that we’re building. 

[00:15:17] And so the engineering managers end up being assigned to the various services. So I have, uh, three engineering managers that are assigned to one, uh, service and a set of scenarios and products. And then a couple other engineering managers that are focus on different aspects of the new capabilities that we’re building. 

[00:15:41] So they end up being very much, uh, targeted to an external deliverable. Um, as far as charter and, and ownership, um. 

[00:15:58] Then as far as the individual funding, you know, how big should each team be and where should the engineers be, be placed? Um, the, I give an engineering manager has, uh, a set of people, a set of engineers that are really the level of that funding is really geared toward again, the product or service that they’re managing. 

[00:16:23] But, the work on a month to month, or an iterate, a sprint to sprint basis is a combination of work that is both servicing existing products, as well as thinking about new functionality. So, we need to kind of fund both. We need to figure out that, you know, how many people do we need to kind of keep things running and keep high support and, and service. 

[00:16:46] And how many people can be allocated toward new development, um, functionality. So that’s kind of a first level planning that we do. And then as far as the, you know, what new work should we focus on? We, we have established, uh, different pillars of scenarios and functionality that are aligned to different business metrics and business outcomes. 

[00:17:13] And we, we do is, uh, you know, kind of a simple budgeting exercise at the beginning of a planning cycle where we say, okay, how much do we want to spend in each of these pillars? And we do some, you know, based on different criteria, we make those decisions. Uh, and then we start looking at the backlogs of each of these pillars and we do a prioritization exercise to understand, okay, what is the most important investments we should be making in these different strategic pillars.  

[00:17:42 ]And then based on that outcome, we understand, okay, what work streams do we need to fund on an iteration or sprint by sprint basis? And then the, then the final piece is, okay, well, how does it give an engineer get assigned to these different work streams? And one of the, one of the aspects that I, that we, that I talk a lot about with my engineering managers is that. 

[00:18:10] You know, growth and challenging work is super important to, to an engineer’s career, as well as retention and happiness and all these different factors that kind of go into managing people. Um, and so we try to you know, have a, have a real open conversation with the engineers and we try to, you know, the perfect alignment is I talk about the, there’s like three points of a triangle, right? 

[00:18:35] There’s the, there’s the business? What does the business need us to get done? There’s the project or the product, what does the product really need to get done? And then the person, what does the engineer need? What challenges and goals do they have? And when we can find a project that meets all three, then that’s like the best of all worlds. 

[00:18:57] And, and so we try hard to make sure that we are very transparent with the plan to all of the engineers and let them know, you know, this is what’s happening and this is what’s coming and then work with them to see where can we get maximum alignment and make sure that everybody feels like they’re getting a challenging assignment that’s helping them grow and helping them meet their career.  

[00:19:21] Susan Ottmann: I find it so fascinating. Cause sometimes the company would just look at the business outcomes, but if you don’t look at the people and stretching the people and keeping their ideas fresh and their learning fresh, then those people are going to go to a different company and there’s a lot of companies out there. 

So to keep that top talent, you need to have that triangle, not just a flatline.  

[00:19:42] Craig Lee: Exactly. Yeah. 

[00:19:44] Susan Ottmann:  We are seeing more and more of our students companies move from a traditional waterfall management to the agile environment. I know that you’ve managed agile projects for a long time. How have you seen it successfully applied in deliverables realized?  

[00:20:00] Craig Lee: I mean, I guess the, I mean, my, my journey with agile has been, um, from the very beginning, when I, when I was first reading about it and first learning about it. Uh, I very much was under the assumption that, you know, you’ve got to just basically follow the manual. Right. It’s like read the book about scrum or read the book about Patreon programming, or read the book about Kanban and like apply it and everything will be great, right? 

[00:20:32] And early on, I remember going to a seminar, um, in the Seattle area and it was, uh, the guy had written an extreme programming book. I can’t remember his name off hand. Uh, and it was just kind of a, a room and we were just asking him questions. Um, and there was one person there and they were from, I won’t say the company’s name, but a very large security company that everybody knows. 

[00:20:57] And they said we had, we were going to build a new version of our product and we decided to follow your book.  So we followed every letter of this book. And after a year, the project crashed and burned. It was a complete failure. And he talked about various aspects of what went wrong and how it went wrong. And the author looked at him and said, well, you obviously didn’t do everything. 

[00:21:24] You didn’t apply the book correctly, right? So, so there’s this belief, I think in some parts of the agile community that, uh, you know, this will just cure every hill, but the reality is that it won’t. Um, and in, and I think I will even go strong to say, if you do attempt to just blindly apply, you know, one of these approaches, you will probably fail. 

[00:21:52] So instead, what I would recommend is, and I think this is true for every aspect. Um, you know, eventually you’ll go through the process, improvement class, and I, I, you know, think about projects it’s, it’s like in every aspect, I think small incremental improvements toward a goal are really the way to go. So what are some things that you can do very practically? 

[00:22:14] I think, um, instead, and then it’s almost like a broken record, but I mean, be very clear when you go in about the scope and priority of what you’re trying to accomplish, because I think again, that’s sets everything up for success. And I think it’s the next thing is really think about how do you move to incrementally faster cycles. 

[00:22:36] So maybe right now, your, you know, your projects are six months long or whatever – you know, some classic waterfall, you know, the long projects – convinced your management to drop that in half. And, and, and it’s not just dropping in half, but then say, well, the benefit is we’ll get to be able to check in with the customer now. 

[00:22:58] Half and half the time and see what they think and make sure that we don’t get all the way to the end and have it be wrong. So again, just think about moving to faster cycles. Um, and again, the big, and I think the reason why we want to move to faster cycles is the, the key insight I think, across all of agile is just the realization that, you know, no one can accurately predict that they’re true. 

[00:23:26] You know, unless you’re working on a project that is exactly the same that you’ve done 10 times and you know exactly what’s going to come and you’ve dealt with all of the potential things that could go wrong in the past. Um, if you’re doing anything that is all new or in, uh, even some projects, uh, that could be in a new geography or a new place with different conditions, um, you just really can’t predict the future out more than, you know, weeks or months. 

[00:23:57] So, uh, make sure you’re moving in these faster cycles and, uh, try to make, as you go forward constant, but small adjustments to your trajectory. And I think if you, if you adopt those two aspects of agile, you can layer on any type of methodology, um, you know, if you like common boards or if you like sprint boards, if you like story points, or if you like some other aspect of agile, I think you can kind of use whatever you, you feel like your organization can tolerate. 

[00:24:33] And, you know, can embrace, but as long as you’re reducing, moving to faster cycle times and you’re constantly checking in with the customer and making small adjustments, I think you’ll, you’ll get the most of the benefit of the agile methodology.  

[00:24:58] Susan Ottmann: One term we’ve started used using in our organization at the university is learn our way forward. And I think as you transition from sort of the geometric style of waterfall, project management and move to an agile world, you need to learn. So it can’t be A then B, a completely separate process. It’s not like I’m implementing a new production line in a new facility. You need to learn how to do it and adjust. So that’s some great insights. Before we finish, is there anything else that we’ve missed that you’d like to add for those who are listening to this podcast?  

[00:25:31] Craig Lee: Sure. I am not sure why, but I think way too much about the McGregor article that we read in EPD 710. And if you’ve forgotten about it, or if you kind of breezed over it, I would highly recommend you go back and reread it again. 

[00:25:47] Um, I just really, really, I think it, I’m not sure if it’s a hyperbole or whether it’s actually true, uh, you know, accurate reflection of reality, but regardless, I really love the point of view in that article that, um, and maybe just a quick recap for those who don’t remember it, it’s, there’s a, there’s a man who is the leader of a 

[00:26:09] Of a plant and the interviewer is shocked because he’s basically not involved in the decision-making at the detailed level. He’s just spending his time thinking and planning about the future. And he talks about how all of the, his reports are completely self, uh, self-empowered and, and, and he actively chooses not to make any decisions for his subordinates, you know, and kind of forces them to learn and grow. 

[00:26:39] And I, and I think that, and I think that approach, uh, I’m trying it in small pieces and I’m just starting to see a lot of benefit. Uh, and I think it boils down to a couple of key things. One is, you know, really think deeply about your role in the organization. Um, and in any, in any role either, you know, you’re a project manager or a manager or a manager manager, or whatever your, you know, your, where you’re at now and where you’re going. In those roles, you’ll find that there are, you know, distinct problems that you’re able to solve. 

[00:27:20] Um, either through your, your ability or your position in the organization. Um, and especially when you, when you make the first transition to manager, uh, the number one thing that trips people up as they, as they keep doing the job they were doing, but they start managing people. And then that just leads to bad micromanagement and lack of empowerment and people be getting upset. 

[00:27:45] But, and so instead think about, you know, now you’re a manager or now you’re a project manager. What problems can you distinctly tackle? Um, and then I think that the correlated to that is actively avoid solving the problems that others have been hired or are equipped to solve. Um, I think that that really gives you 

[00:28:17] as a, as a person in a new role or even an existing role, I think that gives you the space to be challenged and to grow because you’re not dipping your hands down into the details of what you used to do, but you’re trying to kind of reach up to the new things that you could be doing. And I think it also empowers people, um, at their level to, to really be challenged and grow. Um, so I think I, that, I don’t know why I just find myself going back and reading that probably every few months, uh, and getting something new out of it. 

