Justin Y. Chen

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

I like problems at the intersection of algorithms, data analysis, and machine learning. Recently, I have been interested in optimizing repeated computations (e.g., algorithms with predictions), streaming and sketching algorithms, and differential privacy.

Before coming to MIT, 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

2024

Space-Optimal Profile Estimation in Data Streams with Applications to Symmetric Functions

Justin Y. Chen, Piotr Indyk, and David P. Woodruff

In ITCS , 2024

arXiv Bib

@inproceedings{chen2024profileestimation,
  title = {Space-Optimal Profile Estimation in Data Streams with Applications to Symmetric Functions},
  author = {Chen, Justin Y. and Indyk, Piotr and Woodruff, David P.},
  year = {2024},
  booktitle = {ITCS},
}

2023

Data Structures for Density Estimation

Anders Aamand, Alexandr Andoni, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, and Sandeep Silwal

In ICML , 2023

arXiv Bib

@inproceedings{aamand2023datastructdensity,
  title = {Data Structures for Density Estimation},
  author = {Aamand, Anders and Andoni, Alexandr and Chen, Justin Y. and Indyk, Piotr and Narayanan, Shyam and Silwal, Sandeep},
  booktitle = {ICML},
  year = {2023},
}

Differentially Private All-Pairs Shortest Path Distances: Improved Algorithms and Lower Bounds

Justin Y. Chen, Badih Ghazi, Ravi Kumar, Pasin Manurangsi, Shyam Narayanan, Jelani Nelson, and Yinzhan Xu

In SODA , 2023

Bib HTML

@inproceedings{chen2023dpshortestpath,
  title = {Differentially Private All-Pairs Shortest Path Distances: Improved Algorithms and Lower Bounds},
  author = {Chen, Justin Y. and Ghazi, Badih and Kumar, Ravi and Manurangsi, Pasin and Narayanan, Shyam and Nelson, Jelani and Xu, Yinzhan},
  booktitle = {SODA},
  year = {2023},
}

2022

(Optimal) Online Bipartite Matching with Degree Information

Anders Aamand, Justin Y. Chen, and Piotr Indyk

In NeurIPS , 2022

arXiv Bib

@inproceedings{aamand2022matchingdegrees,
  title = {(Optimal) Online Bipartite Matching with Degree Information},
  author = {Aamand, Anders and Chen, Justin Y. and Indyk, Piotr},
  booktitle = {NeurIPS},
  year = {2022},
}

Faster Fundamental Graph Algorithms via Learned Predictions

Justin Y. Chen, Sandeep Silwal, Ali Vakilian, and Fred Zhang

In ICML , 2022

arXiv Bib

@inproceedings{chen2022fastergraph,
  title = {Faster Fundamental Graph Algorithms via Learned Predictions},
  author = {Chen, Justin Y. and Silwal, Sandeep and Vakilian, Ali and Zhang, Fred},
  booktitle = {ICML},
  year = {2022},
}

2020

Worst-Case Analysis for Randomly Collected Data

Justin Y. Chen, Gregory Valiant , and Paul Valiant

In NeurIPS , 2020 (Oral)

arXiv Bib

@inproceedings{chen2020worstcaserandomlycollected,
  title = {Worst-Case Analysis for Randomly Collected Data},
  author = {Chen, Justin Y. and Valiant, Gregory and Valiant, Paul},
  booktitle = {NeurIPS},
  year = {2020},
}