Alexander E. Cohen

AI for Science Researcher. PhD Student @ MIT CSE + ChemE.

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I am a final year PhD student at MIT CSE and ChemE. I am advised by Jörn Dunkel and Martin Bazant and funded by an NDSEG fellowship. I work on machine learning methods for discovering physically-constrained differential equations from data and then controlling and optimizing these systems. I have applied these methods across many fields including biophysics, neuroscience, animal behavior, batteries, and materials. I am excited about all things related to AI for science.

I did my undergrad at the University of Chicago, where I started my research career at the Pritzker School of Molecular Engineering studying liquid crystals and conjugated polymers with Professor Juan de Pablo.

I have also learned a lot from two industry internships in recent years. At IBM Research, I worked on developing spintronic materials for applications in racetrack memory for neuromorphic computing. At Flagship Pioneering, I worked on developing AI agents for automating genome-wide analysis in early stage drug discovery.

Outside of research, I enjoy tennis, squash, hiking, triathlons, trivia, and almost all sports/games.

News

May 07, 2025 I gave an invited talk at the MIT Computational and Systems Biology seminar on Predicting and controlling nonlinear locomotion dynamics using neural activity.
Feb 28, 2025 I gave a presentation at the MIT Computational Science and Engineering seminar on Predicting and controlling nonlinear locomotion dynamics using neural activity.
Apr 01, 2024 My work on learning low dimensional representations and probabilistic models for C. elegans neural activity, locomotion, and behavior was covered in MIT Spectrum.
Dec 09, 2023 I attended the International Institute for Sustainability with Knotted Chiral Meta Matter Winter School 2024 and presented learning models for undulatory locomotion in animals.
Sep 13, 2023 Our work on learning physics from images for lithium ion battery materials was published in Nature and covered in MIT News and SLAC News.
Jul 28, 2023 I presented at JuliaCon 2023 in the computational biology session on low-dimensional mode representataions of biological systems.
Jun 10, 2023 My work on learning physically-constrained models in mode space for biophysical systems was published in Physical Review Letters and covered by the popular science magazine New Scientist.
Mar 10, 2023 I presented at the APS March Meeting 2023 in Las Vegas on learning and controlling phase separation in complex geometries with differentiable physics.
Jun 17, 2022 I gave an invited talk to the battery reading group of the machine learning research team at Toyota Research Institute. I presented on scientific machine learning with applications to continuum modeling of battery materials.
Mar 17, 2022 I presented at the APS March Meeting 2022 in Chicago on spectral model dynamics of animal behavior.