Contact:
jlazarsfeld [at] gmail.com
About:
I am a postdoctoral researcher at SUTD in Singapore
working with Georgios Piliouras and Antonios Varvitsiotis.
I received my PhD in computer science at Yale in May 2024,
where I was advised by James Aspnes. Previously, I was also a
visitor at IST Austria in 2023 and summer 2024, where
I was hosted by Dan Alistarh and Krish Chatterjee.
My research interests are broadly at the intersection
of theoretical computer science and machine learning, including:
- online learning, game theory, decision making, and optimization
- distributed algorithms, opinion dynamics,
and computation in multi-agent settings
- differentially private statistics and optimization
News:
-
September 2024: started as a postdoc at SUTD in Singapore!
-
June 2024: attended PODC in Nantes, and the GAIMSS summer school
and workshop in Metz.
-
April/May 2024: successfully defended my thesis and graduated from Yale!
-
December 2023: attended OPT2023 @ NeurIPS
to present our work on decentralized learning dynamics.
-
August 2023: presented at the
Yale Theory Student Seminar.
-
August 2023: gave a short talk and poster at
WOLA 2023.
-
Winter/Spring/Summer 2023: visited
IST Austria, hosted by Dan Alistarh.
-
Summer 2022: interned with the privacy-preserving machine learning
research group at Meta in NYC, where I collaborated with Sen Yuan
and Huanyu Zhang.
Publications:
-
John Lazarsfeld and Dan Alistarh
Simple Opinion Dynamics for No-Regret Learning
July 2024 -- preprint; preliminary version appeared in OPT workshop at NeurIPS 2023
[ArXiv]
-
Dan Alistarh, Krishnendu Chatterjee, Mehrdad Karrabi, and John Lazarsfeld
Game Dynamics and Equilibrium Computation in the Population Protocol Model
PODC 2024
[ArXiv]
-
Talley Amir, James Aspnes, Petra Berenbrink, Felix Biermeier, Christopher Hahn,
Dominik Kaaser, and John Lazarsfeld
Fast Convergence of k-Opinion Undecided State Dynamics in the
Population Protocol Model
PODC 2023
[ArXiv]
[Proceedings]
-
John Lazarsfeld, Aaron Johnson, and Emmanuel Adeniran
Differentially Private Maximal Information Coefficients
ICML 2022
[ArXiv]
[Proceedings]
-
John Lazarsfeld and Aaron Johnson
Consistency of the Maximal Information Coefficient Estimator
July 2021 -- corrects an error in previous work of Reshef et al. (JMLR, 2016)
[ArXiv]
-
Talley Amir, James Aspnes, and John Lazarsfeld
Approximate Majority With Catalytic Inputs
OPODIS 2020
[ArXiv]
[Proceedings]
-
John Lazarsfeld, Jonathan RodrÃguez, Mert Erden, Yuelin Liu, and Lenore Cowen
Majority Vote Cascading: A Semi-Supervised Framework for Improving
Protein Function Prediction
ACM BCB 2019 -
Companion poster named Best Poster
[Conf. Proceedings]
[Code and Data]
Teaching:
-
Yale CPSC 569: Randomized Algorithms
Graduate Teaching Fellow
Spring 2024
-
Yale CPSC 565: Distributed Algorithms
Graduate Teaching Fellow
Fall 2023, Fall 2022
-
Yale CPSC 365: Introduction to Algorithms
Graduate Teaching Fellow
Spring 2022, Spring 2021
-
Yale CPSC 202: Mathematical Tools for Computer Science
Graduate Teaching Fellow
Fall 2021, Fall 2020
-
Tufts COMP 160: Introduction to Algorithms
Graduate Teaching Assistant
Spring 2019; Summer 2019
Other:
Member of Yale CS Graduate Student Advisory Council (2022-2024)
Graduate Student Representative on Yale CS Climate & Diversity Committee (2020-2021)
Yale Franklin College Graduate Affiliate (2020-2024)
Last Updated: September 2024