[00:28:55] But I just think that that frame is like super important. Um, especially as you go through your career and you kind of, uh, grow into different roles, um, finding that place where you can add unique value is super important. 

[00:29:14] Susan Ottmann: Well, Craig, this has been such an interesting session and we want to thank you so much for your time. 

[00:29:19] Justin Kyle Bush: Thank you for listening to Epod. For more episodes, visit www.interpro.wisc.edu/podcasts. And if you enjoyed this, don’t forget to subscribe, rate, review, and share. 

On this episode, Justin Kyle Bush talks to Theresa Pelkey, the Manufacturing and Project Engineering Director at Kite Hill. Theresa received a bachelor’s in Chemical Engineering and a master’s in Manufacturing Systems Engineering from UW-Madison. She discusses her personal and professional development and the challenges of working, studying and being a parent. She also talks about her experience in the MSE program and gives advice for those who are thinking about continuing their education—whether through a degree or professional development.  

Theresa Pelkey is a Manufacturing and Project Engineering Director at Kite Hill where she is responsible for environmental health and safety program, maintenance reliability, capacity planning, and capital investment for two manufacturing locations. She has had 17 years of production and project management experience in both processing and packaging in food manufacturing. She has operations experience in leading, motivating, and coaching teams to meet production, safety, food safety, quality, environmental, efficiency, maintenance, procedural, and continuous improvement goals as well as proven success in managing large build construction projects. Theresa is a successful leader in the management of capital investment and capacity planning as well as development and implementation of maintenance reliability and EHS strategy. She received a Chemical Engineering undergraduate and a Manufacturing Systems Engineering master’s degree from UW-Madison. 

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Justin Kyle Bush  0:00   

Welcome to Epod, a podcast from the UW Madison’s College of Engineering’s Interdisciplinary Professional Programs. These podcasts are focused on big ideas and engineering and the people behind them. My name is Justin Kyle Busch, and I will be your host. On today’s episode, I talked to Theresa Pelkey, who is the manufacturing and project engineering director at Kite Hill. Teresa received a bachelor’s in chemical engineering, and a master’s in manufacturing systems engineering from UW Madison. On this podcast, she discusses her personal and professional development, the challenges of working, studying and being a parent. She also talks about her experience in the MSE program, and gives advice for those who are thinking of continuing their education, whether through a degree or professional development.   

Justin Kyle Bush 1:01 

Theresa, I’m excited to have you with us today. Thank you. 

Theresa Pelkey  1:05   

Thank you so much for having me.  

Justin Kyle Bush  1:08   

Let’s get started. So tell us a little about yourself and how you got into manufacturing. 

Theresa Pelkey  1:15   

Yeah, so I, in high school, actually, I was, I was in chemistry class. And our teacher had brought in an alumni student that who was an engineer, she came to talk about what engineering was, and I, I, anyways, we did this little exercise where we like solve problems in teams and groups. And I loved it. And she said, This is what engineers do, they solve the world’s problems, and they solve them in teams. And I was like, that sounds great. Like, that sounds easy. And so I really liked chemistry. And I really liked math at the time, and I did not want to be a teacher. As much as I respect teachers. I knew I didn’t want to be a teacher. So I said, Okay, I want to be a chemical engineer. And I knew nothing more than that, besides, I liked math and science.  

And so I went into engineering and, you know, I went to UW Madison, for my undergrad, and they do a really great job of, you know, promoting doing co-ops and interns in the career fair, and just, you know, getting experience and what you’re doing before you actually go to work. And so I, you know, like a good student I listened to my advisor, you know, they invite advisors that I went to the career fair, you know, put my resume out there, and I got it, you know, a job offer with a food manufacturing company, large food manufacturing company, and I took that job, and it was, it was amazing. Um, I in that experience, I got to work, you know, the packaging line and tanks, and I got to, and I just fell in love with watching stuff, like how it was made.   

I mean, I literally, I remember, I was so fascinated with a box paper, like, the way that the roller like flipped the tape and the boxes and folded and a case of Rector and how every little intricate part was totalized and sensors, you know, work to so then have a finished product. And then you know, palletizing machine after in a stretch wrapper, and I was hooked. At that moment, I was like, I loved it, I would stare at the machines and just watch every little thing. And then when there was a failure, like figuring it out, and, and I got to install a tank while I was on that project. And it was such a great experience. And then what really stuck home with me is you know, and I’d worked in, in places where I had been a team lead or things like that, and I got to oversee or supervise teams, you know, in those roles that I did. And it was amazing to the camaraderie to be on a team to stick through, you know, the ups and downs of manufacturing when something fails. And when something when you’re trying to accomplish a goal and, you know, you make some new production improvement or something, some, some change that makes your operator, your team member, their life better, and they’re just so thankful. And you know, they just a lot of the teams that you know, they’re just working hard to bring money home to their families, you know, so to be a part of that. I just like I fell in love.  

And so I knew I wanted to stay a manufacturing I was like, well, that’s great because somebody will you know, it’s okay, if you do an internship, and you don’t like it, you know, I knew some of my friends that were like, I do not like engineering like this does not work for me. And I guess I got lucky, you know, in the sense that I really enjoyed manufacturing and, you know, when you do engineering, you can go into so many different spaces and so I just, I was, I ended up specializing in food manufacturing, which always made me feel good because some other industries can be a little dangerous. And I was like, I don’t want to die every day when I go to work now mind you, I could die. In my work environment too, there are some dangerous things, you know, it’s like we have, you know, equipment that can kill you and things like that too. But, um, you know, we don’t make as much money in manufacturing because it’s low margins, but it’s rather than a tech world or something like that, but at least everybody eats, you know, so it no matter what, it feels a little stable in that sense. So, I’ve just done, you know, kind of gravitated that way and stuck in the food world, it, um, I love how we nourish the world, you know, with food. And also, you know, how important it is to be employees are safe, and the food is safe, and everything that comes with that.  

So that’s what has kept me got me into manufacturing, and then kept me into manufacturing all these years. 

Justin Kyle Bush  6:01   

Not to go off topic, but I know where you’re talking about food. So, what is your favorite food? 

Theresa Pelkey  6:08   

Um, I don’t even know if I have a favorite food. Um, you know, over the year. I have a favorite, I have favorite foods, they’re not very good for me. So, I don’t get to eat them as often. But I think I really love, um, this is this is really funny, this is a little bit like it’s into my world. Favorite fruit is probably pineapple. My favorite food, I always have to say yes to is crème brulee, if it’s on the menu, I have to have it. And I always have to say yes, to addition, it has caper. 

Justin Kyle Bush  6:45   

I really like capers and interesting facts about myself. I, I like food, but like my go to thing is Good & Plenty like, everyone dislikes Good & Plenty. Anytime I go to a store, I will push like 25 boxes into my cart. I love Good & Plenty. So that would be my background is working, manufacturing and Good & Plenty. 

Theresa Pelkey  7:08   

Yeah, there you go, I’m gonna get you know, the one the things that stick with you that you always are, you know, especially if we know how they are made, it makes it even better. 

Justin Kyle Bush  7:20   

It’s great to hear how you got started in manufacturing. So your career is pretty impressive. What helped you succeed when you move from a large multinational to a startup? 

Theresa Pelkey  7:32   

Yeah, um, you know, when I worked at the big company, it was, you know, it’s a great experience; big companies have lots of money, they can give you experience in all sorts of things. And I learned a lot, a lot about process and a lot about analyses and risk analysis and structure and which is great, you really do need some of that, you know, in your background, and, but I really, you know, I wanted to make a difference.  

Flickr or, you know, big companies take a little while to make things happen. And you have ideas, and you have a corporate structure and lots of, you know, lots of decisions and approval processes and things like that. And I just, I wanted to have a say, like, quickly, you know, at the local level. And so I got this opportunity to work at my current, you know, my current company, and I, you know, my current company, Katie Hill, and I, it was a startup, and I had never worked in a startup, my sister had actually worked in startups before, in, she’s in, like, genetics and microbiology, and she had worked in a bunch of startups in the San Francisco Bay Area. And, you know, I did the pros and cons, you make a lot less money. I’ll tell you that much. Right, like, so you have to take and, and you have to you, you work a lot of hours, I would say in the sense that there’s a passion behind what you’re doing, because there’s not a lot of you on those teams. And so, you know, everybody has to step up and do a lot of things they’re not used to doing on the startup and, but there’s, there’s, you know, this whole energy in the space that really helps you start like, you know, going through the ups and downs of startups.  

So when I moved from the big company, to my small company, I remember my boss at the time these moves so quickly, so fast, changes happen. They innovate so fast, new products are launching, like very fast. I remember we had gone to a you know a customer and said we’re going to make this product and we came with like four different you know, I think it was like six different flavors of this. This product we wanted to do and they, they said we want it, we love it. But stores, you know, have resets every certain amount of timeframe, so they only reset their shelves every certain amount of, you know, a certain time. And so you need to either be ready to launch on their store reset, or you miss that whole round. And so, you know, we go into these buyer meetings and they taste your product, and they say, yes, okay, we’re ready. And then you have to be ready to go with that product. And, and my boss comes back, and he’s like, they’re taking it, they want all of it, but we have to install it in the next three months, it has to be running and installed in three months. And I’m like, what, I had never done a project that fast. I mean, everything at my previous company, it had been, you know, could take years to do a project or more, you know, we work through every design. You know, multiple iterations, every risk analysis, talk to every safety, you know, safety manager, quality manager, every review made, its reliability, all that stuff.  

