Data Journeys

Laura Noren is a data science ethicist and researcher currently working in cybersecurity at Obsidian Security in Newport Beach. She holds undergraduate degrees from MIT, a PhD from NYU where she recently completed a postdoc in the Center for Data Science. Her work has been covered in The New York Times, Canada's Globe and Mail, American Public Media's Marketplace program, in numerous academic journals and international conferences. Dr. Norén is a champion of open source software and those who write it.

Enjoy the show!

Show Notes:

• [3:55] Laura explains how she produces the Data Science Community Newsletter, covering things like how the department of defense just got billions in funding to do AI research. How do you incorporate humor into such rigorous coverage?

• [10:22] How can you distinguish signal from noise in choosing a news source?
• [12:13] When and how to control your biases in your work when in the heat of the moment.
• [14:05] Laura's interests in data science began as an undergraduate at MIT, surrounded by people who build.
• [16:10] Sociology in the context of people who build, since people are the *actual* most complicated systems.
• [18:00] What important things defines a profession?
• [19:30] What's the difference between ethics and morals?
• [22:04] How ethics affects the field of data science, specifically.
• [25:35] The data science ethicist as person who is a creator, and not just there to put up stop signs.
• [31:40] How can companies strike a balance between hard stops in a product and more negotiated unique messaging for customers to address ethical employees?
• [38:53] How can smaller companies who can't afford a Chief Ethics Officer monitor and address ethical issues?
• [48:30] Techniques that can be used by individuals and organizations to identify and address ethical issues in a company.
• [50:00] How data scientists can navigate non-black and white ethical issues in their own work.
• [55:15] Laura's recommendations for ethics 101: Data and Society, AI Now Institute, and Open AI.
• [1:00:00] Laura ends off with a call-to-action to start conversations on ethics with your colleagues.

If you enjoyed this episode of Data Journeys, the best way to support the show is by leaving a review on iTunes and sharing on your social medias using the hashtag #datajourneys.

Laura's Twitter: https://twitter.com/digitalflaneuse?lang=en

Wes McKinney is the creator and "Benevolent Dictator for Life" (BDFL) of the open-source pandas package for data analysis in Python, and has also authored two versions of the reference book Python for Data Analysis. Wes is also one of the co-creators of the Apache Arrow project, which is currently his main focus. Most recently, he is the founder Ursa Labs, a not-for-profit open source development group in partnership with RStudio.

He describes himself as a problem-solver, and is particularly interested in improving the usability of data tools for programmers, accelerating data access and in-memory data processing performance, and improving data system interoperability.

In my conversation with Wes today, we focused on getting to know Wes on a more personal level, discussing his background and interests to get some insight into the living legend of open source he has become.

• [3:48] How did coming from four generations of newspaperman impact Wes's upbringing?
• [6:00] What kind of hobbies was he interested in growing up, and what is the origin of his interest in computers?
• [11:08] How did he come to run a Goldeneye 007 world record website, and update and maintain it by hand?
• [16:10] Wes's high school career as a mathlete, and how an early interest in math contributed to his approach to programming.
• [18:15] How wes brings the rigor he learned in mathematics to software engineering.
• [19:50] How languages and math scratch the same itch for composition.
• [21:00] About learning enough German to complete a PhP programming internship in Munich.
• [23:00] How Wes's experience using data in his first year working post-undergrad set him down the path to Pandas.
• [25:00] What went into his decision to take leave from grad school to build Pandas?
• [27:00] The legendary tweet where Wes expressed his sense of purpose and motivation in building Pandas.
• [29:52] Why Wes's work is motivated by the desire to free up people's time to realize their full potential.
• [30:51] Zero to One - Peter Thiel
• [31:40] Why is solving basic efficiency problems, like reading CSV files. so important?
• [34:12] How community management has played such a huge role in making Pandas so successful compared to other tools.
• [39:00] The importance of seeing peers in an open source project as people with good intentions and more than just a GitHub profile.
• [46:00] How do the incentives of an open source project influence prioritization in a project?
• [51:45] How Wes's newest project, UrsaLabs, is tackling the problem of funding in open source software development.
• [56:20] Wes's goals for UrsaLabs over the next five years.

