CV
Hanqing Zhu
PhD student, Dept. of Electrical and Computer Engineering @ UT Austin
Education
- Ph.D. in the University of Texas at Austin (GPA: 3.95/4.00), Texas, US, 2025(expected)
- B.Eng. in Shanghai Jiaotong University (GPA: 3.81/4.00, Rank: 2/57), Shanghai, China, 2020
- Summer School (Full schoolarship) in Peter the Great St. Petersburg Polytechnic University, St. Petersburg, Russia. 2018
Work experience
- Lightelligencea Inc (May 2023 - Sep. 2023)
- Software Research Intern, Software Team, Boston, MA
- Project: Low-bit Noise-aware Training Framework for Photonic AI Accelerator
- Google LLC (Jul. 2022 - Nov. 2022)
- Student Researcher, Google Brain Team, Mountain View, CA
- Project: Integrate DREAMPlace in RL-based ASIC Macro Placement Flow.
- University of Texas at Austin (Aug. 2020 - Present)
- Graduate Research Assistant, UTDA Lab, Austin, TX
Skills
- Programming languages
- Python (PyTorch)
- C++
- Verilog
Awards and Honors
- 1st place at IEEE/ACM MLCAD 2023 FPGA Macro-Placement Contest, MLCAD, 2023
- MLSys Student Travel Award, MLSys, 2023
- Winner at Robert S. Hilbert Memorial Optical Design Competition, Synopsys, 2022
- DAC Young Fellow, 58th IEEE/ACM Design Automation Conference (DAC), 2021
- Shanghai Outstanding Graduate, Shanghai, 2020
- Hongyi Scholarship, Shanghai Jiaotong University, 2019
- Outstanding Undergraduate Scholarship, Shanghai Jiaotong University, 2019
- Samsung Scholarship, Shanghai Jiaotong University, 2018
- Zhiyuan College Honors Scholarship, Shanghai Jiaotong University, 2018
- First Prize in China Undergraduate Mathematical Contest in Modeling, Shanghai Division, 2018
- Academic Excellence Scholarship, Shanghai Jiaotong University, 2017,2018,2019
Professional Service
- Reviewer
- Nature Photonics
- Photonic Network Communications
- International Conference on Learning Representations (ICLR’24)
- Conference on Neural Information Processing Systems (NeurIPS’23)
- AAAI workshop on DL-Hardware Co-Design for AI Acceleration (AAAI’23)
- Design Automation Conference (DAC’23)
- IEEE/ACM International Conference on Computer-Aided Design (ICCAD’22)
- IEEE Transactions on Neural Networks and Learning Systems (TNNLS’22)
- IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS’22, AICAS’23)
Related Courses
- EE382M-26: VLSI CAD and Optimization
- Dr. David Z. Pan
- EE381V: Combinatorial Optimization
- Dr. Constantine Caramanis
- EE381V: Advanced Topics in Computer Vision
- Dr. Zhangyang (Atlas) Wang
- EE382N 20-COMP ARCH: PARALLISM/LOCLTY
- Dr. Mattan Erez
- EE381K: Convex Optimization
- Dr. Constantine Caramanis
- EE382M: VLSI I
- Dr. David Z. Pan
- EE382M: VLSI Physical Design Automation
- Dr. David Pan
- EE382V: Cross-layer Machine Learning Algorithm/Hardware Co-design
- Dr. Mattan Erez and Dr. Michael Orshansky
- EE382M: Verification of Digital System
- Dr. Monica Farkash
- ECE 382V SysML: Computer Systems and Machine Learning Interplay (on-going)
- Dr. Neeraja J. Yadwadkar