# Hello!

I’m Michael:
- 3rd-year Computer Science PhD student advised by Chris Ré.
- Labmate at HazyResearch, Stanford AI Lab, Stanford Machine Learning Group.

I currently work on deep learning architectures for expressive + efficient long sequence modeling, and using these advances to enable learning from new tasks and data types.

I also care about deep learning robustness and personalization.

Before the COVID times, I received my A.B. in Statistics and Computer Science at Harvard in 2020. I’m grateful to have worked with Serena Yeung, Susan Murphy, and Alex D’Amour on computer vision and reinforcement learning in healthcare.

# Research

Effectively Modeling Time Series with Simple Discrete State Spaces

Michael Zhang*, Khaled Saab*, Michael Poli, Tri Dao, Karan Goel, and Christopher Ré
ICLR 2023


Contrastive Adapters for Foundation Model Group Robustness

Michael Zhang and Christopher Ré
NeurIPS 2022


Correct-N-Contrast: a Contrastive Approach for Improving Robustness to Spurious Correlations

Michael Zhang, Nimit S. Sohoni, Hongyang R. Zhang, Chelsea Finn, Christopher Ré
ICML 2022 [Long Talk]


Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning

Mayee F. Chen*, Daniel Y. Fu*, Avanika Narayan, Michael Zhang, Zhao Song, Kayvon Fatahalian, Christopher Ré
ICML 2022


Shoring Up the Foundations: Fusing Model Embeddings and Weak Supervision

Mayee F. Chen*, Daniel Y. Fu*, Dyah Adila, Michael Zhang, Frederic Sala, Kayvon Fatahalian, Christopher Ré
UAI 2022 [Oral]


Personalized Federated Learning with First Order Model Optimization

Michael Zhang, Karan Sapra, Sanja Fidler, Serena Yeung, José M. Álvarez
ICLR 2021


Using Computer Vision to Automate Hand Detection and Tracking of Surgeon Movements in Videos of Open Surgery

Michael Zhang, Xiaotian Cheng, Daniel Copeland, Arjun Desai, Melody Y. Guan, Gabriel A. Brat, Serena Yeung
AMIA 2020 Annual Symposium

Characterizing Policy Divergence for Personalized Meta-Reinforcement Learning

Michael Zhang
NeurIPS 2019 Workshops on Deep Reinforcement Learning and Meta-Learning

Design and Assembly of CRISPR/Cas9-based Virus-like Particles for Programmable and Orthogonal Genetic Engineering in Mammalian Cells

Michael Zhang
Intel Science Talent Search 2016 Finalist (2nd Place)