AJ's Twitter: https://twitter.com/ajgoldstein393

Wes's Twitter:https://twitter.com/wesmckinn

Wes's personal website: http://wesmckinney.com

Wes's LinkedIn: https://www.linkedin.com/in/wesmckinn/

Mike Tamir is the Head of Data Science at Uber ATG. He is a leader in data science, specializing in deep learning and distributed scalable machine learning, and he's also a faculty member at UC Berkeley.

Mike has led several teams of Data Scientists in the San Francisco Bay Area as Chief Data Scientist for InterTrust and Formation, Director of Data Sciences for MetaScale, and Chief Science Officer for Galvanize, where he oversaw all data science product development. He also created an MS degree program in Data Science in partnership with UNH.

Mike began his career in academia serving as a mathematics teaching fellow for Columbia University and graduate student at the University of Pittsburgh. His early research focused on developing the epsilon-anchor methodology for resolving both an inconsistency he highlighted in the dynamics of Einstein's general relativity theory and the convergence of "large N" Monte Carlo simulations in Statistical Mechanics' universality models of criticality phenomena.

The focus of today's conversation was on his fake news detection AI project called Faker Fact.

Show notes:

• 0:00 First, a life update from AJ. Read about his new opportunity in Portland here on his blog.
• 5:28 What is the evolutionary explanation for why a human's capacity for careful, rational thought often takes a back seat to emotion? Explained in a comic on the project website.
• 6:17 Emotions often win over rational though, but as a result, it can be difficult to think clearly on issues we're passionate about.
• 7:05 Why people should be aware of their emotional biases, even though it's not our fault that we have them.
• 7:50 Why Facebook deleted over a billion fake accounts recently, and why fake accounts, clickbait, blatantly false content, and other forms of fake news are everywhere on social media.
• 9:10 What mechanisms can we put in place to counterbalance the parts of our nature that compel us to create and engage with content on an emotional level?
• 9:51 Since a majority of our information is second-hand, how do we distingush what's really true?
• 11:44 How did Mike become motivated to pursue this problem, on top of his full time job at Uber ATG?
• 12:45 How can we tackle "fake news" without censorship?
• 16:40 Post-Walter Cronkite era, how do we create a sense of credibility and neutrality in our information?
• 21:00 Why would it be a mistake if the algorithm learned to only classify right or left wing content as fake news?
• 22:19 The algorithm only looks at the title and words on a page, not the url.
• 23:15 How Walt (the FakerFact AI) classifies different types of content. Satire, journalism, etc.
• 26:46 How do you strike the balance of entertainment and informativeness in content?
• 31:10 What features and characteristics defines each different category of content that Walt identifies?
• 36:16 What is Walt's ideal use case?
• 36:55 You can use the FakerFact Chrome extension to view the "nutrition facts" of the page you're reading.
• 37:42 How does research on run-on sentences and other grammatical choices help Walt understand and score an article?
• 40:34 What techniques were used to train the Walt AI?
• 42:41 A discussion on the use of wisdom of the crowds in algorithms.
• 45:30 What makes it difficult to use the wisdom of the crowds when answers are too closely correlated (because of political affiliations or the news cycle?)
• 46:47 Visit Humanetech.com for tips on regulating your daily notifications and escaping the "24-hour news cycle" to prevent media from controlling your emotions.
• 50:15 Rapid fire questions!
• 52:27 Mike's advice to his 20 year old self.
• 52:40 What was his best investment in himself?
• 53:18 The Deep Learning Book a starting point for basic literacy in data science.
• 53:20 Mike, like lots of guests on this show, makes a distinction between things he believes but couldn't prove right now, and believing things for no good reason.

Show Notes: https://ajgoldstein.com/podcast/ep22

AJ's Twitter: https://twitter.com/ajgoldstein393/

Mike's LinkedIn: https://www.linkedin.com/in/miketamir/

Mike's Twitter: https://twitter.com/MikeTamir

Frank Diana is a recognized futurist, thought leader and frequent keynote speaker. He has served in various executive roles throughout his career and has over 30 years of leadership experience. Currently at Tata Consultancy Services, he is focused on leadership dialog in the context of our emerging future and its implications on business, society, governments, economies, and our environment. He blends a futurist perspective with a pragmatic, actionable approach, leveraging horizon scanning and storytelling to see possible futures and drive foresight into leadership deliberation.