And I had to really get comfortable with, not perfect, right? Like, my manager kept telling me, You’re too corporate, you’re too corporate. And I was like, what, like this, I just tried to make sure we don’t, you know, this is perfect, and he’s like, you can’t be perfect. Your inaction is too slow, like inaction is worse than just putting something out there. And, and, by the time you get it, perfect, we’re going to already change it. Like, it’s going to be a new process, a new end, you know, at my time, so far, I’ve been there five years, we started with, for example, 400 cases a day. And we are now doing of our product, right, we make cheese products, and now we’re doing 6200 cases a day. And that’s only in five years. Time, that grows.  

Justin Kyle Bush  11:52 

Of just cheese products?  

Theresa Pelkey  11:54 

Yeah, yeah. And, um, and, we, we had, and there’s a lot of things, you know, you have to be comfortable with, in a start, you have to be okay, with your stuff that you worked on getting tore out. Like the next, you know, like, I spent an entire summer, um, you know, launching a new product line. And then six months later, they tore it all out, because it wasn’t successful, it wasn’t working. But one of our things that we pride ourselves on as a startup is to be first in the market, because those big companies, they’re going to come with all their money, and come with all their, you know, all their stuff later. And they’re going to make something similar to what you’re making. But, you know, you want to get your tried-and-true customers at the beginning right away, and hopefully, they stay with you. And then as you grow, you can like, bring your price point down.  

But you know, we had to, I had to be okay, with, yeah, just moving forward with something, even though it wasn’t perfect, you know, like using Excel instead of these big systems and these different software programs. And, and one of the things you also have to do at a startup is, um, you know, I collaborated a lot. So, I would use everybody else’s ideas. So, everybody’s new at a startup. So right, so even though you have 20 years of experience, that product, you’re making that company is new, and still…  

Justin Kyle Bush  13:20   

Don’t they make you and startups work very independent, at the same time too, where you sometimes are working on a project by yourself, not always knowing everything, and you’re bringing other people and, correct me if I’m wrong, that’s my interpretation.  

Theresa Pelkey  13:35   

Oh, yeah, because there’s not enough of you, right, so you don’t have a you don’t have a safety. It depends on who you are. But you, let’s say you, you know, don’t have this, one particular thing you use at a big company, could be some analyses that you can quickly get a report out, but maybe you don’t even have any of those systems. So you have to then, devise something of form or a process, to just start to collect the data, you need to be able to do what you want to do. Or if you need, you need to do something, you’re the one to do it. Like so, because there’s not a lot of you on the team, right? There’s not this big company with like, all these layers. So you know, if it’s something you’re passionate about, or something that you, you really have to prioritize the things you work on and the passion you have. And you just take little bites, you know. 

I have, everybody that joins our company, it’s an overwhelm of things to fix, right? Because they come from a big company that has all the processes in place. But what’s really cool about the mentality you have is that you want you have to want to improve, make change. And that comes in a startup. It’s just because that’s innately what we’re out to do is like to be a disruptor in the market or to create something new that no one’s ever made. And so those are the types of people you want to hire the people that are not okay with the status quo. That’s also something you had to fix. Design is like our hiring. And really like fine tuning the type of people you bring into your organization need to be wanting to like, it shouldn’t, like, change the status quo. And not everybody fits in that space. And you have to be okay to say goodbye to people who, like, people come and go in startups a lot, right? Because as you grow, and your different levels of the organization, you need different skill sets.  

And it’s okay, if some people get in, come in, and some people leave, and then they leave, they put their mark on the place, and you take you take their best practices and keep moving forward. So, I think it’s just being adaptable and nimble and, and really collaborating with the team. And really, the biggest thing that has helped us, me succeed, is knowing how valuable people are. Because you really, you really have to rely on people and you got to create really good teamwork, because you’re fighting through a lot of like, failures in our space, right? There’s like the ups and downs, and the failures and things that go well, and the upsets and, and you need to have your team there to go through that with you. 

Justin Kyle Bush  16:18   

So, talking about you moving from a large multinational company, to a startup, how would you describe and talk about your personal and professional development? 

Theresa Pelkey  16:34   

I think I’ve always tried to stay relevant over the years of my career, you know, I had some great, I would take mentors are important. Finding mentors, and its sort of naturally happens, it’s not like, you can just go up to somebody and say, can you be my mentor, you know, maybe you can, I mean, if it’s, it’s a good a good colleague, but sometimes they just naturally happen, you don’t realize they’re happening, but there’s somebody that’s been important in your career, or a coworker that you always, you know, bounce ideas off of, and they end up being your mentor. And staying in touch with them, you know, is super important, because you talk through things and you get networks, right? So, say, hey, I need some help with this one thing you can you tell me somebody in industry that’s good for this, or a place where I can look for this kind of information. So that’s one thing I do is like, really stay in touch with mentors.  

 I also, you know, I continued education. And so, I’ve done some, I’ve done some personal development in, you know, an organization called Landmark Worldwide, I’ve also spent a lot of time with an organization called Project Management Institute. And I guess, in both of those spaces, it’s just, it is conversations, and with other professionals that are out to do more for themselves, right? So they’re out to, and when you when you surround yourself with people that are also always trying to, like grow in some way, it helps you continue to grow rather than, you know, sitting, I guess, scroll through our phones or whatever, like, in the evenings, you know. I feel, which we do, right, like, it’s a great to, you know, relax in some ways, but I think it’s been important for me to feel relevant, you know, to keep my mind active, has really been important for me.  

And, you know, I’ve also tried to be healthy, so is it really aren’t the greatest at it, but you know, exercising and, you know, doing what we need to do spiritually exploring and things like that are, you know, important for me.  

And then I also volunteer, I think that, as an engineer, we have one side of our brain that does a lot. And I tried to keep an artistic side of my brain. So, I play, I play an instrument, I play French horn, and I play in community of bands and orchestras, as a volunteer as we give back to the community through music. And so that helps me keep my artistic side of my brain going so that it’s not always just one side. So, I feel like, that helps me take on the world of work sometimes, is that other release of something. I know some people you know, hide or get into nature and, and do different things like that. I mean, it sounds so like everybody says, this is what you need to do. But when you actually buckle down and do it, it can make such a huge difference to sit to focus on some of that other side of your mental health, I guess. 

Justin Kyle Bush  19:41   

Well, I will share I don’t play an instrument but I felt like if I wanted to play an instrument, I would want to be the drums. So, like even when I go to, like I watch college bands, like watching the people that play the drums, I feel like that would be the best thing. Like they just seem to get into it. So, into but now to like, go on your little horn thing, but I always feel like people try to pick a French horn, or just a horn in general. And I, from what I’ve heard, they’re not the easiest to play. So, I don’t know, like people always go, oh, I’m going to pick that. I would pick the drums, but it’s probably not easy. 

Theresa Pelkey  20:18   

Yeah, you know, it’s kind of funny, I ended up with French horn actually. I played, you know, I played all my life since I was in fourth grade. So, I, I got it, because all the other instruments, I didn’t have a lot of money growing up, we didn’t have money. And so, we, every kid that had money, they bought the instrument they wanted and that’s the one they got. And so, then we actually could rent a French horn from my school for $25 a month. And so that’s how I ended up one French for not in any other reason. Um, and, you know, I, you know, I fell in love with it, you know, over the years, but you know, drums, we have drums in my house. So, I have lots of musical instruments at our house. So, we have all sorts of drums, we got a keyboard, we got xylophone, you know, and you can buy this stuff on, you know, Facebook marketplace, or on Craigslist, or whatever you use to purchase like, you know, aftermarket stuff. You can buy this stuff for like 20 bucks and have some fun in your house to like, let off some steam we play. We just have fun every night. You know what I’m cooking dinner, you know, my husband plays and stuff. So, you know, with our son and so you should get a little drunk to have it in the house. 

Justin Kyle Bush  21:27   

That’s when I should, I should get drums. I mean, I always play my little violin because I want people to feel sorry for me. So, you decided to go for a master’s here at UW Madison, what challenges did you have working and studying? Like that you can share with our listeners? Like what were your challenges? And what can you help people who may be dealing with the same things? 

Theresa Pelkey  21:54   

Oh, geez, yeah, I, I did have ups and downs throughout the program, I think in the sense of like, Did I get it right? Did I get it perfect? Was I that stellar students that I had this vision that I was going to be? Like, not always, you know, I’ll be honest, and it’s okay. Because, you know, I think that you work into your schedule, what works for you.  

And one of the things I found that was really important was upfront, having the support of your company, is super important. So, I think I did that the intro, the intro to the program. UW Madison does a great job with getting you prepared to be an online student. There’s like this free class that you take.  

Justin Kyle Bush  22:47 

Online, yeah, the online student. 

Theresa Pelkey  22:49 

And I was like, oh my God, thank God, I took that because I would have never done the things that I did without taking that class because it tells you all the things you should do to prep, to be a student, and to be a good student as an online student. So, or to even just take on going back to school, when you’re working full time. And so, you know, I, I talked with my manager. Now, we’re a start up, so they didn’t pay for my degree, that’s for sure. But, you know, they said that they supported it. And that’s important because you do need to what’s so great about the program is that you actually do, you use your own work data in a lot of the classes, or you apply what you’re learning to work, or your assignment is go do this at work and come back and like write a paper or something, right? So you have to have support, you can’t fake it, you know. 

