CV
Research Interests
- Foundation models, generative AI, and multimodal machine learning
- Diffusion models, Transformers, and large-scale model training
- AI for science, biomolecular structure prediction, and protein design
Education
- University of Illinois Urbana-Champaign Urbana, ILPh.D. in Computer Science 2019 – 2024Advisor: Jian Peng. Co-advisor: Jianzhu Ma.
- Tsinghua University Beijing, ChinaB.E. in Automation 2014 – 2018
Experience
- ByteDance Seed Bellevue, WASenior Research Scientist Jun 2024 – PresentCore contributor to Protenix, Protenix-v1, and PXDesign.
- University of Illinois Urbana-Champaign Urbana, ILPh.D. Research Assistant Aug 2019 – May 2024Developed generative modeling methods for structured 3D molecular data.
- ByteDance AI Lab / AI4Science San Jose, CAResearch Intern May 2022 – Oct 2022; May 2023 – Aug 2023Worked on diffusion-based generative modeling for structure-based drug design.
- Tencent AI Lab Seattle, WANLP Research Intern May 2020 – Aug 2020Worked on large-scale language model pretraining and dialogue generation with RL.
- Carnegie Mellon University, Robotics Institute Pittsburgh, PAResearch Intern Sep 2018 – Jan 2019Worked on generative modeling for structured video forecasting and trajectory prediction.
Selected Publications
- Unified Modeling of 3D Molecular Generation via Atomic Interactions with PocketXMol. Cell, 2026.
- LinkerNet: Fragment Poses and Linker Co-Design with 3D Equivariant Diffusion. NeurIPS Spotlight, 2023.
- DecompDiff: Diffusion Models with Decomposed Priors for Structure-Based Drug Design. ICML, 2023.
- 3D Equivariant Diffusion for Target-Aware Molecule Generation and Affinity Prediction. ICLR, 2023.
- Energy-Inspired Molecular Conformation Optimization. ICLR, 2022.
- Generative Hybrid Representations for Activity Forecasting with No-Regret Learning. CVPR Oral, 2020.
For the full list, see Publications.
Technical Skills
- Large-Scale ML: distributed training, mixed-precision, activation checkpointing, memory optimization, custom kernels, inference optimization
- Modeling: foundation models, Transformers, diffusion models, multimodal modeling, structured generation, geometric deep learning, reinforcement learning
- Programming: Python, PyTorch, C/C++, Linux, NumPy/SciPy