His leadership experience obtained through various executive roles connects practical realities with the need to focus on an emerging future filled with complexity and change. A strong ability to connect dots enables the identification of future scenarios quickly and broadly, with an ability to see implications years into the future.

The conversation with Frank centered around his research which focuses on scanning the horizon for possible futures. We address common concerns about robots taking over the job market and eventually the world. We seek to understand what's really true, what's fear mongering, and what individuals and businesses should do to prepare for a world of change.

Some topics we covered include:

1. How Frank became so interested in educating the public as a futurist through his early career.
2. Addressing the two tipping points that have occurred thus far in humanity and changed what it means to be human-- and the coming third tipping point.
3. Addressing some of the common fears that people have about the implications of advanced AI and robotics on the future.
4. How the shift to an automated society might cause initial elimination of jobs, but ultimately will allow more time for pursuit of creative, entrepreneurial endeavors.
5. A discussion on the characteristics needed to succeed in a world of change, and what you personally should do to prepare for it.

Show Notes: https://ajgoldstein.com/podcast/ep21

AJ's Twitter: https://twitter.com/ajgoldstein393/

Frank's LinkedIn: https://www.linkedin.com/in/fdiana

Frank's website: frankdiana.net

Kyle Polich is the co-host of the incredibly popular Data Skeptic podcast, which he has been churning out since 2014. He studied computer science and focused on artificial intelligence in grad school. His general interests range from areas like statistics, machine learning, data viz, and optimization to data provenance, data governance, econometrics, and metrology.

The Data Skeptic Podcast features conversations on topics related to data science, statistics, machine learning, and artificial intelligence. The podcast breaks down into two different episode formats. One is a short form podcast where Kyle explains complex data science concepts in a way that non-data scientists can understand. In these episodes he's joined by his co-host and wife, Linh da Tran. The second format is a long form interview format where Kyle interviews experts in various data science and skepticism related arenas about their work.

In this episode of the Data Journeys Podcast, I pick Kyle's brain for patterns noticed and lessons learned through interviewing and teaching his way through nearly 400 episodes of the Data Skeptic Podcast. Some of the topics covered include:

1. How the Data Skeptic podcast became the only podcast to be endorsed by the Pope.
2. Kyle's approach to teaching complex subject matter for entry level comprehension.
3. What patterns and lessons Kyle has taken from interviewing nearly 400 guests on his show over the last four years.
4. Advice for listeners who are considering starting their own podcasts, colored by lessons Kyle has learned in his tenure.
5. Kyle and I get a little meta in trading lessons, best practices, and common experiences learned from their time hosting podcasts.

Enjoy the show!

Show Notes: https://ajgoldstein.com/podcast/ep20

AJ's Twitter: https://twitter.com/ajgoldstein393/

Kyle's LinkedIn: https://www.linkedin.com/in/kyle-polich-5047193

Emily Glassberg Sands is the Head of Data Science at Coursera - the largest online learning platform for higher education with 35M learners from around the world. Her team leads the quantitative measurement, experimentation, and inference that informs Coursera's product and business direction.

Emily received her Bachelor's' degree in Economics from Princeton University, and then moved on to complete her Ph.D. in Economics from Harvard University. At Harvard, her research focused on experimental and applied methods to better understand labor markets and consumer decision-making.

An economist by training, Emily loves using data to build better, smarter products that have a positive impact on society. In this interview, we discuss the insights into Emily has found in her work at Coursera and how they can be applied to give everyone in the world equal access to education. We covered topics like:

1. Emily's journey to a career in science, and how she went from from Montessori school, to the farms of Montana, to an Econ Ph.D at Harvard.
2. How she uses analytics to identify ways to improve the learning experience and provide high quality education worldwide.
3. Emily's Ph.D research on why companies hire based on referrals and how they performed compared to non-referral hires.
4. What is analytical creativity, how can it be developed, and why is it important for a multi-disciplinary team?
5. What Emily and her team at Coursera have discovered about the learning process and the confidence gap among learners.
6. The behaviours of successful learners and how to change your habits.

Enjoy the show!