And then, I was on a different time zone, right, I was in California and so at the time when I started. So, some of my classes were while I was still at work, so I had to feel okay, with taking the time away from work. And so, you know, leaving work to go to a class and feeling confident about doing that or feeling confident about interviewing a coworker about something related, that’s all important. So, your company support is very important.  

And then also, you know, your support of your family is really important. So, before you apply, you want to have the conversation with your significant other, your partner your family. Because there were holidays that I was doing homework, right, like up until, you know, I would come home, I fly home to be with my family and I would be writing a paper that was due right before Thanksgiving dinner. Or you know, I was you know, quick knocking out something that needed to be done or doing some programming thing or something right that I was trying to get done before. And so, you know, my family knew and they knew this is like okay, this is what I’m committed to, and my husband. 

I spent a lot of weekends. So, I was a marathoner weekender, to be honest, and that’s what worked for me, because I, my brain was just right at the end of the workday, and I knew that I couldn’t do work in the evenings, I, because I would work so hard, you know, I work so hard at work, and I just, I couldn’t get, you know, I fall asleep or something while reading. And so, I didn’t do a lot of homework in the during the week, and I would spend most of Saturdays and Sundays, and my husband would have to take care of our son during that time. And so, it, he knew that this is what we were committed to as a couple. It was important and so like having that conversation upfront.  

So if I, you know, if I apply, and I get accepted, all right, you know, can you support me during that, that was something I’m very glad that I did, as you know, as I prep for this, and, you know, I would say, other challenges I had, were being a startup, we didn’t have the data, I didn’t get to always apply the things I wanted to at work, it doesn’t mean, it was bad, I learned a lot, I can still apply them. But, you know, we ended up using a lot of other students’ data, ones that had easy to pull software systems that we, we could pull, because as a startup, we didn’t have, let’s say, I wanted to track downtime on something, I would need to go and create a form and then go collect the data. And then you know, versus somebody else has, like, you know, 15 years of that data in their system or something and ready because they had more robust, so a lot of times we ended up as we did group projects, we used other students’ projects. So, you know, that made me feel a little sad sometimes. But I, you know, I still have that with me now that I can like take that forward into my organization.   

And another thing that would it is important is to have a laptop, I would say, or something, because I actually traveled with it. And I had a, you know, my husband would drive when we would go to like family or family outings or things like that, and I would look into my little world, I will be doing homework in the car, as we were driving typing or something with mobile hotspot. And so, you know, that was how I would get it done, as we were doing family activities, or cell phones, something like that, you know, I’d be able to take my laptop with me to quick get it done, or, and then at work, I um, you know, we didn’t have a lot of conference rooms or things like that. And we were in an open office setting. And so, I sometimes would take my classes out in my car in the parking lot on my laptop. And so those are some things that I adapted to, to be able to make it all work, I guess with life in general. 

Justin Kyle Bush 27:52   

Wow. So, I can’t believe that you worked, studied, had a small child, and you’re back in Wisconsin, but you moved from California, so moved across the country? How did you find time to do it all? And what tools did you use to prioritize? I know that you kind of talked about it. But if you wanted to expand a little bit more, it’s totally up to you. Free Range?  

Theresa Pelkey  28:17   

Yeah, I think well, some of the things I had to do is block my calendar at work. Um, so I had classes, sometimes lectures, the cool thing about the program is they actually have real lectures like in person, it’s not just all recordings, I mean, you can watch the recording if you want, like if you miss it. But if you want to interact and ask questions, and be part of an actual class, they’re all like that. And I think other programs in the world like country aren’t like that, necessarily. And so, um, but my classes were sometimes during my work because of the different time zone. And so I definitely had to block my calendar at work. I also, um, I did limit some of my extracurriculars during that time, I think I just, it was important for me to say like, this is what I’m focused on at this time, you know, like I saw, I did take a break from playing my instrument in the big groups, and I did take a break test on my other volunteer activities or whatever. Because I said, okay, this is my focus now, at this time, because you just, you know, we don’t have enough for our own mental health to be doing so much stuff like, go, go, go.  

And then, um, you know, I also was one of the things that was cool as I communicated. I stayed in communication with my advisor during, well, during life changes. I know that some of our students had, like my co students, my peers, they had, you know, significant life changes, you know, like new jobs moving across the country, and maybe we don’t talk about that stuff in our lives as often as we should. And, you know, even with our managers, right, tell them what’s going on and you know, you can adjust your workload or do different things.  

And so, when I, when I was pregnant, you know, I actually told Susan, before I accepted the program, that I wanted to have a baby. And I was like, I don’t think I could do this, like, how am I supposed to have a baby at the same time as getting my, you know, my master’s degree? And she’s like, oh, I think she had probably, I think multiple children when she was getting her Master’s, they should she mentioned to me, so she, I was like, okay, I can do it. So, when it actually finally, you know, happened, you know, we worked together to create the program and rearrange, like, which classes I took when I heard them. That made sense. So, some classes do have a little bit more of a workflow, you know, and some require more student meetings, like, you know, you have a group in your meeting, like, you have the lectures, and then you also have to meet with your group multiple times a week, and then other classes are a little bit less intensive.  

And so, you know, we worked to create the program that worked for me, so when I was pregnant, when I was on maternity leave, and things like that. So, what would work well, based on workload, and so I think it’s important to have those just to be open and have those conversations about, as well as when things were happening in my life, that you can have a conversation with your professor, like, if something’s something happened this week, like, the cool thing about this program is that we all are working full time jobs. And things happen, like the work has some crazy, you’re your work has some crazy thing that happens, and you need to work, you know, a bunch of extra hours. And, um, you know, professors were so understanding if you just be in communication, rather than say, well, I’m just going to fail this week, you know, or something like that, like some of them would grant extensions and things like that if you had a conversation about what was going on your life. So, I think that’s also one of the things I would recommend to anybody doing something like this, such is to be in communication about what’s going on in your life. 

Justin Kyle Bush  32:04 

Yeah. Well, you kind of shared a little bit about this, but if you want to expand even more, what did you like best about UW Madison’s manufacturing system experience? So, I know you talked a little bit about like the work life balance, but was there anything that you could have used from the program into your career? What helped you advance more in your career? 

Theresa Pelkey  32:27 

Yeah, I think. I was just blown away by it how long it was to learn relevant, courage, industry stuff. You know, I, I had been working for a lot of years and so you just get into the grind and I don’t know, it doesn’t feel like you and your brain is. I just wonder what’s out there, and so for me, being exposed to a lot of other students that were doing cutting edge things and we are dealing with the pandemic at the time and like, how are they handling it? We talk about that in class. That would be one of our discussion topics would be how, how are you dealing with the pandemic and as an or how a supply chain affected your industry or things like that. And that’s so great because I could bring that back and have conversations with my manager about, you know, we should do something like this or we should, you know, things like that. And or it would have me explore other technology solutions and reach out to different companies based on what I was learning from other students in the past.  

For me, that was awesome. Just being able to apply like current technology to my work was, was really valuable and I think that the professors and teachers are guest teachers in the programs are so passionate. I don’t know if because I’m at a different age or what, maybe, maybe the teachers were always passionate undergrad program. I’m not sure and I wasn’t paying attention. But I think, you know, there’s like a little bit of a sense of a peer type environment like our professors were learning from us because we’re in industry and then we were learning from the other students. So, it’s just this fun, collaborative environment. And then there was so much passion from the professors on how to deal with being relevant or how can we use the experience from the students to incorporate into our classrooms? And you know, there was always a survey and the professors would take with the feedback they were getting and incorporated into the next half of the semester.  

And so, it felt like you were really designing your experience, as you know, because they would take that, you know, and they were so passionate. It’s like it’s hard not to get passionate about the topic when the teachers are so passionate. You know, it’s like that made it just go by really, really fast and made you love learning about that new technology because they were so interested in what they were teaching. So, to me, that was a really great thing that UW Madison has to offer, and I didn’t take a program anywhere else. So, I don’t know what other experiences might be. But like, I was really impressed with that, that dialog and that interaction that we got to have with the professors and the students.

Justin Kyle Bush  35:46  

I mean, it shows being a double badger of someone who got their undergrad at UW Madison and then came back to them for their masters, so it seems like they’re doing something right for you to be a double badger and do undergrad and grad. So, for individuals thinking about advancing their knowledge, whether in a degree or professional development courses, what advice can you give them? 

Theresa Pelkey  36:10  

Yeah. You know, getting my degree pushed me to new levels professionally much quicker, I think, than I would have thought there naturally. Now you can always get there naturally as you have your bosses and you work through your development. But you know, the way that we had to present our, our assignments and the way that we had to like gather information and data and just it refined me in ways that I was surprised by. I didn’t really, and I was naturally incorporate, incorporating them into how I presented information to the leadership team at my company or things like that. So, things I had gotten from the classes I started to do, you know, like, how do I? How do I write? Professor, how do I do my presentation so that it’s in a format that is what we did a lot? We may do a lot of presentations in and in our classes, and to report your results or your data or your things like that.  

