Justin Y. Chen

PhD Student at MIT

justin.png

Welcome! I am a fifth year graduate student studying theoretical computer science in the Electrical Engineering and Computer Science department at MIT. I am very happy to be advised by Piotr Indyk.

I think about the intersection of algorithm design, data analysis, and machine learning. Recently, I have been interested in optimizing repeated computations (learning-augmented algorithms), analyzing data with limited memory (streaming and sketching algorithms), and designing algorithms which hide individuals’ sensitive information (differential privacy).

During my PhD, I have been fortunate to work with Morteza Zadimoghaddam, Alessandro Epasto, and Vincent Cohen-Addad at Google Research and Nina Mishra and Tal Wagner at AWS. Previously, I was an undergrad at Stanford University and had the great pleasure of working with Greg Valiant and Peter Bailis. It all started in Hanover, New Hampshire with Chris Polashenski at the Cold Regions Research and Engineering Laboratory.

Feel free to reach out to me at {first 4 letters of first name}{first letter of last name}@mit.edu.


selected publications (see all)

As is the norm in theoretical computer science, authors are ordered alphabetically by last name for most papers. Exceptions use star(s) to indicate first author(s).

2024

  1. Differentially Private Gomory-Hu Trees
    In Preprint , 2024
  2. Evaluating the World Model Implicit in a Generative Model
    In NeurIPS , 2024 (Spotlight Award)
  3. Statistical-Computational Tradeoffs for Density Estimation
    In NeurIPS , 2024
  4. Space-Optimal Profile Estimation in Data Streams with Applications to Symmetric Functions
    Justin Y. ChenPiotr Indyk, and David P. Woodruff
    In ITCS , 2024

2023

  1. Improved Frequency Estimation Algorithms with and without Predictions
    In NeurIPS , 2023 (Spotlight Award)
  2. Constant Approximation for Individual Preference Stable Clustering
    In NeurIPS , 2023 (Spotlight Award)
  3. Data Structures for Density Estimation
    In ICML , 2023
  4. Differentially Private All-Pairs Shortest Path Distances: Improved Algorithms and Lower Bounds
    In SODA , 2023

2022

  1. (Optimal) Online Bipartite Matching with Degree Information
    Anders AamandJustin Y. Chen, and Piotr Indyk
    In NeurIPS , 2022
  2. Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks
    In NeurIPS , 2022
  3. Faster Fundamental Graph Algorithms via Learned Predictions
    Justin Y. ChenSandeep SilwalAli Vakilian, and Fred Zhang
    In ICML , 2022

2020

  1. Worst-Case Analysis for Randomly Collected Data
    Justin Y. ChenGregory Valiant , and Paul Valiant
    In NeurIPS , 2020 (Oral Award)