Show Notes: https://ajgoldstein.com/podcast/ep19

AJ's Twitter: https://twitter.com/ajgoldstein393/

Emily's LinkedIn: https://www.linkedin.com/in/egsands/

Emily's Twitter: https://twitter.com/emilygsands

Dan Hammer is an environmental economist and winner of the 2017 Pritzker Prize for the Environment. Currently he serves as a National Geographic Fellow and the co-founder of Earthrise Media, and throughout 2016, he was the Senior Policy Advisor to the U.S. Chief Technology Officer, Megan Smith, as part of the Obama Administration.

Before arriving at the White House, Dan was the Presidential Innovation Fellow that released the first API listing for NASA. Prior to NASA, Hammer was the Chief Data Scientist at the World Resources Institute, where he helped re-launch Global Forest Watch, an open-source project to monitor deforestation.

After graduating from Swarthmore College in 2007 with high honors in mathematics and economics, and before receiving his PhD in environmental economics from the University of California, Berkeley, Dan was a Thomas J. Watson Fellow and traveled to Polynesia to build and race outrigger canoes. Today, among many other amazing mentors, he continues to works with Steve McCormick (former CEO of The Nature Conservancy) on web service infrastructure for environmental information.

played in the academic/private/public sectors // has stayed so true to a single mission across 17+ positions over 10 years // we took a while to get to the prize & White House

Some topics we covered include:

• How the strong sense of safety he experienced in his childhood has supported all the risk-taking he now takes on in his career.
• The lasting impact that mentors like Megan Smith, Steve McCormick, David Wheeler, and Arvind Subramanian have had on his career
• Where he sees the job of a data scientist (who knows what), ending, and a subject matter expert (who knows why), beginning.
• The most meaningful moments of his experienced at the White House, from working with a brilliant mentor to being in the situation room during the Flint Water Crisis.
• How teaching math to inmates at San Quentin State Prison for 2 years catalyzed his path to the World Resources Institute and NASA.
• Why -- across 17+ positions over the past 10 years -- democratizing scientific data and making it more accessible to the public has been THE consistent focus of his work.

Enjoy the show!

Show Notes: https://ajgoldstein.com/podcast/ep18/

Dan's Website: https://www.danham.me/r/about.html

AJ's Twitter: https://twitter.com/ajgoldstein393/

In this episode of the Data Journeys podcast, we have not one, but two guests!

Sivan Aldor-Noiman and Erik Andrejko join me from Wellio: an intelligent platform that uses machine learning and behavioral science to help people plan, shop, prepare and enjoy healthy meals at home.

Wellio is on a mission to decode how meals are prepared and enjoyed at home, both on an individual-level in terms of people's preferences and on a global-level in terms of semantic & nutritional understanding of food.

Quite interestingly, Sivan began her career in the Israeli army, serving as an instructor for an anti-tank missile unit. Afterwards, she transitioned to school and received her undergraduate degree in Industrial Engineering and a Master in Statistics from the Technion Israel Institute of Technology. At 26 years old she moved to the U.S. to complete a PhD degree in Statistics from The Wharton Business School at the University of Pennsylvania. Since then, she's spent the majority of her career at The Climate Corporation, which -- despite what the name may suggest -- specializes in using data science to optimize farming practices.

Erik graduated Summa Cum Laude in 2001 from Arizona State University's undergraduate Computer Science program before receiving his PhD in Pure Mathematics from the University of Wisconsin-Madison in 2009. He and Sivan first started working together at the Climate Corporation, where he was the VP of Science and Director of Data Science, leading the implementation of large scale statistical machine learning within numerous domains, including climatology, agronomic modeling and geo-spatial applications.

Today, Erik is the Co-Founder and Chief Technology Officer (CTO) at Wellio, while Sivan is the Head of Data Science and self-proclaimed Chief Fun Officer.

In this conversation, we dive deep into Wellio's artificial intelligence for eating, covering topics like:

• What it means to be a passionate disagreeable giver and how this fits into Sivan being Wellio's Chief Fun Officer
• The problem that Wellio is solving and why's is personally meaningful to Sivan and Erik
• The approach Wellio takes toward building recommendation systems to understand home chefs and using computer vision techniques to semantically learn food
• The most (and least) nutritious meals for your dollar, ranked by Wellio's nutritional density model
• Why Erik sees trust as the necessary gateway when transitioning from descriptive recommendations to prescriptive recommendations
• How they learned at The Climate Corporation to help people who know nothing about data science (farmers) understand the power of it for their business

Enjoy the show!

Show Notes: https://ajgoldstein.com/podcast/ep17/

Sivan's LinkedIn: https://www.linkedin.com/in/sivan-aldor-noiman-297aa421/

Erik's LinkedIn: https://www.linkedin.com/in/erik-andrejko-0900a51b/

AJ's Twitter: https://twitter.com/ajgoldstein393/

Jim Guszcza is the US Chief Data Scientist at Deloitte Consulting. Deloitte is the largest professional services network in the world in terms of revenue, with over 263,900 professionals globally.

Jim has been with Deloitte since 2001, where he took the lead on using behavioural nudge tactics to effectively act on model indications and prompt behavior change. Since then, he has gained extensive experience in applying predictive analytics to a variety of public and private sector domains.

In addition to his work with Deloitte, Jim is also an author and former professor at the University of Wisconsin-Madison business school. He received his PhD in Philosophy from the University of Chicago.

In this interview, we explore how philosophy and nudge theory can be applied to change human behavior using data science. Some highlights include:

• How growing up a sci-fi fan led Jim to pursue science and philosophy as a career.
• The importance of a philosophy education and how it can train you to analyze issues you encounter in your career.
• How philosophy can be used to humanize algorithms and aid in complex decision making processes by taking away individual bias.
• How data science can uncover and expand 'nudge theory' to encourage certain human behaviors.
• Where are the boundaries of using information uncovered by data science? When does it cross the line from actionable to information overload?

Enjoy the show!

Show Notes: https://ajgoldstein.com/podcast/ep16/

Jim's LinkedIn: https://www.linkedin.com/in/jim-guszcza-5330375/

AJ's Twitter: https://twitter.com/ajgoldstein393/

Travis Oliphant is the Founder & CEO of Quansight: a company that bridges open-source communities and innovative companies by growing talent, building technology, and discovering new products.

For years Travis has been an indispensable contributor toward data science's open-source movement through so many different outlets:

• Founder, Director, & Former CEO @ Anaconda, Inc: a free and open source distribution of over 250 popular data science packages for Python and R, used by over 6 million users.
• Founder, Chairman of the Board @ NumFOCUS Foundation: the world-renowned open-source community promoting open code development and reproducible scientific research.
• President @ Enthought: a software company best known for the early development and maintenance of the SciPy stack.
• Creator of NumPy, SciPy, Numba, & XND: all invaluable open-source Python libraries

Before founding Continuum Analytics (later renamed to Anaconda) in 2012, Travis received a Ph.D. from the Mayo Clinic, B.S. and M.S. degrees in Mathematics and Electrical Engineering from Brigham Young University, and spent nearly a decade thereafter as an Assistant Professor of Electrical and Computer Engineering at BYU before phasing out of academia to focus on creating open-source software to support industry.

Naturally, our conversation focused on his work with creating, evolving, and funding open-source software, covering topics like:

• How growing up in a bad neighborhood and being raised Mormon led him to an early fascination with computers and a laser-focus on helping people
• What led him to take a leap from the stability of a 10-year academic career to trying to support a family while working on free software
• Why open-source software has been such a centerpiece throughout his career, the inflection points he's experienced over the past 20 years
• Why community-based, open-source models have been severely underfunded, how Travis has managed to "monetize open source" with his newest company, Quansight.
• The open-source community's recent shift "away from 'local co-op' and toward 'big agriculture'", the challenges Travis is seeing pop-up
• The story behind how he first wrote the Numpy library (all by himself!) over 4-5 months of 60-70 hour workweeks before others started to see any potential
• All of the amazing open-source projects he and his team need support with from brilliant data scientists like you :)

Enjoy the show!

Show Notes: https://ajgoldstein.com/podcast/ep15/

Travis' LinkedIn: https://www.linkedin.com/in/teoliphant/

Travis' Twitter: https://twitter.com/teoliphant

AJ's Twitter: https://twitter.com/ajgoldstein393/