Hi there 👋, I am Fangzhao. I am a PhD student in the Department of Electrical Engineering at Stanford University, advised by Stephen Boyd and Mert Pilanci. Prior to joining Stanford, I obtained my bachelor’s degree from the University of British Columbia, majoring in combined honors computer science and math. My research interest lies broadly in convex optimization and deep learning.
I'm open and happy to chat about research ideas :)
email: zfzhao@stanford.edu
News: I will be co-organizing "ScaleOPT: GPU-Accelerated and Scalable Optimization" workshop at NeurIPS 2025 with Parth Nobel, Maximilian Schaller, Tetiana Parshakova, Tobia Marcucci, and Alexandre Amice. Check our website ScaleOpt for update! The workshop focuses on exploring how current software and hardware developed for AI systems can be used for optimization problem solving. Related paper contribution is welcome! See you in San Diego!
Research
- AlgoTune: Can Language Models Speed Up General-Purpose Numerical Programs?
Ori Press, Brandon Amos, Haoyu Zhao, Yikai Wu, Samuel K. Ainsworth, Dominik Krupke, Patrick Kidger, Touqir Sajed, Bartolomeo Stellato, Jisun Park, Nathanael Bosch, Eli Meril, Albert Steppi, Arman Zharmagambetov, Fangzhao Zhang, David Pérez–Piñeiro, Alberto Mercurio, Ni Zhan, Talor Abramovich, Kilian Lieret, Hanlin Zhang, Shirley Huang, Matthias Bethge, Ofir Press
Preprint 2025
[pdf][code][website] - Solving Large Multicommodity Network Flow Problems on GPUs
Fangzhao Zhang, Stephen Boyd
Preprint 2025
[pdf][code][arXiv] - Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes
Erica Zhang*, Fangzhao Zhang*, Mert Pilanci
International Conference on Machine Learning (ICML) 2025
[pdf][arXiv] - Newton Meets Marchenko-Pastur: Massively Parallel Second-Order Optimization with Hessian Sketching and Debiasing
Elad Romanov, Fangzhao Zhang, Mert Pilanci
International Conference on Learning Representations (ICLR) 2025
[pdf][arXiv] - Spectral Adapter: Fine-Tuning in Spectral Space
Fangzhao Zhang, Mert Pilanci
Conference on Neural Information Processing System (NeurIPS) 2024
[pdf] [code][arXiv] - Riemannian Preconditioned LoRA for Fine-Tuning Foundation Models
Fangzhao Zhang, Mert Pilanci
International Conference on Machine Learning (ICML) 2024
[pdf] [code][arXiv] - Optimal Shrinkage for Distributed Second-Order Optimization
Fangzhao Zhang, Mert Pilanci
International Conference on Machine Learning (ICML) 2023
[pdf][arXiv] - Implementation of an Oracle-Structured Bundle Method for Distributed Optimization
Tetiana Parshakova, Fangzhao Zhang, Stephen Boyd
Optimization and Engineering 2023
[pdf] [code] [arXiv]
Teaching
Head TA: EE364A/CME364A (2024-2025 Winter, Stanford)
Honors
Stanford Graduate Fellowship Stanford 2021
Governor General's Academic Medal (media cover) UBC 2021