I am a final-year Ph.D. student at the ECE Department of UT Austin, advised by Prof. David Z. Pan
(Fellow of ACM, IEEE, SPIE) and Prof. Ray T. Chen
(Fellow of NAI, IEEE, OSA, SPIE). I have the privilege to work closely with Prof. Zhangyang
"Atlas" Wang at UT Austin on efficient AI.
I was raised, and completed all my schooling before college, in
a small town
in southwest China.
I’m grateful for the opportunities that followed; the path I’m on now was beyond anything I imagined growing up. 🙂
My research aims to develop efficient and scalable AI algorithms and systems.
I am particularly excited about redesigning AI algorithms to be both theoretically
grounded and practically efficient.
🤗 Scalable and Theory-grounded Optimization for Foundation Models: Pre-Training & Post-training (RL)
🤗 Hardware-software Co-design for Efficient AI Deployment
♾️Meta AI|May '25 – Dec '25|Research Scientist Intern
🧠Google Brain|Jul '22 – Nov '22|Student Researcher
Topic: RL-based Chip Placement
|Advisor:Dr. Joe Jiang
I have published papers in top conferences in machine learning/ system/computer architecture/design automation, including MLSys, HPCA, NeurIPS, ICCV, COLM, DAC, ICCAD, and TCAD.
The Path Not Taken: RLVR Provably Learns Off the Principals (Arxiv)
Hanqing Zhu,
Zhenyu Zhang,
Hanxian Huang,
DiJia Su,
Zechun Liu,
Jiawei Zhao,
Igor Fedorov,
Hamed Pirsiavash,
Zhizhou Sha,
Jinwon Lee,
David Z. Pan,
Zhangyang Wang*†,
Yuandong Tian*†,
Kai Sheng Tai*† Arxiv 2025. NeurIPS 2025 Workshop on Efficient Reasoning (Spotlight)
[
Paper /
Blog /
X post /
量子位 /
新智元
]
First theory-driven RLVR study and guidance for geometry-aligned RL optimization
Can Test-Time Scaling Improve World Foundation Model?
(Arxiv)
Wenyan Cong*,
Hanqing Zhu*,
Peihao Wang,
Bangya Liu,
Dejia Xu,
Kevin Wang,
David Z. Pan,
Yan Wang,
Zhiwen Fan,
Zhangyang Wang
Conference on Language Modeling (COLM), 2025
[
Paper /
Code
]
First efficient test-time scaling for world foundation model
APOLLO: SGD-like Memory, AdamW-level Performance
(Arxiv)
Hanqing Zhu*,
Zhenyu Zhang*,
Wenyan Cong,
Xi Liu,
Sem Park,
Vikas Chandra,
Bo Long,
David Z. Pan,
Zhangyang Wang,
Jinwon Lee
Conference on Machine Learning and Systems (MLSys), 2025
PACE: Pacing Operator Learning to Accurate Optical Field Simulation for Complicated Photonic Devices
(Arxiv)
Hanqing Zhu,
Wenyan Cong,
Guojin Chen,
Shupeng Ning,
Ray Chen,
Jiaqi Gu,
David Z. Pan
Conference on Neural Information Processing Systems (NeurIPS), 2024
[
Paper /
Code
]
Theory-grounded efficient and fast operator model for scientific simulation
Lightening-Transformer: A Dynamically-operated Optically-interconnected Photonic Transformer Accelerator
(Arxiv)
Hanqing Zhu,
Jiaqi Gu,
Hanrui Wang,
Zixuan Jiang,
Zhekai Zhang,
Rongxin Tang,
Chenghao Feng,
Song Han,
Ray T. Chen,
David Z. Pan
IEEE International Symposium on High Performance Computer Architecture (HPCA), 2024
(Acceptance Rate: 18.3%)
[
Paper /
Code
]
Hardware-software Co-design; First photonic transformer accelerator