And so, I, I started doing that at work and I had people at work that were coming up to me to say they noticed a difference. And so much so that on multiple occasions my CEO would message me after something and say how much he had seen my development improving and changing. Still, I don’t know, it was like, it was like a sneak attack, seeing what was happening in the background. I didn’t realize that it was happening, but that, that development is something that I maybe would have not got to naturally. You know, it was like it was expected from the teachers for you to do well in that space. And it was like, you know what they expected on how we turn papers in or turn to turn assignments in. And so that refinement and that’s yeah, it just it, it transferred to my work and it was something that people noticed.  

And so, getting my master’s degree and then some of my other sort of certifications along the way, like I had said earlier, it made me feel relevant and keep my knowledge up. But you know, it really gives me the confidence to ask for what I know I deserve. And, you know, I asked to move across the country and keep my current, you know, stay at my current company. They had never done that type of thing. You know, who would have thought that I could be an engineer and a facility that does installations of equipment and, well, some big companies to do that, I guess. But I never thought, our company didn’t think we could do that right.  

And so, I had to ask to do that, and I felt confident that I am worth it. I’m worth it for them to retain me because I have these skill sets that I’ve been in these programs and, and then after I got my degree and shortly after I actually asked them to create a directory role for me. And they did. And I think that, you know, having the skill set that you get in these programs and continuing your education just really gives you the confidence to say, like, I am worth it and I deserve this. I deserve this promotion. I deserve this new title. I deserve this new wage. I deserve for you to respect me in these meetings where I deserve to be at the table in these meetings, however, it comes up that looks like it, it can come out of doing these programs and that competence, and some people have confidence, but I would say I’m a confident person, but there is like a background comfort that comes that’s not like you can outwardly be confident, like, like a piece inside of that confidence, I felt like came out of doing this program. 

Justin Kyle Bush  40:20  

I feel like the competence and then sometimes always having the knowledge to back it up because you can just be confident and just like, talk about things. But like if you don’t have the knowledge or like, I don’t say smart, but really, I guess I would say knowledge just because like, you can back it up like everything you have your undergrad, you have your masters. All of this. Like you deserve what you deserve, like everyone deserves more. You should be able to come to the table and ask for be sitting at the table. I don’t think sometimes we have enough people at the table that should be at the table with different skill sets, backgrounds, diverse individuals, like that is who should be at the table, not the same people. And it’s interesting for you to say that and then to hear that because it makes me think, like, always asking for more, because I know that some people don’t always ask for more or are afraid to, and for you to have the confidence to ask for more is just, it speaks a lot about you and everything that you learned. And your professional development of what we’re giving our students to, to go out there and not be afraid to ask for what they deserve. 

Theresa Pelkey  41:27  

So absolutely, I couldn’t agree more and know it’s so important for all the voices and diverse perspectives to be out there in their company is making the difference that they need that they can make, and a lot of people are lost in the mix. And so, I think that, I even saw in all of my peers, I saw their confidence come forward in that and that as the semesters went on and I got to see the different things that were happening and different people get different jobs and things like that, I think it definitely came out of them before that, for sure. 

Justin Kyle Bush  42:10  

Well, it was really great to talk to you, and I learned a lot about you that I didn’t already know, and I’m sure all of our guests listening are learning a lot about you. Is there anything else you would like to tell our guests listening and people who possibly can read it? Also, when we put it up for the version, for people that can read it also? 

Theresa Pelkey  42:31  

Yeah, I think I would. I, I would love to just say the little I’m a little bit biased, I guess, because I have my degree in my undergrad at Madison. But you know, there’s something really awesome about degrees from Madison. I am so proud of a degree from Madison because I just feel so prepared. You know, when I went out into as an undergrad, out into the world, the world, when I went out to go where I did, I just, you know, I hit the ground running. You know, I felt so prepared and ready to go. And I had always been confident and saying, look, I got my degree from Madison. And so, when I saw the program come through, as an alumni student, into my email to get a master’s degree at Madison, I, I was like okay sure. It did not disappoint. The same way it was on my program, I so, so I see… I feel so fortunate to have had the opportunity to be a part of the program because again, I just felt like they just do such a great job of making sure that what you’re learning and what you’re doing can actually be taken out into the world. That you are going to be able to be successful, make a difference at your company, and do the things you need to do. Like every class was all about that, you know?  

And so, you know, the and then the caliber of students that are that you’re with is really top notch. I mean, the conversations are, yeah, the people are very smart, you know, in the program.  And they push you and they challenge you and you have debates with your students, your college students, your peers, right, and nobody, and there’s like a collaboration that happened in the program. And, you know, I don’t know, that’s just, it always felt like that as an undergrad and it felt like that again. And I don’t, you know, kudos to the admissions group that’s doing what they do because there is something there to be said about like healthy debate that you get in the program and I think that Madison gives that.  

And so, I was just super happy with my experience as in this program, and I couldn’t recommend it more to anybody that is thinking about doing it. They do such a great job with the platform, the lectures, the, the learning set up, and accessibility and recordings, and just everything that exists inside of the, the online program and the way that it’s designed. I was very impressed with. And I felt like I was doing it. It wasn’t an online program, then I felt like it was just like I was on campus, even though it was all own.  

And so, to be able to get that experience, even though I was in California when I first started or you live in a small town somewhere and you can’t drive to Madison or something like that is super great. And just for anybody that does it, three years goes by really fast. You know, you think like, I don’t think I could do this or I’m working too much or, you know, I don’t have time for this or whatever. You’ll be so happy and proud that you invested and did that for your time, and it goes by very fast and before you know it, you’re done and you have that degree. And then you move on to the next bigger and better things. 

Justin Kyle Bush  46:18 

Yeah. Perfect. Thanks. Thanks for joining me on this podcast. I hope you come back and do another run in the future. 

Theresa Pelkey  46:26 

Thank you so much. And this was it was super fun, and it was great to talk about my experience and thank you for having the opportunity to be a part of this.

Justin Kyle Bush  46:39  

Perfect. Yeah. Thanks. Have a good day. 

Theresa Pelkey  46:43  

You too, thank you!

Justin Kyle Bush  46:45 

Thank you for listening to Epod. For more episodes, visit www.interpro.wisc.edu/podcasts, and if you enjoyed this, don’t forget to subscribe, rate, review, and share.

On this episode, Susan Ottmann talks to Dr. Dan Negrut from the UW-Madison Mechanical Engineering department about his course Computing Concepts for Applications in Engineering, the field of big data, and how simulation and modeling fits into the field of data analytics.

Dr. Dan Negrut is the Mead Witter Foundation Professor in the Mechanical Engineering Department at UW-Madison. His research and teaching are focused on the application of computational science to engineering. Dr. Negrut founded the Wisconsin Applied Computing Center, he leads the Simulation-Based Engineering Lab, and is a NVIDIA CUDA Fellow. Prior to joining UW-Madison, Dr. Negrut worked at Mechanical Dynamics, Inc. and the University of Michigan. He was also a Visiting Scholar at the Mathematics and Computer Science Division of Argonne National Laboratory. Negrut has a Ph.D. in mechanical engineering from the University of Iowa.

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On this episode, Susan Ottmann talks to Tony Orzechowski from Abbott Diagnostics R&D Data Analytics organization about how he got started in data analytics and why it’s important for engineers today. 

Tony Orzechowski is is the director of the Abbott Diagnostics R&D Data Analytics organization and an instructor in the Master of Engineering Data Analytics program at UW-Madison. He has a distinguished career with over 35 years of increasing management responsibility. He is responsible for both ongoing support of product launch and digital innovation with over 100 statisticians, data scientists, business analysts, quality engineers, and data management personnel. He received a mechanical engineering undergraduate and a manufacturing systems engineering master’s degree from UW-Madison. Tony is a certified Six Sigma Black Belt, an adjunct lecturer at Northwestern University, and is launching a new non-credit course Data for Technical Leaders at UW-Madison.

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Justin Kyle Bush  0:03   

Welcome to Epod, a podcast from the UW-Madison’s College of Engineering Office of Interdisciplinary Professional Programs. These podcasts are focused on big ideas in engineering and the people behind them. My name is Justin Kyle Bush, and I’m your host. 

On today’s episode, Susan Ottmann talks to Tony Orzechowski, from Abbott Diagnostics, R&D data analytics organization, about how he got started in data analytics and why it’s important for engineers today. Take it away, Susan.  

Susan Ottmann  0:43   

Welcome, Tony. 

Tony Orzechowski  0:46   

Hey, thank you, Susan. Thanks so much. And first, I just want to thank you for allowing me to share some time with you on this. This is exciting. I love this topic. And I think we’ll have a good time here. So, I think you want to talk a little bit about our history in how I got started with analytics, right?  

Susan Ottmann  1:06 

Yes. Before we start, how about just tell me a little of your story?  

Tony Orzechowski  1:09  

Yeah, absolutely. So, you know, I’m going to take you in a way-back machine here. I’m a UW Madison graduate, mechanical engineering, about 37 or so years back, and in my first year, I was assigned into some Process Automation work that was just incredibly challenging, a wonderful project. But you know, that was a, I think, a key part of my future, because it really, you know, the fact that we had to automate and hold such high levels of quality on processes that traditionally had been very manually oriented. And so therefore, adjustments can be made by the technician to adjust for perhaps, changes in tensile strength of materials, or other incoming raw material variations, environmental conditions. And at that time, you know, I really didn’t have a background in analytics.

