Research Interest

Generative Modeling: Flow Matching, Diffusion, Discrete Diffusion, Operator Learning: Modeling Continuous Spatiotemporal Dynamics, Integral Equations, Computational Biology: Single-cell Transcriptomics Data Analysis, LLMs and Agentic AI: Autonomous Systems for Biological Discovery

Education

Publications

1. Non-Markovian Discrete Diffusion with Causal Language Models
   Y. Zhang*, S. He*, et al.
   NeurIPS 2025 (Poster)
2. TANTE: Time-Adaptive Operator Learning via Neural Taylor Expansion
   Z. Wu, S. Wang, S. Zhang, S. He, et al.
   In Review
3. Intelligence at the Edge of Chaos
   S. Zhang*, A. Patel*, S. Rizvi, N. Liu, S. He, et al.
   ICLR 2025 (Poster)
4. COAST: Intelligent Time-Adaptive Neural Operators
   Z. Wu, S. Zhang, S. He, et al.
   AI4MATH Workshop at ICML 2025 (Poster)
5. Scaling Large Language Models for Next-Generation Single-Cell Analysis
   S. Rizvi*, D. Levine*, A. Patel*, S. Zhang*, E. Wang*, S. He, et al.
   bioRxiv
6. CaLMFlow: Flow Matching using Causal Language Models
   S. He*, D. Levine*, et al.
   arXiv
7. Operator Learning Meets Numerical Analysis: Improving Neural Networks through Iterative Methods
   E. Zappala, D. Levine, S. He, et al.
   arXiv

* denotes equal contribution.

Honors & Awards

• Fan Family Fellowship, Yale University (2025)

• Outstanding Achievement in Mathematics Award, University of Michigan, Ann Arbor (2023)

• James B. Angell Scholar, University of Michigan, Ann Arbor (2023)

• University Honors, University of Michigan, Ann Arbor (2022, 2023)

Services

• Conference Reviewer:
  • International Conference on Learning Representations (ICLR)
  • AI4MATH Workshop at ICML 2025

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