Overview

Experience and Education

Senior Data Science Engineer, SimSpace

Principal Data Scientist, Geneia

Medical Science Liaison (MSL), Rheumatology, Bristol-Myers Squibb

Medical Science Liaison (MSL), Neurology, EMD Serono

University of Pennsylvania, Philadelphia, PA, Ph.D., Neuroscience

LinkedIn

Github

Publications

Thesis Lab

Professional Outlets

AI and Bias In Healthcare – a video discussion about social bias in artificial intelligence and how to address it

AI interpretability is especially critical in healthcare – a blog post about model interpretability

Model interpretability and healthcare – highlights from a podcast about data science, model interpretability, COVID-19, and healthcare

Personal Projects

State-Space Models: Learning the Kalman Filter – Different research fields may speak different mathematical languages. There’s nothing like rigorous software testing for accurate translation. Go here for the code.

Beyond Point Estimates – When we need to predict more than just a mean or a median, full posterior distributions from Bayesian models are often the way to go. But sometimes, that’s too computationally intensive and we need some shortcuts. Quantile regression is a handy alternative. For even more efficiency, we can use multi-task learning so that a single model produces all the quantiles we want. Go here for the code.

Weather and climate API – Using mock testing and FastAPI to query, create, and test web APIs. Go here for the code.

Pandas vs. Polars, Python vs. Rust: Who will win? – Benchmarks are nice, but how fast are our favorite data tools on realistic data workflows? Go here for the code.

Bayesian Updating with a Beta-Binomial Model: Basketball Edition – We start the season thinking our team is this good (or bad). But as the wins and losses pile up, how do we update our priors? Go here for the code.

Bayesian Updating with a Dirichlet-Multinomial Model: Visualizing More Outcomes – As we add outcomes to our model, the concepts stay the same but the dynamics grow more complex. Viewing animations of the model can help us develop intuitions about how it works. Go here for the code.

Investment Performance Metrics Dashboard – Plotly Dash app for tracking profit/loss and other investment performance per transaction or over time. Go here for the code.

Monitoring Data Pipelines with Airflow and Tcl/Tk – Airflow is terrific for scheduling and monitoring data pipeline components. But we also want to monitor in real-time what’s happening inside those components. Go here for the code.

Add Columns to Polars Dataframes Quickly – There are straightforward, slow ways to do things, and then there are faster ways. Know how to choose. Go here for the code.

Deep Reinforcement Learning and Rainbow – How does a computer learn to play video games?

Information Theory for Toddlers – A low-entropy bedtime story

SHAP Tutorial – How do we use Shapley values to interpret machine learning models? Go here for the code.

Case Study: How to Translate a Healthcare Problem into a Predictive Modeling Problem – How do we correctly select cases for our training data?

The Peanuts Project – Charlie Brown, Snoopy, Lucy, Linus . . . who was the most important character? Which of their relationships was the strongest? Indulge some nostalgia and hum some Guaraldi!

Classifying Medicine – How do patients experience conventional and alternative medicine differently? Yelp, random forests, ROC curves, and so much more!

Recent posts

Recent posts, mostly links to interesting articles that I have been reading:

  • What I Read: Reward Hacking
    https://lilianweng.github.io/posts/2024-11-28-reward-hacking Reward Hacking in Reinforcement LearningLilian WengNovember 28, 2024 “Reward hacking occurs when a reinforcement learning (RL) agent exploits flaws or ambiguities in the reward function to achieve high rewards,Continue readingWhat I Read: Reward Hacking
  • What I Read: data engineering
    https://javisantana.com/2024/11/30/learnings-after-4-years-data-eng.html Learnings after 4 years working with +50 companies on data engineering projectsJavi Santana “I like to call it “high performance data engineering”…. Some practical learnings, in no particular order…”
  • What I Read: Diffusion, Flow Matching
    https://diffusionflow.github.io Diffusion Meets Flow Matching: Two Sides of the Same CoinRuiqi Gao, Emiel Hoogeboom, Jonathan Heek, Valentin De Bortoli, Kevin P. Murphy, Tim SalimansDec. 2, 2024 “Flow matching and diffusionContinue readingWhat I Read: Diffusion, Flow Matching
  • What I Read: Bayesian Mixed Models
    https://towardsdatascience.com/evaluating-bayesian-mixed-models-in-r-python-27d344a03016?gi=e23f7abbacbf Evaluating Bayesian Mixed Models in R/PythonEduardo Coronado SrokaJul 3, 2020 “…model checking and evaluation are just one of those things you can’t (and shouldn’t) avoid… Yet, I think inContinue readingWhat I Read: Bayesian Mixed Models
  • What I Read: Autoencoders, Interpretability
    https://adamkarvonen.github.io/machine_learning/2024/06/11/sae-intuitions.html An Intuitive Explanation of Sparse Autoencoders for LLM InterpretabilityAdam KarvonenJun 11, 2024 “Sparse Autoencoders (SAEs) have recently become popular for interpretability of machine learning models…”
  • What I Read: simulations, chaos testing
    https://www.datadoghq.com/blog/engineering/formal-modeling-and-simulation How we use formal modeling, lightweight simulations, and chaos testing to design reliable distributed systemsArun Parthiban, Sesh Nalla, Cecilia Wat-KimNovember 20, 2024 “To analyze a distributed system during itsContinue readingWhat I Read: simulations, chaos testing
  • What I Read: Mathematics, ML
    https://thegradient.pub/shape-symmetry-structure Shape, Symmetries, and Structure: The Changing Role of Mathematics in Machine Learning ResearchHenry Kvinge16.Nov.2024 “What is the Role of Mathematics in Modern Machine Learning?”
  • What I Read: Polars vs pandas
    https://labs.quansight.org/blog/dataframe-group-by The Polars vs pandas difference nobody is talking aboutMarcoGorelliNovember 11, 2024 “We’ll then take a look at elementary aggregations with both the pandas and Polars APIs. Finally, we’ll lookContinue readingWhat I Read: Polars vs pandas
  • What I Read: Flow
    https://drscotthawley.github.io/blog/posts/FlowModels.html Flow With What You KnowScott H. HawleyNovember 13, 2024 “In this tutorial post, we provide an accessible introduction to flow-matching and rectified flow models, which are increasingly at theContinue readingWhat I Read: Flow
  • What I Read: Replacements
    https://tech.instacart.com/how-instacart-uses-machine-learning-to-suggest-replacements-for-out-of-stock-products-8f80d03bb5af?gi=a743b3b54c9f How Instacart Uses Machine Learning to Suggest Replacements for Out-of-Stock ProductsAhsaas BajajNov 7, 2024 “You’ve carefully chosen each item, but then you’re notified that some products might not beContinue readingWhat I Read: Replacements

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