Justin Y. Chen

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 and recent 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

Scalable Private Partition Selection via Adaptive Weighting

Justin Y. Chen, Vincent Cohen-Addad, Alessandro Epasto, and Morteza Zadimoghaddam

ICML 2025

arXiv Bib

@inproceedings{chen2025privatepartition,
  title = {Scalable Private Partition Selection via Adaptive Weighting},
  author = {Chen, Justin Y. and Cohen-Addad, Vincent and Epasto, Alessandro and Zadimoghaddam, Morteza},
  year = {2025},
  booktitle = {ICML},
}

Breaking the n^1.5 Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition

Anders Aamand, Justin Y. Chen, Mina Dalirrooyfard, Slobodan Mitrović, Yuriy Nevmyvaka, Sandeep Silwal, and Yinzhan Xu

ICML 2025

Bib

@inproceedings{aamand2025privateexpander,
  title = {Breaking the n<sup>1.5</sup> Additive Error Barrier for Private and Efficient Graph Sparsification via Private Expander Decomposition},
  author = {Aamand, Anders and Chen, Justin Y. and Dalirrooyfard, Mina and Mitrović, Slobodan and Nevmyvaka, Yuriy and Silwal, Sandeep and Xu, Yinzhan},
  year = {2025},
  booktitle = {ICML},
}

Improved Approximations for Hard Graph Problems using Predictions

Anders Aamand, Justin Y. Chen, Siddharth Gollapudi, Sandeep Silwal, and Hao Wu

ICML 2025

Bib

@inproceedings{aamand2025learnedhardgraph,
  title = {Improved Approximations for Hard Graph Problems using Predictions},
  author = {Aamand, Anders and Chen, Justin Y. and Gollapudi, Siddharth and Silwal, Sandeep and Wu, Hao},
  year = {2025},
  booktitle = {ICML},
}

Learning-Augmented Frequent Directions

Anders Aamand, Justin Y. Chen, Siddharth Gollapudi, Sandeep Silwal, and Hao Wu

ICLR 2025 (Spotlight Award)

arXiv Bib

@inproceedings{aamand2025learnedfrequentdirections,
  title = {Learning-Augmented Frequent Directions},
  author = {Aamand, Anders and Chen, Justin Y. and Gollapudi, Siddharth and Silwal, Sandeep and Wu, Hao},
  year = {2025},
  booktitle = {ICLR},
}

2024

Differentially Private Gomory-Hu Trees

Anders Aamand, Justin Y. Chen, Mina Dalirrooyfard, Slobodan Mitrović, Yuriy Nevmyvaka, Sandeep Silwal, and Yinzhan Xu

Preprint 2024

arXiv Bib

@inproceedings{aamand2024dpghtree,
  title = {Differentially Private Gomory-Hu Trees},
  author = {Aamand, Anders and Chen, Justin Y. and Dalirrooyfard, Mina and Mitrović, Slobodan and Nevmyvaka, Yuriy and Silwal, Sandeep and Xu, Yinzhan},
  year = {2024},
  booktitle = {Preprint},
}

Evaluating the World Model Implicit in a Generative Model

Keyon Vafa*, Justin Y. Chen, Jon Kleinberg, Sendhil Mullainathan, and Ashesh Rambachan

NeurIPS 2024 (Spotlight Award)

arXiv Bib

@inproceedings{vafa2024worldmodels,
  title = {Evaluating the World Model Implicit in a Generative Model},
  author = {Vafa*, Keyon and Chen, Justin Y. and Kleinberg, Jon and Mullainathan, Sendhil and Rambachan, Ashesh},
  year = {2024},
  booktitle = {NeurIPS},
}

Statistical-Computational Tradeoffs for Density Estimation

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

NeurIPS 2024

arXiv Bib

@inproceedings{aamand2024densitytradoffs,
  title = {Statistical-Computational Tradeoffs for Density Estimation},
  author = {Aamand, Anders and Andoni, Alexandr and Chen, Justin Y. and Indyk, Piotr and Narayanan, Shyam and Silwal, Sandeep and Xu, Haike},
  booktitle = {NeurIPS},
  year = {2024},
}

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

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

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

Improved Frequency Estimation Algorithms with and without Predictions

Anders Aamand, Justin Y. Chen, Huy Lê Nguyễn, Sandeep Silwal, and Ali Vakilian

NeurIPS 2023 (Spotlight Award)

arXiv Bib

@inproceedings{aamand2023freqestprediction,
  title = {Improved Frequency Estimation Algorithms with and without Predictions},
  author = {Aamand, Anders and Chen, Justin Y. and Nguyễn, Huy Lê and Silwal, Sandeep and Vakilian, Ali},
  booktitle = {NeurIPS},
  year = {2023},
}

Constant Approximation for Individual Preference Stable Clustering

Anders Aamand, Justin Y. Chen, Allen Liu, Sandeep Silwal, Pattara Sukprasert, Ali Vakilian, and Fred Zhang

NeurIPS 2023 (Spotlight Award)

arXiv Bib

@inproceedings{aamand2023ipstable,
  title = {Constant Approximation for Individual Preference Stable Clustering},
  author = {Aamand, Anders and Chen, Justin Y. and Liu, Allen and Silwal, Sandeep and Sukprasert, Pattara and Vakilian, Ali and Zhang, Fred},
  booktitle = {NeurIPS},
  year = {2023},
}

Data Structures for Density Estimation

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

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

SODA 2023

PDF Bib

@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

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},
}

Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks

Anders Aamand, Justin Y. Chen, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Nicholas Schiefer, Sandeep Silwal, and Tal Wagner

NeurIPS 2022

arXiv Bib

@inproceedings{aamand2022wltestgnn,
  title = {Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks},
  author = {Aamand, Anders and Chen, Justin Y. and Indyk, Piotr and Narayanan, Shyam and Rubinfeld, Ronitt and Schiefer, Nicholas and Silwal, Sandeep and Wagner, Tal},
  booktitle = {NeurIPS},
  year = {2022},
}

Faster Fundamental Graph Algorithms via Learned Predictions

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

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

NeurIPS 2020 (Oral Award)

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},
}