Really, in the early 80s, that wasn’t prevalent for engineers, they have that background. And with the challenges of this particular project, I was lucky enough to have a manager who was an incredible mentor for me, throughout my career, who pointed me in the direction and said, “You know, I’ve heard of something called the design of experiments, you know, Tony, take a look at that, because that maybe that could help us in this very challenging project that we have to understand, you know, how we can drive better quality through variance reduction, among others.” That, for me, was a monumental key point in my career. I didn’t realize at that moment, but I look back now. That’s how I got myself into analytics 37 years ago, was going to my first courses on design experiments. You know, under George Fox and Conrad Fund, Swarm Biscard, all from the University of Wisconsin. I was located down in Racine, Wisconsin, at Modine Manufacturing. And that was a point where, as I learned it, things just opened up for me. I began understanding how to use analytics to characterize the factors that actually drove the variation in my outcome responses. It gave me a broader view of all this, it began to teach me about the idea of pattern recognition, signals, and noise.

And all of that really was what allowed me to be successful in that automation project that we had going and really opened the door to me becoming a master black belt over time, returning to Madison for my Master’s in Manufacturing Systems Engineering. And then my career had added for the last 27 years, leading an analytics organization. But it really all started with an engineering challenge that couldn’t be solved with engineering alone. It really needed to be complemented with not just mechanistic understanding, but also empirical understanding. And then empirical understanding was all around analytics. So, it’s kind of an exciting start, and makes me think back of some exciting times in my career. 

Susan Ottmann  3:56   

Yes, definitely fascinating. Let’s dive a bit deeper and talk about data analytics and why it is important for engineers today.  

Tony Orzechowski  4:04   

Sure, sure. Well, I think of three things when I think of your question here. One is you’ll just for the traditional engineer coming in, who needs to look at data. And you know, very traditionally engineers, you know, we’re characterizing our process. We’re trying to figure out, you know, what are the key influential factors that drive my outcomes? We’re looking at times to optimize our outcomes, you know, to drive to minimum variation or maximum yield, among other things. Often, I’m also looking to understand the capability of my product or process to meet output requirements. And then finally, I’m also looking to run and design experiments around investigational, when things aren’t going quite right, or I need to improve further, how I do that. All of these are, you know, data-driven type of activities, that when I compliment again, my engineering background is supplemented with data analytics, it’s a really powerful combination, because I can understand very quickly how to separate signal from noise, understand how to make decisions better, more rapidly and do it in a very effective way. So, it’s not as if analytics would replace it, but it supplements it. That’s path number one.

Path number two, which is becoming more prevalent today, is where analytics becomes the product itself. I’ll give you one example from my current industry, which is in healthcare and diagnostic testing. Is where we’re looking at taking multiple sources of information: demographic information about the patient, information about the results, test results, we produce on your blood or other fluids that are tested, and bringing this together, using machine learning and other methods to actually produce a probabilistic outcome of your potential for future healthcare incidences. If it might be heart disease, it might be cancer, or others. You know, blending these multiple data sources together, the analytics, the algorithm itself becomes the product, becomes a medical device, okay. And that’s an exciting field, where analytics, you take your engineering or science background, and now it becomes, again, a new product versus just a support to the engineering activities.

So that’s a second path, then finally, we know our leaders, including myself, you know, really are always looking to say, how do we make our best decisions under uncertainty, you know, when there’s variation present when there is uncertainty, and our decisions are not deterministic, or probabilistic. And again, using statistics and using data analytics to extract out our best estimates of what will probably happen in the future under various outcomes. That, you know, translate that and talk about risk and quantify the level of risk and probability is extremely powerful. So those three pathways for engineering are I think where analytics really complements it very well. 

Susan Ottmann  6:57   

Over the last year, you and I have discussed different areas of data analytics. When you think of data through the lens of an engineer, what key areas must they have proficiency?   

Tony Orzechowski  7:08   

Yeah, I think it links back to what we were just speaking of. It’s signal and noise. If that’s pattern recognition, if I’m doing vision systems, I’m doing acoustics, or if I’m looking for anomalies and idea, it’s really all about recognizing patterns and recognizing relationships between input and output factors. This is what’s going to allow me to be able to detect where something’s different. Again, if it’s facial recognition, again, if it’s looking for defects in my chip manufacturing, whatever it might be. And then if I go one level deeper, if I’m going to be really good at the analytics, I also have to primarily focus on how I design for analytics, what kind of experimental work will I do, if it’s A/B testing, if it’s factorial designs, if it’s randomized experimentation. But I need, as an engineer, one of the things I’d highly recommend for any engineer, is to go into depth of understanding how to design the best experiments that can be then leveraged for analytics, either by yourself, or by an analytics support team. Which is what we have here in my company, is our engineers do both. They analyze but they also come to a group like mine for very detailed analytics. But it all starts with really designing for those analytics effectively. And I’d highly recommend that for any engineer, if they’re going down the analytics path or not.  

Susan Ottmann  8:33   

One area I’d like to explore further that I find interesting is active experimentation. Please explain the theory of active experimentation and your viewpoint on how this can accelerate design cycles.  

Tony Orzechowski  8:46   

Yeah, sure. You know, active experimentation. I think that’s the theme we’ve been talking about, and I love it. I think it’s the single most important skill an engineer can have and an analytics person, for sure, it is understanding active experimentation. And I’ll use the term design of experimentation. I look at active experimentation as a broad set of activities that start from understanding the business outcome or the system level outcome I’m looking for, taking it down, and then understanding the responses that I measured, being able to characterize it. Look at those responses over time. Look at the robustness of them. You know, how variable are they? How well does my measurement system measure those responses, right? If I can understand that, that’ll give me a sense of the capability and how far I need to go to improve. 

That foundational piece going into the design of experiments, then drives me into: How do I select the factors that I may want to do active experimentation on? How boldly can I begin to separate those levels from low to high as I try to understand their effect on the outcome when I’m looking at the response? That bold experimentation, which is really there to amplify the signal, you know, in our design experiment approaches.

And then finally, the parts I begin looking are also the level of noise that’s present in my response, and in my factors and strategies of how I reduce that type of noise. To again amplify the signal to noise ratios to be able to say, “Yes, I can tell you that here’s the key influential factors.” And if I do that and understand the concept of which factors may confound my experiment, how to randomize it correctly, all this comes together to really provide me an experiment that tells me, here’s the key influential factors. But also, I can begin building models for optimization and really tuning my end effect, most importantly. So again, sound foundational experimental methods, I think are probably the most important skill, an engineer, outside their engineering domain, that they can complement their current process with. They complement their mechanistic understanding with empirical understanding. 

Susan Ottmann  10:54   

I find it quite interesting. I did a lot of the “Oh, when I was a young engineer,” and the field has come quite far. And the idea of getting what’s the noise and understanding that noise is so important. Another area that aligns with your work is storytelling with data, please explain this concept and how you’ve applied it to projects in the past.  

Tony Orzechowski  11:17   

Sure, sure. If I start with what I believe, my own personal view on the purpose of data analytics, is really simply to obtain meaning from data, any meaning from data. And that’s the key to analytics. However, to really have an effect, that’s not enough. You have to be open to communicate that meaning to others, if it’s your colleagues, or if it’s leadership. And that’s where storytelling comes in. Because, you know, myself, and over the years, I probably under-appreciated storytelling in my career. Only recently, in the last five to 10 years, did I really become, as I moved up in my career, to understand the power and the importance of storytelling. Because at the end of the day, we want to believe that somehow the data will speak for themselves. And the story will come out. And this, really, if you look how decisions are made by leaders, it’s an emotional event. It’s one that uses their experiential, their intuition. Yes, the data influences it. And that’s where, again, being able to effectively tell a story with your data that, you know, talking about “the big idea, my unique point of view, here’s what I believe my data is telling me,” but also what’s at stake. That creates emotional connection to your audience, if it’s a leader or others, because what’s at stake for them really causes an emotional event, which allows them to retain the information, allows it to resonate with them. And it really will allow you to, again, you know, to bring your compelling story forward in a way that they’ll embrace. So, you know, looking at that part of its really critical.

And I’d probably throw one other element on. You know, if I look at the leadership’s role in analytics, I look at leadership as having the key role with data analytics. You know, it’s not only just the analysis itself, what the analytics people will do, but if you look at it, all the key problems that really are important to the business are owned by leaders. If you look at who owns the resources to solve these problems, again, it’s our leaders. The planning process that will allow us to effectively plan the right experiments with the right resources and capital and expense dollars is our leadership. And for them to have a sound grounding, you know, through storytelling and through other means, to be able to understand what you’re trying to convey to them, they’re the ones that will control that your ability to be successful. So being able to speak to them in a way that will resonate with them best is very, very important to the career of anybody who’s in analytics, especially as they move up, you know, driving, you know, broader and broader types of decisions.   

Susan Ottmann  13:48 

So, we’d like to say one of the objectives of the program is to turn data into decisions, and you need to articulate that, and that’s really in that data storytelling.  

Tony Orzechowski  13:59 

Yeah, absolutely. Absolutely. I completely agree with your premise there, the decisions and the actions that will follow from those. Absolutely.  

