News

Jul 06, 2026 “EdgeBench: Unveiling Scaling Laws of Learning from Real-World Environments” is now available on arXiv.
Jun 30, 2026 I defended my Ph.D. thesis and graduated from BICMR, Peking University. I have joined ByteDance Seed as a researcher.
Apr 09, 2026 Our paper “DRAW: Domain Weight Randomization with Bayesian Updating for LLM Pre-Training” has been accepted by TMLR.
Nov 24, 2025 “ODE-based Learning to Optimize” is now published in Mathematical Programming.
Sep 18, 2025 Our paper “Accelerating Optimization via Differentiable Stopping Time” has been accepted as a spotlight at NeurIPS 2025!
Jun 04, 2025 I will present “OptMATH: A Scalable Bidirectional Data Synthesis Framework for Optimization Modeling” at ICCOPT 2025 in Los Angeles, University of Southern California (pending US visa approval).
Jun 04, 2025 Our paper “OptMATH: A Scalable Bidirectional Data Synthesis Framework for Optimization Modeling” has been accepted as a poster presentation at ICML 2025! I will attend the conference (pending Canadian visa approval). Find us at Vancouver!
Apr 03, 2025 I will give two talks titled ‘‘OptMATH: A Scalable Bidirectional Data Synthesis Framework for Optimization Modeling ‘’ and ‘‘Accelerating Optimization via Differentiable Stopping Time’’ at MOS2025.
Sep 26, 2024 I will give a talk titled ‘‘ODE-based Learning to Optimize’’ at The Applied Math PhD Seminar, Fudan University.
Jun 05, 2024 I will present the “ODE-based Learning to Optimize” at the poster session of 2024 International Workshop on Modern Optimization and Applications. The poster can be found here.
May 26, 2024 I will give a talk at The China conference on Scientific Machine Learning 2024 on the session of recent advances on learning to optimize.