Zhengqi Gao

PhD student Department of EECS Massachusetts Insitute of Technology E-mail: zhengqi@mit.edu

About me

Hi, I am a fifth year PhD student at MIT EECS, advised by Prof. Duane S. Boning. Previously, I obtained my M.S. and B.E. from School of Microelectronics, Fudan University, where I worked closely with Prof. Jun Tao and Prof. Xin Li (Duke Univ.) in the area of electronic design automation (specifically, Bayes methods for DFM). My current research interests lie mainly in design automation for elecronic and photonic integrated circuits (What is it?), novel computing paradigm (e.g., KirchhoffNet), and applied machine learning. In the summer before coming to MIT, I was very lucky to work with Prof. Ron Rohrer on the adjoint method and Prof. Hang Zhao on multimodal learning. I interned at Baidu, Shanghai Qizhi AI Insitute, Nvidia Research, and Apple. My research is interdisciplinary in nature, and has been recognized across multiple domains—for example, in EDA, I received first place in the ACM/IEEE DAC’25 PhD Forum and the Biren scholarship (1 of 3 nationwide) in 2020; in Photonics, my work was selected as Editor’s Highlights by Optica Express (2024) and Photonics Research (2023); and in ML, I was named an ML and Systems Rising Star in 2024 and had an ICLR’23 Spotlight Paper (top 5%), among a few others.

Pin1: I will be on the academic job market in fall 2025 (for the 2025-2026 cycle).

News

This is a queue (FIFO) of length 10.

• Jun, 2025: Awarded the first place in DAC’25 PhD forum.

• May, 2025: One paper on diffusion is accepted by ICML 2025. Will do an internship with Apple AR/VR display.

• May, 2024: Named as an ML and Systems Rising Star.

• Jan, 2024: Won the Best Pitch Award at MIT MTL student conference MARC 2024.

• Jun, 2023: Ranked 2nd in CVPR’23 TTM Challenge.

• Apr, 2023: Presented on MIT MTL Mini Techical Symposium on Integrated Photonics.

• Apr, 2023: Selected as a DAC young fellow.

• Jan, 2023: Our joint work with Prof. Wim Bogaerts has been accepted by Photonics Research, and highlighted as an Editor's pick.

• Jan, 2023: One paper is accepte by ICLR 2023 (top 5%, oral); see our webpage.

• Sep, 2022: One paper is accepted by Neurips 2022.

Selected Publications

Machine learning

• RL Tango: Reinforcing Generator and Verifier Together for Language Reasoning
  Kaiwen Zha*, Zhengqi Gao*, Maohao Shen, Zhang-Wei Hong, Duane S. Boning, Dina Katabi
  Arxiv Preprint 2025 (*Equal contribution)
  

• REG: Rectified Gradient Guidance for Conditional Diffusion Models
  Zhengqi Gao, Kaiwen Zha, Tianyuan Zhang, Zihui Xue, Duane S. Boning
  International Conference on Machine Learning (ICML 2025)
  

• Nominality Score Conditioned Time Series Anomaly Detection by Point/Sequential Reconstruction
  Chih-Yu Lai, Fan-Keng Sun, Zhengqi Gao, Jeffrey Lang, and Duane S. Boning
  Conference on Neural Information Processing Systems (Neurips 2023)
  

• The Modality Focusing Hypothesis: Towards Understanding Crossmodal Knowledge Distillation
  Zihui Xue*, Zhengqi Gao*, Sucheng Ren*, and Hang Zhao
  International Conference on Learning Representations (ICLR 2023, top 5%, oral) (*Equal contribution)
  

• Learning from Multiple Annotator Noisy Labels via Sample-wise Label Fusion
  Zhengqi Gao, Fan-Keng Sun, Mingran Yang, Sucheng Ren, Zikai Xiong, Marc Engeler, Antonio Burazer, Linda Wildling, Luca Daniel, and Duane S. Boning
  European Conference on Computer Vision (ECCV 2022)
  

• NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation
  Jiaqi Gu, Zhengqi Gao, Chenghao Feng, Hanqing Zhu, Ray T. Chen, Duane S. Boning, and David Z. Pan
  Conference on Neural Information Processing Systems (Neurips 2022)
  

• Co-advise: Cross Inductive Bias Distillation
  Sucheng Ren, Zhengqi Gao, Tianyu Hua, Zihui Xue, Yonglong Tian, Shengfeng He, and Hang Zhao
  IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2022)
  

Design Automation

• KirchhoffNet: A Scalable Ultra Fast Analog Neural Network
  Zhengqi Gao, Fan-Keng Sun, Ron Rohrer, and Duane S. Boning
  IEEE/ACM International Conference on Computer Aided Design (ICCAD 2024) (common Q&A, poster)

• Gradient-Based Power Efficient Functional Synthesis for Programmable Photonic Circuits
  Zhengqi Gao, Xiangfeng Chen, Zhengxing Zhang, Uttara Chakraborty, Wim Bogaerts, and Duane S. Boning
  IEEE Journal of Lightwave Technology
  

• Provable Routing Analysis of Programmable Photonics
  Zhengqi Gao, Xiangfeng Chen, Zhengxing Zhang, Chih-Yu Lai, Uttara Chakraborty, Wim Bogaerts, and Duane S. Boning
  IEEE Journal of Lightwave Technology
  

• Automatic Synthesis of Light Processing Functions for Programmable Photonics: Theory and Realization
  Zhengqi Gao, Xiangfeng Chen, Zhengxing Zhang, Uttara Chakraborty, Wim Bogaerts, and Duane S. Boning
  Photonics Research (Editor's pick)
  

• Efficient Performance Trade-off Modeling for Analog Circuit based on Bayesian Neural Network
  Zhengqi Gao, Jun Tao, Fan Yang, Yangfeng Su, Dian Zhou, and Xuan Zeng
  IEEE/ACM International Conference on Computer Aided Design (ICCAD 2019)

• Efficient Parametric Yield Estimation over Multiple Process Corners via Bayesian Inference based on Bernoulli Distribution
  Zhengqi Gao, Jun Tao, Dian Zhou, and Xuan Zeng
  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (IEEE TCAD)

• Circuit Theory of Time Domain Adjoint Sensitivity
  Jiahua Li*, Danyal Ahsanullah*, Zhengqi Gao*, and Ron Rohrer
  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (IEEE TCAD) (*Equal contribution)
  

• Efficient Non-Monte-Carlo Yield Estimation
  Zhengqi Gao and Ron Rohrer
  IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (IEEE TCAD)

For a full publication list, please see my google scholar page.