Peter Pao-Huang | Stanford University

About Me

I am a Computer Science Ph.D. student at Stanford University. My research aims to develop principled machine learning methods to understand, treat, and reverse disease. I am partially funded by the NSF Graduate Research Fellowship (GRFP).

Previously, I obtained my B.S. in Computer Science from the University of Illinois at Urbana-Champaign, advised by Vikram Adve (UIUC), Bonnie Berger (MIT), and Morgan Levine (Altos Labs).

Research Interests

Generative Modeling, Geometric Deep Learning, Optimal Transport
Structural Biology, Single Cell, Molecular Simulation

Publications

ICML 2025

Geometric Generative Modeling with Noise-Conditioned Graph Networks

Peter Pao-Huang, Mitchell Black, Xiaojie Qiu

International Conference on Machine Learning 2025

In Submission

Unpaired Single Cell Dataset Alignment with Wavelet Optimal Transport

Peter Pao-Huang, Kyra Thrush, Daniel Montemayor, Morgan Levine

In Submission

NeurIPS GenBio 2023

Scalable Multimer Structure Prediction using Diffusion Models

Peter Pao-Huang, Bowen Jing, Bonnie Berger

NeurIPS 2023 Generative AI and Biology Workshop

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ICLR MLDD 2023

EigenFold: Generative Protein Structure Prediction with Diffusion Models

Bowen Jing, Ezra Erives, Peter Pao-Huang, Gabriele Corso, Bonnie Berger, Tommi Jaakkola

Machine Learning for Drug Discovery Workshop at International Conference on Learning Representations 2023

PDF Code

MLSys 2023

ApproxCaliper: A Programmable Framework for Application-aware Neural Network Optimization

Yifan Zhao, Hashim Sharif, Peter Pao-Huang, Vatsin Shah, Arun Narenthiran Sivakumar, Mateus Valverde Gasparino, Abdulrahman Mahmoud, Nathan Zhao, Sarita Adve, Girish Chowdhary, Sasa Misailovic, Vikram Adve

Proceedings of Machine Learning and Systems 2023

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