Alan Nawzad Amin

I'm Alan Nawzad Amin! I build statistical tools for all types of biological sequence data, especially proteins, transcriptomics, and genomics. In particular, I build models and tests that 1) scale to massive modern datasets, 2) are provably flexible enough to fit this data, and 3) incorporate biological assumptions.

Bio: I am a faculty fellow at the Courant Institute at New York University, working with the Wilson lab. In the summer of 2023, I was a postdoc at Jura bio. I completed my PhD in the Harvard Systems Biology program supervised by Debora Marks in 2023. I graduated with a BS in Biochemistry and Mathematics from the University of Toronto in 2019. A link to my CV. A link to a research statement (2023).

Contact: Please feel free to reach out to me at alanamin@nyu.edu! On Twitter @AlanNawzadAmin.

Selected works

* denotes equal contribution

Building scalable, flexible models of sequence data

Scalable and Flexible Causal Discovery with an Efficient Test for Adjacency. paper, code
    Amin A N, Wilson A G. ICML, 2024

Kernels with Guaranteed Flexibility for Reliable Machine Learning on Biological Sequences. paper, code
    Amin A N, Weinstein E N*, Marks D S*. arXiv, 2023. Student Research Award at 2023 New England Statistics Symposium

A generative nonparametric Bayesian model for whole genomes. paper, code
    Amin A N*, Weinstein E N*, Marks D S. NeurIPS, 2021.

Evaluating models of sequence data

Kernel-Based Evaluation of Conditional Biological Sequence Models. paper
    Glaser P, Paul S, Hummer A M, Deane C M, Marks D S, Amin A N. ICML, 2024.

A Kernelized Stein Discrepancy for Biological Sequences. paper, code
    Amin A N, Weinstein E N*, Marks D S*. ICML, 2023.

Non-identifiability and the blessings of misspecification in models of molecular fitness and phylogeny. paper
    Weinstein E N*, Amin A N*, Frazer J, Marks D S. NeurIPS, 2022. (Oral)

Teaching

CSCI-102: Data structures Fall 2024. Reach out if you’re an undergrad at NYU and would like to be a grader this fall!

CSCI-102: Data structures Spring 2024. Website