CV

Education

• Ph.D. in Computational Biology, Cornell University, 2025 (expected)
• B.A. in Biology and Mathematics, Pomona College, 2017

Work experience

• 2022: PhD Intern
  • Medical Affairs, Regeneron Pharmaceuticals
  • Developed machine learning models to identify predictors of NSCLC patient outcomes.

Publications

Training deep learning models on personalized genomic sequences improves variant effect prediction

A. Y. He, N. P. Palamuttam, and C. G. Danko. Training deep learning models on personalized genomic sequences improves variant effect prediction. BioRxiv. doi.org/10.1101/2024.10.15.618510

Dissection of core promoter syntax through single nucleotide resolution modeling of transcription initiation

A. Y. He and C. G. Danko. Dissection of core promoter syntax through single nucleotide resolution modeling of transcription initiation. BioRxiv. doi:10.1101/2024.03.13.583868

Topological descriptions of protein folding

E. Flapan, A. Y. He, and H. Wong. Topological descriptions of protein folding. Proc. Natl. Acad. Sci. U.S.A. doi:10.1073/pnas.1808312116.

Bcl-2 overexpression improves survival and efficacy of neural stem cell-mediated enzyme prodrug therapy

R. Mooney, A. A. Majid, D. Mota, S. Aramburo, A. Y. He, J. Covello-Batalla, D. Machado, J. Gonzaga, L. Flores, and K. S. Aboody. Bcl-2 overexpression improves survival and efficacy of neural stem cell-mediated enzyme prodrug therapy. Stem Cells Int. doi:10.1155/2018/7047496.

Talks

Training deep learning models on personalized genomic sequences improves variant effect prediction

August 30, 2024

Talk at Machine Learning in Computational Biology, University of Washington, Seattle, WA.

Dissection of core promoter architecture through single nucleotide-resolution modeling of transcription initiation

August 30, 2023

Talk at Mechanisms of Eukaryotic Transcription, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York