Matthew Levine

Matthew Levine

PhD Candidate in Computing + Mathematical Sciences

Advised by Andrew Stuart

California Institute of Technology

About me

Welcome! I am a graduate student in computing and mathematical sciences at Caltech. My work focuses on improving the prediction and inference of physical systems by blending machine learning, mechanistic modeling, and data assimilation techniques. I aim to build robust, unifying theory for these approaches, as well as develop concrete applications. I have worked substantially in the biomedical sciences, and enjoy collaborating on impactful applied projects.

I expect to graduate in May 2023, and am actively seeking full-time positions at the intersections of machine learning, dynamical systems, and biomedical sciences. Please feel free to email me if you have open positions for which I would be a good fit!

I am always interested in developing new collaborations. If you think our work is complementary, please send me a message!


  • Dynamical Systems
  • Machine Learning
  • Data Assimilation
  • Biomedicine


  • BA in Biophysics, 2015

    Columbia University

  • PhD in Computing + Mathematical Sciences, Expected May 2023


Recent Publications

Learning Absorption Rates in Glucose-Insulin Dynamics from Meal Covariates
Real-Time Electronic Health Record Mortality Prediction during the COVID-19 Pandemic: A Prospective Cohort Study
Using Data Assimilation of Mechanistic Models to Estimate Glucose and Insulin Metabolism



PhD Student

Computing + Mathematical Sciences, California Institute of Technology

Sep 2018 – Present Pasadena, CA
Advised by Professor Andrew Stuart

Research Associate

Biomedical Informatics, Columbia University Medical Center

Jul 2015 – Jul 2018 New York, NY
Supervised by Professors David Albers, George Hripcsak, and Lena Mamykina