Justin Y. Chen (original) (raw)

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), designing algorithms which hide individuals’ sensitive information (differential privacy), and analyzing data with limited memory (streaming and sketching algorithms).

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).

2025

1. Learning-Augmented Frequent Directions
   ICLR 2025 (Spotlight Award)

2024

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

2023

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

2022

1. (Optimal) Online Bipartite Matching with Degree Information
   NeurIPS 2022
2. Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks
   NeurIPS 2022
3. Faster Fundamental Graph Algorithms via Learned Predictions
   ICML 2022

2020

1. Worst-Case Analysis for Randomly Collected Data
   NeurIPS 2020 (Oral Award)