Susan Ottmann  14:05 

Tony, students enrolling in the engineering data analytics programs are often career changers, moving from design or operations into a more full-time data role. Any advice for those looking for a career in data?  

Tony Orzechowski  14:18 

Yeah, yeah. You know, I’ll probably give you two pathways here. One is for the engineer who wants to remain an engineer, still wants to be designing product, but they want to use analytics, right? This kind of a program, you know, at Wisconsin is an incredible benefit to them because it shows them how to complement their engineering background, again, all the things we talked about earlier. If it’s algorithm development, or if it’s characterization, capability analysis, optimization, all those things are true. 

You know, the pathway that I pick, again, as I transition from an engineer to a data analytics person, and that was a transition, right? That happened. I started as an engineer, you know, complemented my engineering background with analytics. And then as I saw my passion in this area, I really focused heavily, completely on the analytics in support of engineers. And that’s that, that’s the next step for me was transitioning to an analytics support role. And, you know, the beauty of that is, you get to impact all products. You know, at least here, our group is probably on any given day, associated with 30 to 40 different products that are ongoing in our R&D organization. And that breadth of impact, I couldn’t have it, you know, if I were, if I remained as an engineer. That’s important to me, you know, everybody’s different. But that’s important, to be able to have that breadth of impact.

But I will say it does take a level of, I guess I would call professional maturity, because you have to understand that you’re stepping away from, I’ll call it the direct contact to the product as an engineer, where you’re designing the physical elements of it. And you’re moving sometimes into a support role for the engineers to be able to help them with their analytics and drive those things forward. And you’re a step further, you know, in this supply chain, you’re one step further back. Again, the breadth is awesome. But you have to understand that you are no longer what the engineer was, and you have to accept that role. And that takes some thinking. You have to really be ready for that decision.

One other part that I will say is, an engineer who moves into an analytics role is a really valuable entity because they become translators. And I’ve read some papers on this topic I so enjoyed that really talk about the fact that if I can have somebody in the analytics team – like I do today, I have both engineers and scientists who actually develop products – when they work with their analytics with the science team or the engineering team, they have a level of fluency, and credibility, because they understand the engineering, they understand the challenges that those folks are facing, they can translate the analytics into a way that that scientist or engineer can understand. And then they can actually, again, focus on the problem much more rapidly and effectively. That’s an incredibly powerful role for an engineer who wants to transition to data analytics, to think of themselves as an analytics expert, who has an engineering background and becomes a translator between those organizations. That shouldn’t be underestimated, the power of that particular service. 

Susan Ottmann  17:15   

Before we go, is there anything else you’d like to tell prospective students listening to this podcast, or others interested in learning more about engineering, data analytics, and its application in industry?  

Tony Orzechowski  17:26   

Yeah, yeah. I think as you start down the road, and you look at UW’s program, and I think UW’s program is unique because I think it hits the right elements. It is not only focused on the technical elements of machine learning, it looks at the breadth of analytics. It looks at even the use of statistical methods for descriptive analytics, it looks at storytelling, it looks at visualization. I think these are all really important elements. And that will give you, as generalist analytics, just a great overview.

Within that program, I’d highly recommend you find the area that you feel the greatest passion for. That might be, again, if it’s descriptive analytics, describing what’s happened, building dashboards, visualizing it, finding those, you know, those anomalies and saying, this is where we need to focus for the future for management or others. Or if they happen to be causal analytics to see why something happened, what were the causal factors, or if it’s optimization, right. All of those, each of those are very deep domains. And you know, becoming great at all of them is very, very difficult. I would say, find the area that you really have a passion in and go deep in it, and really become great at it. Again, you can expand and move into other areas. But again, find your passion within analytics, because it is such a broad, exciting area. And it’s not going away, it is only becoming stronger and more important as we move into the future with lower cost data storage, greater computing power. You know, our sensor technology, our ability to embed analytics in our products is growing.

With all these things happening, I think the future for an engineer with an analytics background is just outstanding. I just again would just say, focus on your passion, and that’s going to take care of itself. 

Susan Ottmann  19:16   

Well, Tony, thank you so much for your time today. I find your perspective fascinating and your energy exciting. And I believe your passion for the field of data will really help our students. We appreciate you talking to us and informing us on some very interesting topics. Thank you.  

Justin Kyle Bush  19:35 

Thank you for listening to Epod. For more episodes, visit interpro.wisc.edu/podcasts. And if you enjoyed this, don’t forget to subscribe, rate, review and share. 

On our premier episode, Susan Ottmann talks to Dr. Barry Van Veen from UW-Madison’s Electrical and Computer Engineering Department about machine learning and why it’s important for engineers focused on data. Barry also discusses the engineering perspective on data science and data analytics versus a computer science view, as well as why signal processing is such a hot area of study today.  

Barry D. Van Veen received his BS degree in Electrical Engineering from Michigan Technological University in Houghton, Michigan, in 1983, and his PhD in electrical engineering from the University of Colorado-Denver in 1986. He has been with the Department of Electrical and Computer Engineering at the University of Wisconsin–Madison since 1987, where he is currently the associate chair for graduate and online studies, and the Lynn H. Matthias Professor of Electrical and Computer Engineering. He has co-authored the book Signals and Systems with S. Haykin. His current research interests include signal processing and its applications—including the development of algorithms for biomedical signal processing problems. 

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Justin Kyle Bush  0:00   

Welcome to Epod, a podcast from the UW Madison’s College of Engineering Office of Interdisciplinary Professional Programs. These podcasts are focused on big ideas and engineering and the people behind them. My name is Justin Kyle Bush, and I am your host.  

On today’s episode, Susan Ottmann talks to Dr. Barry Van Veen from UW Madison’s Electrical and Computer Engineering Department about machine learning and why it’s important for engineers focused on data. Barry also discusses the engineering perspective on data science and data analytics versus the computer science view, as well as why signal processing is such a hot area of study today. Take it away, Susan.  

Susan Ottmann  0:54   

Barry, welcome. 

Barry Van Veen  0:56   

Good morning, Susan. It’s great to be here today. 

Susan Ottmann  1:00   

Before we start, Barry, please give us your view on the data analytics program. 

Barry Van Veen  1:08   

Well, data has come to be such a central issue in our world today, from a decision-making standpoint, as well as through all aspects of engineering. Just, you know, trying to understand how systems are behaving, how processes are performing, and so on. So, it has emerged with time over the last decade or so, and it’s certainly a skill that people who earned degrees a number of years ago may not have had the opportunity to develop in their studies. So, the program gives students a great opportunity to catch up on some of the latest tools and techniques for working with data, drawing inferences from data, understanding it and being savvy about modern tools, and computer methods for assessing data. So, I think that it plays an important role given just the way the field has evolved, and the importance of data and engineering today. 

Susan Ottmann  2:22   

Let’s dive a bit deeper and talk about machine learning and why that’s important for engineers who are focused on data today. 

Barry Van Veen  2:31   

Well, first of all, let’s talk about what machine learning is. Because it’s a buzzword, sometimes it may not have a lot of meaning to folks, but we can teach a computer to recognize patterns and to learn in the same way that humans do. Right? So how do we learn? We learn by seeing lots of examples. And, you know, we kind of do this instinctively, when we’re children. You know, we see patterns on a page and we learn what words mean. We learn grammar, we see pictures, and we can tell the difference between a cat and a dog. And those are all things that we can have machines learn as well. And so the thing is, data has such a huge volume that we are able to collect and store now and so on. It’s just impossible for any one person to be able to carefully analyze all that data using the power of their human brain, right? And I mean, we’ve all as engineers, we’ve been using calculators for a really long time, because it’s so much more effective than computing, right? So, machine learning allows us to automate the handling of data. Much the same way that we can use robots, for example, to automate manufacturing. So, it really is enabling things like self-driving cars, and all sorts of systems like that, where we want to be able to analyze vast volumes of data by a computer and make good decisions in a short period of time. 

Susan Ottmann  4:26   

That’s a great description and really helpful. What’s unique about your machine learning course and the reason that you focus on the fundamentals? 

Barry Van Veen  4:37   

Well, so the course that I teach a lot and that I’ve worked on in terms of creating the online curriculum is – the title is somewhat indicative of the topic, right? It’s called Matrix Methods in Machine Learning, and there’s a lot of math behind the way that we teach machines to learn and the way that we, you know, develop algorithms for that process. So, matrices, if you recall, and don’t have, you know, nightmares thinking, hearing the word, right. But matrices are just a collection of numbers in a rectangular format, right? So, rows and columns. We’re used to tables, you can think of tables as matrices. And there’s a lot of math on studying how to manipulate and what you can learn about ordered arrangements of data.  

A big part of machine learning is recognizing patterns in data. Okay. We want to understand, for example, what kind of patterns are there across the rows of this data. So, we might have a case where I mean, the classic example is the Netflix problem, where we have a whole bunch of movies. And then we have a whole bunch of individuals that have watched and rated those movies. And so, we have a matrix of ratings, right? And we’d like to understand what the patterns are, so that we can make predictions of what movie you might like. Okay, you’ve liked these other three movies and we’ve seen that there’s all these other people that have liked these same three movies and have also liked a fourth one, so maybe you’d like that fourth one, okay.  

