Profile banner
Anay Gupta

Anay Gupta

Computer Science student and researcher in bioinformatics and AI

Atlanta, Georgia
nih+1
Joined September 2025
Favicon imagelinkedin.com

Network

1.5K connections
🎓
UNT Academics
💡
AI/ML Founders
🧬
Biotech Genomics AI
💻
Microsoft AI Research
⚕️
MD Anderson Researchers
🌱
YC Ecosystem
🛡️
AI Safety Alignment

Summary

Anay Gupta is a highly accomplished Computer Science student at Georgia Institute of Technology, with a strong focus on research in bioinformatics, computational biology, and machine learning. He has contributed to significant work in single-cell transcriptomics and synthetic regulatory DNA elements, demonstrating expertise in deep learning applications within biological contexts. nih+1
His research career began early, earning him the prestigious 2022 Goldwater Scholarship while attending the Texas Academy of Mathematics and Science (TAMS). During this time, he was a research intern at the Broad Institute of MIT and Harvard, where he focused on bioinformatics analyses for single-cell experimentation and investigating genomics in drug resistance. unt+2
Anay has gained valuable industry experience through internships at top technology and biotechnology companies, including Amazon (SDE Intern), Microsoft (Research Intern), Genentech (Research Intern), and Delta Air Lines (MLE Intern), indicating a broad interest and capability in both software development and machine learning engineering.

Work

Education

Writing

Polygraph: a software framework for the systematic assessment of synthetic regulatory DNA elements

May 6, 2025

Introduces Polygraph, a Python framework for evaluating synthetic DNA elements based on features like diversity, motif/k-mer composition, similarity to endogenous sequences, and screening with predictive and foundational models.

Favicon imagegenomebiology.biomedcentral.com

Consequences of training data composition for deep learning models in single-cell biology

February 24, 2025

Investigates the impact of training dataset composition on deep learning models for single-cell transcriptomics, emphasizing the importance of diverse data and strategies for optimizing future single-cell foundation models.

Favicon imagepubmed.ncbi.nlm.nih.gov

Hobbies

Enjoys playing table tennis, poker, and swimming. getzlab