Ph.D. in Computer Science, Cornell University

Tri-Institutional Fellow in Computational Biology and Medicine

Bio

I am currently a Senior Applied Scientist at Amazon AI Labs. My current areas of interest include semantic parsing, large language models, generative models, multimodal models, NLP, and machine learning.

Papers

• My Google Scholar page for my latest publications
• My Publications at Amazon.Science
• My ACL Anthology

Fun Stuff + Personal

• My Math Genealogy (source). My math genealogy ancestors include Hilbert, Lipschitz, Gauss, Dirichlet, Fourier, Poisson, Lagrange, Laplace, Euler, Bernoulli, and Leibniz.
• LinkedIn

Blog posts

1. (Feb 2022) Improving question-answering models that use data from tables
2. (May 2019) Use Object2Vec to learn document embeddings
3. (Nov 2018) Introduction to Amazon SageMaker Object2Vec
4. (Oct 2018) Amazon SageMaker Neural Topic Model now supports auxiliary vocabulary channel, new topic evaluation metrics, and training subsampling

Software

• dna2vec - Consistent vector representations of variable-length k-mers
• GIMSAN - software for motif-discovery equipped with a practical and reliable statistical significance analysis.
• ALICO - Alignment Constrained null set generator: a framework to generate randomized versions of an input multiple sequence alignment that preserve some of its crucial features including its dependence structure.
• GibbsILR - motif-finder that optimizes for the incomplete likelihood ratio (ILR)