That’s a simplified example, but there’s patterns in this data and we want to be able to extract those patterns. And matrix math, linear algebra, is the tool of choice for extracting those patterns from the data. So take lifetime students, there’s a number of different branches of engineering where this is the case lifetime. So, take a linear algebra class from a math department. And it just seems like totally, you know, theoretical ideas and abstract and spend all this time proving that something is a vector space. Well, when you take a class like the one we teach on Matrix Methods in Machine Learning, you’re going to be learning that same kind of math stuff. But it’s applied to actual real problems and showing how you can use these concepts like eigen decomposition, singular value decomposition, solving least squares problems, rank and subspaces, and how those actually have meaning in real problems.  

Why students find that to be a really useful way to learn the underlying mathematics is to do so in the context of a current application. They also find that they’re much better prepared, then, when they go to use these tools for machine learning that are out there, having an understanding of what’s sort of under the hood, and what are the fundamental principles that allow these things to work, allows you to make, you know, to intelligently use the tools and allows you to, you know, hopefully make decisions like, “Hey, this doesn’t quite make sense. I wonder if this situation is applying what we talked about where I can’t trust the result?” Okay, so understanding the fundamentals puts you in a great position to be a very effective user of these tools, and not just treat it as a magic black box. 

Susan Ottmann  8:54   

We definitely hear that from our students that the applied nature of the course, both understanding the fundamentals, but then applying it, allows them to learn. And I think as adults, that’s how we learn, we learn by applying. There is some confusion out there between the engineering perspective on data science and data analytics versus the computer science view. What do you see as the difference between these two? 

Barry Van Veen  9:19   

Well, it’s an interesting question. It’s one that I get a lot from students and I have both students. I have engineering students taking my classes. I have a lot of computer science students taking these classes. And generally the engineering students or the engineering perspective on data science is driven a lot more by the fundamentals and by understanding, you know, why things work the way they do, right? Engineers are often asking why, so that we can be more effective at creating new things, right? Why does this work the way it does? Why is this part failing? What’s wrong with it? You know, what, where’s the issue in the manufacturing line? We’re in the process of problem solving, we need to understand why.  

So, I think that one of the things you’ll see is that, in general, is that an engineering perspective on data science is more driven from the fundamentals of understanding why. And it’s less driven by things like, you know, by stereotype. This is a stereotype, okay, I’m going to admit it right up front that this is a stereotype because I’ve had some, I’ve had some computer science students that are, I guess, I’ll call them secret engineers. But they’re just, you know, outstanding students, right. But the classic thing I could see and can characterize in class kind of gives an illustration of the difference is that we’ll have something and we’ll be working on a problem in class, and the engineering students are over here. And they’re trying to like, sort of understand why it works that way and what’s a special case and all that. And the computer science students have gone out, and they’ve found a GitHub someplace, and they’ve downloaded this software, they’ve installed it, they’ve got it running, and they’re feeding data into it. And, you know, just making it, using it. Um, so the, I see that that’s kind of an exaggeration of sorts, but of the different scales, engineers being much more focused on the why.  

The other thing that helps here is to understand the history. Engineers have been interested, gosh, forever, in building things, and designing better instruments, better machines, better processes, and so on. And in the process, in order to do that, we build sensors, right, we build electrical engineering, right? We talk a lot about, I mean, we’ll have these, right, your digital camera, there’s a sensor, we’re sensing the environment, even radar, going back to the 40s. And before even processing, you know, communicating information by radio, all those things, we’re building environmental sensors. You know, measuring RPM on an engine, right? You can tell I’m an electrical engineer, my examples outside of that field are a little primitive, right? But engineers have been building systems that collect data and have been trying to use data to understand how things work for a really long time.  

And on the computer science side, the explosion in data analysis and data science has been fueled a lot by recent technology. You know, things like Google and the amount of data that Google collects, and Amazon, and those kinds of more modern things. And so, I think the engineering perspective really is tied to a lot to the physical world and is tied to the why. And that really distinguishes the engineering perspective that I see. Right, is we’re really tied to the physical world and to the why things happen and how, you know, things work. 

Susan Ottmann  13:54   

Thanks for that explanation. I think we are problem solvers as engineers, and the Why is so important. Another area we find our students interested in is learning about signal processing. Now, I’m a mechanical engineer. So sometimes your work is a bit foreign to me. From a non-electrical engineers perspective, what are the key components of signal processing and why is it important? 

Barry Van Veen  14:21   

Yeah, I sometimes joke that signal processing is what we used to call machine learning before machine learning was invented. And a lot of the mathematical tools that are used in signal processing are extremely similar to the tools that we use in machine learning. Now, when we talk about a signal, we generally referred to some data that depends on some other, on an underlying Physical construct.  

Okay, so like, if you’re collecting data in time, in other words, you’re collecting samples of data and processing those samples sequentially. Or like my voice that’s being recorded right now, right, there’s a bunch of samples of that sound that are being stored. And they’re stored as a function of time. You think of a picture, the values of those pixels occur as a function of position. Okay, so there’s some underlying physical aspect to the world that’s meaningful to that data. And that’s where the term signal comes in. Okay, I think about that as distinct from the kind of ratings data that I mentioned earlier, where, you know, users are rating products or movies or something. You know, there’s no underlying physical notion of something like time, or space, that is associated with that data. It’s just data unconnected from a physical construct. So that’s really where the whole idea of a signal comes from.  

So, signal processing refers to a set of tools that can be used to analyze data where the data is collected over time, for example, or over space. And most commonly, it’s those two cases. There are some special tools that apply when you have this dependence on time or on space. But the tools that apply more broadly to data, which is devoid of that construct of any link to that sort of physical connection, also apply to data that is a function of time. So, the signal processing round generally includes an additional set of tools and ideas that allow you to exploit either say, the time dependence, or the spatial dependence of those signals. And, or data as I should say, with the spatial time dependence of that data.  

So, I don’t know if that helps Susan, make that a little clear, or more clear, but it’s basically, signal tends to imply there’s some underlying physical quantity that is linking the various data that you collect. 

Susan Ottmann  17:50   

I find it very clear, I find it fascinating as well. So, each time I do one of these podcasts, I learn quite a bit. But before we go, is there anything else you’d like to tell our prospective students listening to this podcast or others just interesting in learning more about machine learning, or data analytics, and its application in industry? 

Barry Van Veen  18:11   

Well, you know, being a professor, I’m used to talking 75 minutes at a time, so I could go on for a long time about this. But a couple things, um, one thing that that comes to mind, that was sort of a eureka moment for me, as someone who’s been, you know, working on this stuff since the 80s, was in, I think it was fall of 2019, when Apple announced their iPhone 11. It was actually interesting to me, because one of my hobbies is photography. So I was pretty interested in the camera and the technology that they were using in the iPhone 11. And what was absolutely fascinating is they were touting their technology in a public advertisement as being based on the phone having a neural engine using machine learning. And they were talking, again – this is a public facing ad – okay, for the general public, they’re talking about how many matrix computations the chipset in this iPhone 11 could pull off in in a certain amount of time. And I never thought I would see the day where matrix computations would show up in a public facing advertisement. So, I guess you could maybe, you know, maybe the thing is, if I’m really brutally honest, maybe I’ve just been insecure all these years and Apple finally validated my existence. But I’m joking about that. But that was pretty remarkable.  

And I think, you know, you don’t have to look far to see the pervasive nature of just being able to analyze data. I mean, you know, the whole medical realm, which is an area that I’ve been emphasizing in my career since at least 2000. You know, how do we like, like looking for cancer in Radiological images? I mean, that’s something that’s just taking off from a machine learning standpoint. And you know, one of the you mentioned earlier, my interest in developing algorithms for better understanding how the brain functions. Well one of the things we do is we try to build network models for the human brain. Like so how do different areas of the brain interact with one another to accomplish certain cognitive tasks and other things? Like how is the network connectivity of the brain disrupted when you lose consciousness, such as when you go to sleep or experience anesthesia? And, you know if you look at the things we can measure today, with things like fMRI or even older technologies like electroencephalography, things we can measure about the brain are really quite astounding. And you quickly generate data that overwhelms you in terms of any sort of, you know, human interpretation.  

So, these kind of techniques are just everywhere, right? You don’t have to look far. Think about self driving cars. Think about intelligent inventory management, you know, in a factory, all this is data driven. And I think that we are going to be in for, the age of data is going to be here for quite a while. So, I see it as a really rich opportunity. I personally, my entire career, I’ve gotten to work on all sorts of different problem application areas. From acoustics to wireless communication, to medical imaging, and so on. And it’s just been an incredibly rich career, and what has linked all those things are the tools that I have that allow me to analyze data. Okay, so that’s been super fun. And I really, I think that that’s likely to be the case, for the foreseeable future. We’re gonna have, you know, the power of these tools is growing and the breadth of their applications is growing, it’s just going to create a lot of opportunities. Okay, that wasn’t quite 75 minutes, but I think I came close, so I better quit. 

Susan Ottmann  23:13   

I find your view of the world of data stimulating and knew that you’re helping our students, and so many companies in this very complex arena. Thank you so much for your time today. We appreciate you talking to us and informing us on these very intriguing topics. 

Justin Kyle Bush  23:32   

Thank you for listening to E-Pod. For more episodes, visit interpro.wisc.edu/podcasts. And if you enjoyed this, don’t forget to subscribe, rate, review and share.