Junyao Zhao

Office: Bâtiment Sophie Germain, 4053   8 Place Aurélie Nemours   75013 Paris Email: junyao.zhao at irif.fr

Hi! I am an FSMP postdoctoral fellow at IRIF (CNRS & Université Paris Cité), hosted by Adrian Vladu. I did my Ph.D. in computer science at Stanford University, where I was very fortunate to be advised by Aviad Rubinstein. Before that, I obtained an M.Sc. with distinction in computer science from ETH Zürich and a B.Eng. in software engineering from Tongji University.

My current research focuses on algorithmic game theory, submodular optimization, and online learning. Specific topics include: beyond-worst-case analysis, communication complexity, simplicity vs. efficiency in algorithmic mechanism design, and strategic robustness in online learning.

Publications

• From No-Regret to Strategically Robust Learning in Repeated Auctions
  Junyao Zhao
  [arXiv]
• Fixed-Parameter Tractable Submodular Maximization over a Matroid
  Shamisa Nematollahi, Adrian Vladu, Junyao Zhao
  ITCS 2026, [arXiv]
• Universal Online Contention Resolution with Preselected Order
  Junyao Zhao
  ICALP 2025, [arXiv]
• Strategizing against No-Regret Learners in First-Price Auctions
  Aviad Rubinstein, Junyao Zhao
  EC 2024, [arXiv]
• The Power of Menus in Contract Design
  Guru Guruganesh, Jon Schneider, Joshua Wang, Junyao Zhao
  EC 2023, [arXiv]
• Multi-Channel Auction Design in the Autobidding World
  Gagan Aggarwal, Andres Perlroth, Junyao Zhao
  EC 2023, [arXiv]
• Beyond Worst-Case Budget-Feasible Mechanism Design
  Aviad Rubinstein, Junyao Zhao
  ITCS 2023, [arXiv]
• Maximizing Non-Monotone Submodular Functions over Small Subsets: Beyond 1/2-Approximation
  Aviad Rubinstein, Junyao Zhao
  ICALP 2022, [arXiv]
• Budget-Smoothed Analysis for Submodular Maximization
  Aviad Rubinstein, Junyao Zhao
  ITCS 2022, [arXiv]
• Cardinality Constrained Submodular Maximization for Random Streams
  Paul Liu, Aviad Rubinstein, Jan Vondrák, Junyao Zhao
  NeurIPS 2021, [arXiv]
• Randomized Communication Complexity of Randomized Auctions
  Aviad Rubinstein, Junyao Zhao
  STOC 2021, [arXiv]
  Invited to SIGecom Exchanges November 2021, Invited to HALG 2022
• Exponential Communication Separations between Notions of Selfishness
  Aviad Rubinstein, Raghuvansh R. Saxena, Clayton Thomas, S. Matthew Weinberg, Junyao Zhao
  STOC 2021, [arXiv]
• Robust Maximization of Non-Submodular Objectives
  Ilija Bogunovic, Junyao Zhao, Volkan Cevher
  AISTATS 2018, [arXiv]
• Improving Optimization-Based Approximate Inference by Clamping Variables
  Junyao Zhao, Josip Djolonga, Sebastian Tschiatschek, Andreas Krause
  UAI 2017, [proceedings]
  Oral Presentation

Teaching

I served as a teaching assistant for the following classes:

• Design and Analysis of Algorithms, Stanford University, Winter 2024
• Topics in Intractability: Unfulfilled Algorithmic Fantasies, Stanford University, Winter 2022
• The Practice of Theory Research, Stanford University, Winter 2020
• Machine Learning, ETH Zürich, Autumn 2016

Industry

During my Ph.D., I spent two summers as a research intern in the Market Algorithms team at Google Research in Mountain View. When I was an undergraduate, I interned at Microsoft Research Asia in Beijing and Morgan Stanley in Shanghai.

Service

I am serving or have served as a PC member for EC (2026), WINE (2024), and as a reviewer for SICOMP (2025), STOC (2026), FOCS (2023, 2024), SODA (2022, 2024, 2026), AAAI (2025, 2026), ICML (2024), NeurIPS (2023, 2025), and ICLR (2023).

I served as an organizer of Stanford Theory Lunch during 2021-2022.