Contact:

About:

I am recent PhD graduate in computer science at Yale, where I was advised by James Aspnes. During summer 2024, I will be a postdoc at IST Austria working with Dan Alistarh and Krish Chatterjee. Starting fall 2024, I will be a postdoc at SUTD in Singapore working with Georgios Piliouras and Antonios Varvitsiotis.

My research interests are broadly at the intersection of algorithms, theoretical machine learning, and the foundations of data science, including:

• online learning, bandits, decision making, and games
• distributed algorithms, opinion dynamics, and computation in multi-agent settings
• differentially private statistics and optimization

News:

• June 2024: I will be attending 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: will be attending 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.
• During the Winter/Spring 2023 semester, I was a visitor at IST Austria hosted by Dan Alistarh.
• In Summer 2022, I interned with the privacy-preserving machine learning research group at Meta in NYC, where I collaborated with Sen Yuan and Huanyu Zhang.

Publications:

• Dan Alistarh, Krishnendu Chatterjee, Mehrdad Karrabi, and John Lazarsfeld
  Game Dynamics and Equilibrium Computation in the Population Protocol Model
  PODC 2024
  [ArXiv]
• John Lazarsfeld and Dan Alistarh
  The Power of Populations in Decentralized Bandits
  June 2023 -- preprint; preliminary version appeared in OPT workshop at NeurIPS 2023
  [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 Rodríguez, 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: