How do we make the next generation of machine learning models that are explainable? How do you start finding new kinds of models that might be explainable? Where do you even start thinking about that process from a research perspective?

Nikos begins with a discussion on how we make decisions in general.  In the scientific world, we mostly reason through statistical or cause-and-effect type scenarios.  We can predict outcomes and train our models to produce the results we traditionally expect.

He then discusses other early pioneers in this work, for example, back in the 70s, a rules engine was developed to help clinicians make diagnoses.  It turns out that humans are very complex and hard to codify.  Dr. Charles Forgy wrote his thesis on the Rete algorithm which is what modern-day rules-based engines stem from.

After the AI winter period, there was the introduction of neural networks that would encode the rules.  This became an issue for explainability on why the rule was created.  The neural networks create a mathematical weighted data model evaluated against the outcome.  Without the ability to open up the network to determine why some data was weighted higher than another, has been the challenge in explaining the results we see.  

There is also a concern from the European Union General Data Protection Regulation (GDPR) where a human has the right to obtain meaningful information about the logic involved, commonly interpreted as the right to an explanation.    

We want to look at explainability through two factors: a local point of view and a global point of view.  The global objective is to extract a general summary that is representative of some specific data set. So we explain the whole model and not just local decisions.  The local objective is to explain a simple prediction as a single individual observation in the data. But you have a decision according to a neural network or a classifier or a regression algorithm, so the objective is to explain just a single observation.  

There are five problems that present themselves in explainability:  Instability, Transparency, Adversarial Attacks, Privacy, and Analyst Perspective.

For Instability, we look at heat maps as they are very sensitive to hyperparameters, meaning the way that we tuned that network.  How we adjusted the sensitivity then impacts the interpretation. Transparency becomes more difficult the more accurate machine learning is.  We call that transparency because machine learning models, neural networks, are black boxes with very high dimensionality. But what's interesting is that we can say that their prediction accuracy makes explainability inversely proportional to that.  An Adversarial Attacks example is to imagine that interpretability might enable people, or programs to manipulate the system. So if one knows that for instance, having three credit cards can increase his chance of getting a loan then they can game the system by increasing their chance of getting the loan without really increasing the probability of repaying the loan.  Privacy can impact your access to the original data especially in complex systems where boundaries can exist between other companies.  You might not have the ability to access original data.  Lastly, the Analyst Perspective. When a human gets involved to explain the system, important questions include, where to start first and how ensuring the interpretation aligns with how the model actually behaved.  There are some systems by which the ML has multi-use and the human is trying to understand the perspective of use for the result given.  These are some specific ways we have found that create the complexity and challenges in explainability with machine learning models.

We continue to learn and adjust based on those learnings.  This is a very interesting and important topic that we will continue to explore.

Citations

Dr. Charles Forgy (1979), On The Efficient Implementation of Production Systems, Carnegie Mellon University, ProQuest Dissertations Publishing, 1979, 7919143

Nadia Burkart, Marco F. Huber (2020) A Survey on the Explainability of Supervised Machine Learning, arXiv:2011.07876 (cs)

Further Reading

https://openaccess.thecvf.com/content_CVPR_2019/papers/Pope_Explainability_Methods_for_Graph_Convolutional_Neural_Networks_CVPR_2019_paper.pdf

https://towardsdatascience.com/explainable-deep-neural-networks-2f40b89d4d6f

Nikos' Papers:

https://www.mdpi.com/2079-9292/8/8/832/htm

https://link.springer.com/article/10.1007/s11423-020-09858-2

https://arxiv.org/pdf/2011.07876.pdf

https://arxiv.org/pdf/2110.09467.pdf

Host:Angelo Kastroulis

Executive Producer: Kerri Patterson; 

Producer: Leslie Jennings Rowley; 

Communications Strategist: Albert Perrotta;

Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

Angelo and Manos' connection began in the 265 Course at Harvard University on Big Data Systems. This course inspired Angelo's thesis. The two discuss Manos' papers and how the future of Big Data is on the boundaries of Moore's Law. If you think about LSM trees (Log-Structured Merge Trees) and compacting data, what is considered acceptable deletion when users ask for their data to be removed? Is it when the data is removed from the identifying user that is good enough? In the analysis of Big Data Systems, considerations are always towards performance. An extensive delete sequence will cause a significant disruption in the system. Most people would address the completion of current execution cycles, perhaps during non-peak hours, and flag the no longer valid data. Maybe it could be that your data starts to become dirty, then what? How do you solve issues like privacy and the request for the "Right to be forgotten" or the "Right to erase"?  

Manos speaks about the papers he has written, which you can read in the links below. He addresses the delete question and boundaries with privacy in mind.  Performance is a crucial factor, and looking at the issue holistically is just as important as encryption when protecting privacy.

Mano's Research Papers 

• https://dl.acm.org/doi/10.1145/3318464.3389757
• https://disc-projects.bu.edu/lethe/
• https://blogs.bu.edu/mathan/2020/06/29/lets-talk-about-deletes/

Further Reading

• CS265: Big Data Systems - Spring 2020
• Manos Athanassoulis homepage
• California Consumer Privacy Act - BCLP California Consumer Protection Act Information
• General Data Protection Regulation (GDPR) – Final text neatly arranged
• Fast 21 Chen Hao

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

Angelo begins this episode with a few questions about the changes caused by COVID-19, specifically around the patient data gathering, such as blood pressure. With telemedicine practice, how reliable is the data, who is legally responsible for the accuracy of the data gathered, and how exactly do clinical decision support (CDS) tools adjust with this new change in a traditional clinician workflow?

Angelo explores more on the topic of IoT devices and the data brought into medical decisions. Again, how accurate is the data from these IoT devices, such as Fitbit scales, that a clinician can diagnose and treat from? 

Jim brings up some of the challenges that came with telemedicine such as workflow within a clinic. If the clinician seeing a patient wants the dietitian to speak with the patient, it is more of a challenge to coordinate than being within a few feet of each other. The other challenge relates to security policy and considerations patients need to agree to regard their personal privacy. To get into a virtual visit with a clinician, a patient has to follow the security protocol that provides a barrier for some elderly and disabled patients. Lastly, the challenge of all this data a patient could be collecting in their IoT devices is, how do you move that data into the EHR or in some format a CDS tool could ingest?

With the use of CDS, machine learning, and AI, the future is ripe for opportunity.

Further Reading
What is CDS - Health Gov IT
ResearchGate Publication on IoT in Health Care
Privacy-Preserving Single Decision Tree
Jim Shalaby on Twitter and LinkedIn

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

In any discussion of artificial intelligence and machine learning today, artificial neural networks are bound to come up. What are artificial neural networks, how have they developed, and what are they poised to do in the future? Host Angelo Kastroulis dives into the history, compares them to biological systems that they are meant to mimic, and talks about how hard problems like this one need to be handled carefully.

Angelo begins with a discussion of how biological neural networks help make our brain a powerful computer of complexity. He then talks about how artificial neural networks recruit the same structures and connections to create artificial intelligence. To understand what we mean by artificial intelligence, Angelo explains how the Turing Test works and how Turing’s work forms a foundation for modern AI. 

He then discusses other early pioneers in this work, namely Frank Rosenblatt, who worked on models that could learn or “perceptrons.” Angelo then relates the history of how this work was criticized by Marvin Minsky and Seymour Papert and how mistakes in their own work put the potential advances of artificial neural networks back by about two decades.

Using image recognition as a case study, Angelo ends the episode by talking about about various approaches’ benefits and drawbacks to illustrate what we can do with artificial neural networks today.

Citations

Hebb, D.O. (1949). The organization of behavior: A neuropsychological theory. New York: Wiley.

Minsky, M. (1954.) Theory of neural-analog reinforcement systems and its application to the brain-model problem. Doctoral dissertation. Princeton: Princeton University.

Minsky, M. and Papert, S. (1969). Perceptrons: An introduction to computational geometry. Cambridge: MIT Press.

Rosenblatt, F. (1957). "The perceptron: A perceiving and recognizing automaton.”Buffalo: Cornell Aeronautical Laboratory, Inc. (Accessible at https://blogs.umass.edu/brain-wars/files/2016/03/rosenblatt-1957.pdf)

Rosenblatt, F. (1962). Principles of neurodynamics: Perceptrons and the theory of brain mechanisms. Washington, D.C.: Spartan Books_._

Turing, A. (1950, October). "Computing machinery and intelligence," Mind, LIX: 236, pp. 433–460. https://doi.org/10.1093/mind/LIX.236.433  

Further Reading

Warren McCollough and the McCollough-Pitts Neuron
Church-Turing Thesis

Turing Test

XOR or Exclusive or

Host: Angelo Kastroulis

Executive Producer: Kerri Patterson; Producer: Leslie Jennings Rowley; Communications Strategist: Albert Perrotta; Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

© 2021, Carrrera Group

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

Can computer science help us live longer? It is a complex question that requires an understanding of our anticipated lifespan and how the health of our body at this moment compares to the average person our age, what some would call calculating our biological age. What computer science stands to help with is understanding how better to pinpoint that biological age and what factors may play into holding off the aging process. Guest Andy Lee, of NeuroInitiative and Vincere Biosciences, speaks with host Angelo Kastroulis about this hard problem of predicting our biological age and potentially reversing it.

Angelo begins with a discussion on some of the classical age models that have previously looked at the question of determining one’s biological age. He talks about classic papers by Horvath and Fahy that have changed the way scientists think about aging. He introduced the idea of epigenetics, or the study of the changes in living things that are caused by the medication of the way genes are expressed rather that the more classical mode of altering the genetic code itself. 

With his Guest Andy Lee, founding CTO of NeuroInitiative and COO of Vincere Biosciences, Angelo dives deeper into DNA methylation signatures and the patterns that we can look at to begin to determine someone’s biological age. Andy describes how computer science and neural networks are modernizing these determinations and what that means for improving our longevity.

The pair note the challenges posed by the sheer volume of genetic data and what advances in data science can make in our ability to push this area forward, including therapeutics for diseases such as Parkinson’s. They talk about how computer science is allowing us to have transformative information brought to us so that then we can intervene and act on it. 

About this Episode’s Guest

Andy Lee is Co-Founder, Director, and CTO of NeuroInitiative, where he is co-inventor on multiple granted and pending patents surrounding the SEED simulation platform, as well as COO at Vincere Biosciences, Inc., a Cambridge, MA, company developing disease-modifying therapies for Parkinson's disease. Previously, Andy was VP of Engineering at Black Knight through F500 acquisition, spin-out, and IPO. He has led teams of over 100 members and continues to actively code to create new data-driven solutions. You can find out more on Twitter (@Andy_D_Lee) and LinkedIn.

Citations

Fahy, GM, Brooke, RT, Watson, JP, et al. Reversal of epigenetic aging and immunosenescent trends in humans. Aging Cell. 2019; 18:e13028. https://doi.org/10.1111/acel.13028

Horvath S. (2013). DNA methylation age of human tissues and cell types. Genome biology, 14(10), R115.https://doi.org/10.1186/gb-2013-14-10-r115

Johnson, A.A., Shokhirev, M., and Shoshitaishvili, B. (August 2019). Revamping the Evolutionary Theories of Aging. Ageing Research Reviews. 55. Doi: 10.1016/j.arr.2019.100947.

The Matt Walker Podcast. https://sleepdiplomat.com/podcast

Further Reading

Handbook of Epigenetics, 2nd Edition

NeuroInitiative

Vincere Biosciences

Andy Lee on Twitter and LinkedIn

About the Host

Angelo Kastroulis is an award-winning technologist, inventor, entrepreneur, speaker, data scientist, and author best known for his high-performance computing and Health IT experience. He is the principal consultant, lead architect, and owner of Carrera Group, a consulting firm specializing in software modernization, event streaming (Kafka), big data, analytics (Spark, elastic Search, and Graph), and high-performance software development on many technical stacks (Java, .net, Scala, C++, and Rust). A Data Scientist at heart, trained at the Harvard Data Systems Lab, Angelo enjoys a research-driven approach to creating powerful, massively scalable applications and innovating new methods for superior performance. He loves to educate, discover, then see the knowledge through to practical implementation.

Host:Angelo Kastroulis

Executive Producer: Kerri Patterson; Producer: Leslie Jennings Rowley; Audio Engineer: Ryan Thompson; Communications Strategist: Albert Perrotta

Music: All Things Grow by Oliver Worth

© 2021, Carrrera Group

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

On the most recent episode, host Angelo Kastroulis made a case computer science as a potential force for good. In this continuation of the theme, he talks about why excellence is a value to strive toward and how it differs from perfection, how simplifying the question can lead to a more valuable answer, and how questions about personalized medicine point to the potential for quantum to make big improvements to life.

Angelo begins with a revelation that excellence is value that he holds dear. He distinguishes “quality”—which he defines as some standard that you're measuring yourself or others against to try to compare to similar things—and “excellence,” which means being outstanding or extremely good when compared with peers. He cautions against striving for perfection, as the benefit rarely exceeds the cost of the pursuit of perfection. This spurs musings about the limits to continuous improvement and how to balance costs and quality.

He illustrates this through the example of parsing JSON and rest servers and asks how, instead of micro-optimizing a solution we might go about eliminating serialization completely. Ultimately, he posits that the single most important factor that delineates a mediocre developer from a phenomenal developer is the ability of the latter to step away from the decades-old tendency to meet requirements and instead find ways to rethink the problem space, eliminating limitations at the onset rather than mitigating later. Relatedly, he contends that attainable smaller goals are far more valuable than ones that are very, very lofty and unreachable because we can achieve those small goals.

Angelo recounts part of a conversation he had with Kerri Patterson, Chief Strategy Officer at Carrera, on the importance of solving really hard problems and how they can impact our lives. She spoke about how most healthcare data systems are set up based on insurance rules, not patient outcomes and needs. She suggests incorporating quantum computing into clinical decision support and, ultimately, personalized medicine. This is taste of conversations that will appear in more detail later in the series.

Angelo concludes this episode with a brief discussion of what machine learning is, how its two main categories—unsupervised and supervised learning—differ, and how these concepts will fuel much of the content in the upcoming episodes of Counting Sand.

About the Host

Angelo Kastroulis is an award-winning technologist, inventor, entrepreneur, speaker, data scientist, and author best known for his high-performance computing and Health IT experience. He is the principal consultant, lead architect, and owner ofCarrera Group, a consulting firm specializing in software modernization, event streaming (Kafka), big data, analytics (Spark, elastic Search, and Graph), and high-performance software development on many technical stacks (Java, .net, Scala, C++, and Rust). A Data Scientist at heart, trained at the Harvard Data Systems Lab, Angelo enjoys a research-driven approach to creating powerful, massively scalable applications and innovating new methods for superior performance. He loves to educate, discover, then see the knowledge through to practical implementation.

Citations

Bruce, V., and Young, A. (1986). Understanding face recognition. British Journal of Psychology, 77, 305-327. doi: 10.1111/j.2044-8295.1986.tb02199.x

Foer, J. (2012, February). Feats of Memory Anyone Can Do. TED2012. Retrieved September 16, 2021, from https://www.ted.com/talks/joshua_foer_feats_of_memory_anyone_can_do

Siegler, M.G. (2010, August 4). Eric Schmidt: Every 2 Days We Create As Much Information As We Did Up To 2003. Retrieved September 16, 2021, from https://techcrunch.com/2010/08/04/schmidt-data/

SINTEF. (2013, May 22). Big Data, for better or worse: 90% of data generated over last two years. Retrieved September 16, 2021, from https://sciencedaily.com/releases/2013/05/130522085217.htm

Further Reading

Urs Hölzle on Infrastructure for the Long Term

More on Face Perception

Hippocampus

Parietal Lobe

Method of Loci

Host:Angelo Kastroulis

Executive Producer:Kerri Patterson; Producer:Leslie Jennings Rowley; Communications Strategist:Albert Perrotta

Music: All Things Grow byOliver Worth

© 2021, Carrrera Group

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

Can computer science improve our lives? Most people who work in the field would like to think so. Host Angelo Kastroulis, CEO of the Carrera Group, considers the ways that computer science is poised to make our lives better but also introduces caveats such as bias in machine learning. Along the way he sets up future episodes’ themes—including predictive analytics, simulation, and health decision support systems—that he will dive into with more technical detail.

Acknowledging that technology is neither a panacea nor a tool without downsides, Angelo starts with a review of some research on the psychological and sociological effects of social media. Beyond social media, he also questions the predictive ability of big data and introduces the idea of bias in machine learning. He does this through recalling a chance encounter with an old friend and fellow computer scientist, Andy Lee. Andy is the chief technology officer and founder of NeuroInitiative, a company that uses advanced simulation techniques to try to create new drug compounds, as well as chief operating officer of Vincere Biosciences, a company that takes these drug compounds all the way to human trials and hopefully to the market. Andy talks about how, since bias is unavoidable, we should find a way to make this weakness the strength of the model. In considering this, Angelo defines key concepts such as a model’s features, what accuracy means, and why it is important not to conflate correlation with causation. 

He shares the important axiom "All machine learning models are bad and some are less bad than others" and exhorts listeners to “Never lie with stats.” He ends by suggesting a few actionable ways that computer science can help our lives become better, setting up themes—including predictive analytics, simulation, and health decision support systems—that he will dive into with more technical detail in future episodes of Counting Sand.

About the Host

Angelo Kastroulis is an award-winning technologist, inventor, entrepreneur, speaker, data scientist, and author best known for his high-performance computing and Health IT experience. He is the principal consultant, lead architect, and owner of Carrera Group, a consulting firm specializing in software modernization, event streaming (Kafka), big data, analytics (Spark, elastic Search, and Graph), and high-performance software development on many technical stacks (Java, .net, Scala, C++, and Rust). A Data Scientist at heart, trained at the Harvard Data Systems Lab, Angelo enjoys a research-driven approach to creating powerful, massively scalable applications and innovating new methods for superior performance. He loves to educate, discover, then see the knowledge through to practical implementation.

Citations

Bruce, V., and Young, A. (1986). Understanding face recognition. British Journal of Psychology, 77, 305-327. doi: 10.1111/j.2044-8295.1986.tb02199.x

Cameron, S. (2018, November 12). Shark Attacks, ice Creams, and the Randomised Trial. Retireived Septmber 16, 2021, from https://the-gist.org/2018/11/shark-attacks-ice-creams-and-the-randomised-trial/

Data Never Seeps Infographic. (n.d.). Domo.Com. Retrieved September 16, 2021, from https://www.domo.com/learn/infographic/data-never-sleeps-8

McLean Hospital. (2021, February 9). Here’s How Social Media Affects Your Mental Health. Retrieved September 16, 2021, Https://Www.Mcleanhospital.Org/Essential/It-or-Not-Social-Medias-Affecting-Your-Mental-Health. https://www.mcleanhospital.org/essential/it-or-not-social-medias-affecting-your-mental-health

World Happiness 2019 Chapter 2. (n.d). https://Worldhappiness.Report/Ed/2019/Changing-World-Happiness/. Retrieved September 16, 2021, from https://worldhappiness.report/ed/2019/changing-world-happiness/

Further Reading

Is Social Media Bad For You: The Evidence and the Unknowns

World Happiness Report 2021

Person Perception 25 Years after Bruce and Young (1986)

Host: Angelo Kastroulis

Executive Producer: Kerri Patterson; Producer: Leslie Jennings Rowley; Audio Engineer: Mert Çetinkaya; Communications Strategist: Albert Perrotta

Music: All Things Grow by Oliver Worth

© 2021, Carrrera Group

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

How much sand would it take to fill the universe? And what does this 2,000-year-old question have to do with a podcast on today’s big data challenges? In this kick-off episode of the Counting Sand podcast, host Angelo Kastroulis, CEO of Carrera Group, explains how an early research paper by Archimedes of Syracuse has much in common with his own approach to today’s big questions in data science and how the paper provides not only a metaphor for how we can meld research and practice in tackling today’s big problems but also the inspiration for the perfect podcast name.

In order to explain the origin of the name of this podcast, Angelo starts with a little history on Archimedes, as both a practical designer and also a scientist interested in the theoretical underpinnings of mathematical principles.

Angelo then talks about some important research by Archimedes but begins by explaining what a research paper is, what the history of research papers is, and why anyone undertakes writing one. He then spends time talking about Archimedes’ paper that attempts to spell out how many grains of sand would be needed to fill the universe. Of course, to answer this, Archimedes needed to approximate the size of the universe and, in order to do that, he had to develop a new number system.

Angelo—who himself has both a Greek and entrepreneurial heritage—begins to draw parallels to Archimedes and his approach to the sand problem and his own approach to understanding and addressing big problems today. He talks about his journey to find the balance of the theoretical and practical, just as Archimedes did, applying a rigorous methodology, dealing with disappointment, and exercising patience.  Angelo shares his first operating axiom: “When the solution isn’t readily apparent, be patient, keep researching; the solution will present itself.”

In his work as a data scientist and technologist best known for his high-performance computing and Health IT experience, Angelo uses this process time and again. In this episode he gives examples from his own research career and the applications he has developed. Ultimately he shares his axiom #2: “If you find yourself doing too much theory, do more application and it will make your theory better, If you find yourself doing too much application, do more theory and it will make your application better.”

As Angelo says, Counting Sand will be a bit different than other podcasts. We will talk about some big problems and both discuss the theory behind potential solutions and see how they can be applied to tackle real problems. We are excited to bring listeners along for the ride.

Citations

Bourne, S. (2004, Deecembeer 6). A Conversation with Bruce Lindsay. A conversation with Bruce Lindsay – ACM Queue. Retrieved October 4, 2021, from https://queue.acm.org/detail.cfm?id=1036486.

Heath, T.G. (2020). The Sand-Reckoner of Archimedes (Vol. 1). Library of Alexandria.

Kastroulis, A. (2019). Towards Learned Access Path Selection: Using Artificial Intelligence to Determine the Decision Boundary of Scan vs Index Probes in Data Systems (Doctoral dissertation, Harvard University)

Further Reading

On Archimedes’ Sand Reckoner

Angelo Kastroulis’ Harvard master’s thesis

The Harvard Data Systems Lab

“Publish or Perish”

About the Host

Angelo Kastroulisis an award-winning technologist, inventor, entrepreneur, speaker, data scientist, and author best known for his high-performance computing and Health IT experience. He is the principal consultant, lead architect, and owner of Carrera Group, a consulting firm specializing in software modernization, event streaming (Kafka), big data, analytics (Spark, elastic Search, and Graph), and high-performance software development on many technical stacks (Java, .net, Scala, C++, and Rust). A Data Scientist at heart, trained at the Harvard Data Systems Lab, Angelo enjoys a research-driven approach to creating powerful, massively scalable applications and innovating new methods for superior performance. He loves to educate, discover, then see the knowledge through to practical implementation.

Host:Angelo Kastroulis

Executive Producer:Kerri Patterson; Producer:Leslie Jennings Rowley; Communications Strategist:Albert Perrotta

Music: All Things Grow byOliver Worth

© 2021, Carrera Group

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth

How many grains of sand are there in the universe? To answer that hard question, Archimedes had to invent an entire number system and only then could he attempt the computation. Today’s data challenges make that look easy.

On the COUNTING SAND podcast, host Angelo Kastroulis tackles the hard problem of how to make meaning of all the data available today.

Every few episodes, Angelo will introduce a new theme at the heart of big data and IT infrastructure and highlight the most cutting-edge applications. Whether discussing the best designs for a complex data system or the social implications of bringing a diverse skill set to data science, each new episode will provide research-backed perspectives on today’s hardest problems.

If you are pushing boundaries on methodologies that solve complex problems and want to stay up to date on the latest industry trends in Artificial Intelligence, Systems Thinking, and Big Data in general, our discussions will give you insight that you need now.

Starting October 5, 2021. 

Follow us now on your favorite podcasting platform or find us on CountingSandShow.com

About the Host

Angelo Kastroulis is an award-winning technologist, inventor, entrepreneur, speaker, data scientist, and author best known for his high-performance computing and Health IT experience. He is the principal consultant, lead architect, and owner of Carrera Group, a consulting firm specializing in software modernization, event streaming (Kafka), big data, analytics (Spark, elastic Search, and Graph), and high-performance software development on many technical stacks (Java, .net, Scala, C++, and Rust). A Data Scientist at heart, trained at the Harvard Data Systems Lab, Angelo enjoys a research-driven approach to creating powerful, massively scalable applications and innovating new methods for superior performance. He loves to educate, discover, then see the knowledge through to practical implementation.

Host: Angelo Kastroulis

Executive Producer: Kerri Patterson; Producer: Leslie Jennings Rowley; Communications Strategist: Albert Perrotta

Music: All Things Grow by Oliver Worth

© 2021, Carrrera Group

Host: Angelo Kastroulis

Executive Producer: Náture Kastroulis

Producer: Albert Perrotta

Communications Strategist: Albert Perrotta

Video/Audio Engineer: Ryan Thompson

Music: All Things Grow by Oliver Worth