Hi, I'm Mike

I am currently a postdoctoral fellow in computer science at Harvard SEAS, advised by Prof. Finale Doshi-Velez. We are actively exploring applications of machine learning to clinical medicine, especially combination therapies for major depression and interventions in the Intensive Care Unit (ICU).

Our recent methods cover two exciting areas of core ML research: (1) Semi-supervised learning: We have new objectives for training semi-supervised latent variable models can simultaneously discovering disease subtypes and suggest useful treatments. (2) Explainable AI: Our upcoming AAAI '18 paper shows how to optimize deep neural networks to have more interpretable decision boundaries, especially for clinical tasks.

I completed my Ph.D. in computer science at Brown University in May 2016, advised by Prof. Erik Sudderth. My thesis studied large-scale unsupervised clustering problems like organizing every New York Times article from the last 20 years or segmenting the human genome to find patterns in the epigenetic modifiers that amplify or inhibit expression. My technical focus was developing reliable non-convex optimization algorithms for a broad family of Bayesian nonparametric models that include mixtures, topic models, sequential models, and relational models. We have released an open-source Python package called BNPy. Please try it out!

Job Search!

I am actively looking for tenure-track faculty positions this season (2017-18). Please reach out if you have questions.

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