About me

Hello! My name is Jennifer Sun, and I am a Ph.D. candidate at the Department of Operations Research and Financial Engineering at Princeton University, advised by Prof. Elad Hazan from the Department of Computer Science. I am generously awarded with the Gordon Wu Fellowship. I am broadly interested in machine learning theory, and my recent work particularly focuses on online learning and its intersection with optimization and nonstochastic control theory.

During my time at Princeton, I also worked as a student researcher for the algorithmic efficiency team with Vlad Feinberg at Google DeepMind, working on memory efficient optimization algorithms. Before coming to Princeton, I graduated Summa Cum Laude and Phi Beta Kappa from The University of Chicago with a Bachelor of Science degree in Mathematics with honors.

I was born and raised in Shanghai, China. I moved to Pebble Beach, CA to attend Stevenson School at the age of 15. Those four years have been the most precious time in my life.

Preprints

Publications

[7] Online Control in Population Dynamics. NeurIPS 2024
with Noah Golowich, Elad Hazan, Zhou Lu, Dhruv Rohatgi.
Link to paper.

[6] Tight Rates for Bandit Control Beyond Quadratics. NeurIPS 2024
with Zhou Lu.
Link to paper.

[5] Second Order Methods for Bandit Optimization and Control. COLT 2024
with Arun Suggala, Praneeth Netrapalli, Elad Hazan.
Link to paper.

[4] Optimal Rates for Bandit Nonstochastic Control. NeurIPS 2023
with Stephen Newman, Elad Hazan.
Link to paper.

[3] Sketchy: Memory-efficient Adaptive Regularization with Frequent Directions. NeurIPS 2023
with Vladimir Feinberg, Xinyi Chen, Rohan Anil, Elad Hazan.
Link to paper. Link to code.

[2] Partial Matrix Completion. NeurIPS 2023
with Elad Hazan, Adam Tauman Kalai, Varun Kanade, Clara Mohri.
Link to paper.

[1] Detecting critical transition signals from single-cell transcriptomes to infer lineage-determining transcription factors. Nucleic acids research
with Xinan H Yang, Andrew Goldstein, Zhezhen Wang, Megan Wei, Ivan P Moskowitz, John M Cunningham.
Link to paper. Link to package.

Services

Reviewer: NeurIPS 2023, STOC 2024, ICML 2024

Program committee: COLT 2023, 2024

Teaching

ORF 531: Computational Finance in C++, Fall 2022

ORF 335: Introduction to Financial Mathematics, Spring 2023

COS 597Q: AI Alignment and Safety, Fall 2023

COS 324: Introduction to Machine Learning, Spring 2024

Experience

Work Experience

  1. D.E. Shaw & Co.: Quantitative Research Intern

    06-08/2024 | New York, NY

    Systematic equities.

  2. Google DeepMind: Student Researcher

    06-08/2023, 10-12/2023 | San Francisco, CA

    Dynamic memory allocation extension on the memory efficient optimizer Sketchy.