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HTTP/2 200 server: GitHub.com content-type: text/html; charset=utf-8 last-modified: Sat, 27 Sep 2025 05:25:13 GMT access-control-allow-origin: * etag: W/"68d77539-a6c6" expires: Tue, 30 Dec 2025 16:49:12 GMT cache-control: max-age=600 content-encoding: gzip x-proxy-cache: MISS x-github-request-id: C0D7:15317B:A4E940:B924F5:69540030 accept-ranges: bytes age: 0 date: Tue, 30 Dec 2025 16:39:12 GMT via: 1.1 varnish x-served-by: cache-bom-vanm7210054-BOM x-cache: MISS x-cache-hits: 0 x-timer: S1767112753.607539,VS0,VE218 vary: Accept-Encoding x-fastly-request-id: 329ac203ab77c87ad870aa9b003910a8cb059de1 content-length: 8420 Fred Zhang's Homepage

Fred Zhang

z0@berkeley.edu

I am a research scientist at Google DeepMind, working on reasoning & RL.

I completed my Ph.D. from the Theory Group at Berkeley EECS in 2024, advised by Jelani Nelson. Prior to that, I received B.S. in Computer Science and B.S. in Mathematics from Duke University.

Links: Google Scholar / Twitter

Gemini Team (incl. Fred Zhang).

Technical report, 2025 (arXiv)

ForecastBench: A Dynamic Benchmark of AI Forecasting Capabilities

Ezra Karger, Houtan Bastani, Chen Yueh-Han, Zachary Jacobs, Danny Halawi, Fred Zhang, Philip E. Tetlock ( Equal contribution).

ICLR 2025 (arXiv, code)

Approaching Human-Level Forecasting with Language Models

Danny Halawi, Fred Zhang, Chen Yueh-Han, Jacob Steinhardt ( Equal contribution).

NeurIPS 2024 (arXiv, Twitter thread, blogpost, code)

Towards Best Practices of Activation Patching in Language Models: Metrics and Methods

Fred Zhang, Neel Nanda (contribution order).

ICLR 2024 (arXiv, Twitter thread)

Adaptive Regret for Bandits Made Possible: Two Queries Suffice

Zhou Lu, Qiuyi Zhang, Xinyi Chen, Fred Zhang, David P. Woodruff, Elad Hazan (contribution order)

ICLR 2024 (arXiv)

Constant Approximation for Individual Preference Stable Clustering

Anders Aamand, Justin Y. Chen, Allen Liu, Sandeep Silwal, Pattara Sukprasert, Ali Vakilian, Fred Zhang (alphabetical order)

NeurIPS 2023 Spotlight

On Robust Streaming for Learning with Experts: Algorithms and Lower Bounds

David P. Woodruff, Samson Zhou, Fred Zhang (alphabetical order).

NeurIPS 2023 (arXiv)

Privately Estimating a Gaussian: Efficient, Robust and Optimal

Daniel Alabi, Pravesh K. Kothari, Pranay Tankala, Prayaag Venkat, Fred Zhang (alphabetical order).

STOC 2023 (arXiv)

Bayesian Strategy-Proof Facility Location via Robust Estimation

Manolis Zampetakis, Fred Zhang (alphabetical order).

AISTATS 2023 (Paper)

Robust Algorithms on Adaptive Inputs from Bounded Adversaries

Yeshwanth Cherapanamjeri, Sandeep Silwal, David P. Woodruff, Qiuyi Zhang, Fred Zhang, Samson Zhou (alphabetical order).

ICLR 2023 (arXiv)

Online Prediction in Sub-linear Space

Binghui Peng, Fred Zhang (alphabetical order).

SODA 2023 Best Student Paper (arXiv, talk at Google, talk at TTIC)

Optimal Query Complexities for Dynamic Trace Estimation

David P. Woodruff, Qiuyi Zhang, Fred Zhang (alphabetical order)

NeurIPS 2022 (arXiv)

Faster Fundamental Graph Algorithms via Learned Predictions

Justin Y. Chen, Sandeep Silwal, Ali Vakilian, Fred Zhang (alphabetical order).

ICML 2022 (arXiv)

Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization

Samuel B. Hopkins, Jerry Li, Fred Zhang (alphabetical order).

NeurIPS 2020 (arXiv)

Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms

Alexander Wei, Fred Zhang (alphabetical order).

NeurIPS 2020 (arXiv)

A Fast Spectral Algorithm for Mean Estimation with Sub-Gaussian Rates

Zhixian Lei, Kyle Luh, Prayaag Venkat, Fred Zhang (alphabetical order).

COLT 2020 (arXiv)

SGD on Neural Networks Learns Functions of Increasing Complexity

Preetum Nakkiran, Gal Kaplun, Dimitris Kalimeris, Tristan Yang, Benjamin L. Edelman, Fred Zhang, Boaz Barak (contribution order).

NeurIPS 2019 Spotlight ( arXiv)

Minimum Cut and Minimum k-Cut in Hypergraphs via Branching Contractions

Kyle Fox, Debmalya Panigrahi, Fred Zhang (alphabetical order).

SODA 2019 & ACM Transactions on Algorithms (TALG) (journal version)

Notes

Leverage Score and Feature Dimensionality (comment on Toy Models of Superposition, 2023)
Mirror Descent and Online Learning (lecture note, 2019)
Tensor Networks, Matrix Product States and Density Matrix Renormalization Group ( blog post at Windows on Theory, 2018)
Goodness-of-fit Testing and Structure Learning of the Ising Model (survey, 2018)
The Topological Approach Towards Graph Evasiveness (survey, 2017)
Berkeley Food Opinions (2024)

Teaching

Graduate Student Instructor, UC Berkeley

CS 294-165: Sketching Algorithms (Fall 20)
CS 170: Efficient Algorithms and Intractable Problems (Spring 20)

Undergraudate Teaching Assistant, Duke University

COMPSCI 330: Design and Analysis of Algorithms (Spring 16, Fall 16, Spring 17, Fall 17, Spring 18)

Contact