I am currently interested in computational genomics and the development of large-scale foundation models for biological applications, particularly models pretrained on DNA sequences and the epigenome, and the application of these models to understanding disease mechanism at the single cell level. In my current work, I am fortunate to be advised by Raul Rabadan and develop deep learning methods to predict the mutational evolution of viral protein sequences.
I am passionate about diversity and inclusion in the tech, AI, and medical communities, both in regards to patient access to care and on the side of AI researchers and scientific development. At Microsoft New England, I co-founded and led a Diversity Recruiting Initiative with Ehi Nosakhare targeting the recruitment of underrepresented minorities for entry-level AI positions, an effort that I am particularly excited about.
Selected Publications
A deep dive into single-cell RNA sequencing foundation models
Rebecca Boiarsky, Nalini Singh, , Gad Getz, David Sontag
Machine Learning in Computational Biology (MLCB 2023), Nov. 2023 (Oral Presentation, Top 15%)
abstract
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GET: A foundation model of transcription across human cell types
Xi Fu, Shentong Mo, Anqi Shao, Anouchka Laurent, , Adolfo A. Ferrando, Alberto Ciccia, Yanyan Lan, Teresa Palomero, David M. Owens, Eric P. Xing, Raul Rabadan
bioRxiv 2023.09.24.559168, Sept. 2023
abstract
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TabLLM: Few-shot classification of tabular data with large language models
Stefan Hegselmann, , Hunter Lang, Monica Agrawal, Xiaoyi Jiang, David Sontag
26th International Conference on Artificial Intelligence and Statistics (AISTATS 2023), Apr. 2023
abstract
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Examination and extension of strategies for improving personalized language modeling via interpolation
Liqun Shao, Sahitya Mantravadi, Tom Manzini, , Manon Knoertzer, Soundar Srinivasan, Chris Quirk
First Workshop on Natural Language Interfaces (ACL 2020), July 2020
abstract
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