I am a final-year Ph.D. candidate in Electrical Computer Engineering at University of Texas at Austin, under the supervision of Prof. David Z. Pan (Fellow of ACM, IEEE, SPIE) and Prof. Ray T. Chen (Fellow of NAI, IEEE, OSA, SPIE). I work closely with Prof. Zhangyang "Atlas" Wang on efficient AI algorithms at UT Austin.

Previously, I received the B.Eng. degree in Microelectronics Science and Engineering from Shanghai Jiaotong University with highest honor, in 2020.

I was recognized as the ML and Systems Rising Star in 2025, received MLSys'25 Outstanding Paper Award (Honorable Mention), CVPR'25 AI4CC Workshop Best Paper Award, Texas ECE Achievement Award and more honors.

I am seeking industrial positions starting Fall 2025. Feel free to reach out via email (hqzhu AT utexas.edu) if there are opportunities aligned with my expertise.

I grew up and completed all my education before college in a small town in southwest China. I am so grateful for the opportunities I've had. The path I’m on now is something I couldn’t have imagined when I was growing up.

My research interests lie in Efficient AI computing, focusing on Efficient ML Hardware/Systems and Efficient AI Algorithms with Hardware/Systems Awareness. I firmly believe that the co-design of hardware and algorithms is key to unlocking the ultimate efficiency in AI computing, shaping my expertise across both domains.

• Hardware-Software Co-Design for Emerging AI Hardware and Computing Systems:
  
  • First Photonic Transformer Accelerator [HPCA'24]. Released the first arch-level photonic accelerator simulator
  • Ultra-fast Optical Device Simulation for Photonic Design Automation [NeurIPS'24]
  • Efficient and Robust Photonic AI engine [Journal of Lightwave Technology'24, MLsys'23 SNAP workshop, ICCAD'22, TCAD'22, ASP-DAC'22]
  • Contributor of first photonic AI library TorchONN (PCM-based ONN, on-chip training)
• Efficient Training and Inference for Large Foundation Models:
  
  • Memory-efficient LLM pre-training (APOLLO) [MLSys'25] (🏆 Outstanding Paper Honorable Mention), Efficient Test-time Scaling for Multi-modality Model[COLM'25], Efficient On-Chip Training [NeurIPS'22]
  • Efficient Pre-LN Transformers [NeurIPS'23 Spotlight], Memory-efficient Neural Network with in-situ weights generation [ICCV'21]

The University of Texas at Austin Ph.D. in Electrical and Computer Engineering (2020 - 2025) First year (2020-2021) conducted part-time in China due to COVID-19 Advisor: Prof. David Z. Pan(Fellow of ACM, IEEE, SPIE)

Shanghai Jiao Tong University B.Eng. in Microelectronics Science and Engineering (2016 - 2020) Graduated with Highest Honors Overall GPA: 3.81/4.00, Ranking: 2/57

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