I’m a 5th year PhD student in the Program in Biophysics at Harvard, advised by Debora Marks (link) and Jeff Miller (link). I develop statistical tools for modern molecular biology, working in the fields of Bayesian statistics, probabilistic machine learning, biophysics and genomics.
My primary theoretical interests are in the statistical foundations of biological sequence analysis, from the perspective of Bayesian methodology. My primary applied interests are in understanding and predicting complex biological sequence dynamics, especially over very short timescales (programmed mutagenesis systems) and very long timescales (the whole of evolution). Other research themes include model misspecification, approximate Bayesian inference, library design strategies, and pathogen forecasting.
I received my A.B. from Harvard in 2016, concentrating in Chemistry and Physics. I worked with Adam Cohen, developing analysis methods for high-throughput single-cell electrophysiology data. My graduate research is supported by a Hertz Foundation Fellowship (link).
Email: eweinstein [at] g.harvard.edu
Eli N. Weinstein, Debora S. Marks. A structured observation distribution for generative biological sequence prediction and forecasting. Accepted at ICML, 2021. preprint. Pyro code. Edward2 code. talk.
Eli N. Weinstein, Jonathan Frazer, Debora S. Marks. Deconvolving fitness and phylogeny in generative models of molecular evolution. Workshop on Learning Meaningful Representations of Life, NeurIPS 2020. paper. talk.
My google scholar page is here.