Meena Jagadeesan

PhD Student

UC Berkeley

I'm a rising 5th year PhD student in Computer Science at UC Berkeley. My advisors are Michael I. Jordan and Jacob Steinhardt, and I’m affiliated with the Berkeley AI Research Lab. I'm supported by an Open Philanthropy AI Fellowship and a PD Soros Fellowship.

Before coming to Berkeley, I received an A.B. and S.M. in computer science, math, and statistics at Harvard, where I was advised by Jelani Nelson. I've interned at Microsoft Research Redmond with Suriya Gunasekar and Ilya Razenshteyn (summer 2020) and at Microsoft Research New England with Nicole Immorlica and Brendan Lucier (summers 2023 and 2024).

Here's my CV and my contact info.

Research Overview

My current research focuses on machine learning in marketplaces and other multi-agent environments (e.g., content recommendation marketplaces, emerging ecosystems built on foundation models, etc.). I investigate how interactions between ML models and competitive pressures can disrupt model performance, agent behavior, and market structure. For example, some of my work studies how competition between content creators impacts supply-side diversity, how competition between model-providers induces non-monotonic scaling laws, and how competition between repeatedly interacting agents affects learning dynamics. I also study related problems in domains such as LLM agents at test-time, strategic classification, and fairness in ad auctions.


(* denotes equal contribution, α-β denotes alphabetical ordering)

  • Accounting for AI and Users Shaping One Another: The Role of Mathematical Models
    (α-β) Sarah Dean, Evan Dong, Meena Jagadeesan, and Liu Leqi.
    Position Paper. (Presented under a different title at AAAI 2024 Workshop on Recommendation Ecosystems.)

  • Incentivizing High-Quality Content in Online Recommender Systems
    Xinyan Hu*, Meena Jagadeesan*, Michael I. Jordan, and Jacob Steinhardt.
  • Impact of Decentralized Learning on Player Utilities in Stackelberg Games
    (α-β) Kate Donahue, Nicole Immorlica, Meena Jagadeesan, Brendan Lucier, and Aleksandrs Slivkins.
    ICML 2024. (To be presented at ESIF-AIML 2024 and EC 2024 Workshop on Foundation Models and Game Theory.)

  • Feedback Loops With Language Models Drive In-Context Reward Hacking
    Alexander Pan, Erik Jones, Meena Jagadeesan, and Jacob Steinhardt.
    ICML 2024.

  • Clickbait vs. Quality: How Engagement-Based Optimization Shapes the Content Landscape in Online Platforms
    (α-β) Nicole Immorlica, Meena Jagadeesan, and Brendan Lucier.
    The Web Conference (WWW) 2024.
    [paper] [talk] [slides] [poster]

  • Can Probabilistic Feedback Drive User Impacts in Online Platforms?
    (α-β) Jessica Dai, Bailey Flanigan, Nika Haghtalab, Meena Jagadeesan, and Chara Podimata
    AISTATS 2024.
    [paper] [poster]

  • Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition
    Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, and Nika Haghtalab.
    NeurIPS 2023. (To be presented at ESIF-AIML 2024.)
    [paper] [talk] [slides] [poster]

  • Supply-Side Equilibria in Recommender Systems
    Meena Jagadeesan, Nikhil Garg, and Jacob Steinhardt.
    NeurIPS 2023. (To be presented at FORC 2024.)
    [paper] [talk] [slides] [poster]

  • Competition, Alignment, and Equilibria in Digital Marketplaces
    Meena Jagadeesan, Michael I. Jordan, and Nika Haghtalab.
    AAAI 2023.
    [paper] [slides] [poster]

  • Performative Power
    (α-β) Moritz Hardt, Meena Jagadeesan, and Celestine Mendler-Dünner.
    NeurIPS 2022.
    [paper] [talk] [slides] [poster] [MAIEI blog]

  • Regret Minimization with Performative Feedback
    Meena Jagadeesan, Tijana Zrnic, and Celestine Mendler-Dünner.
    ICML 2022. (Presented at FORC 2022.)
    [paper] [slides] [talk]

  • Inductive Bias of Multi-Channel Linear Convolutional Networks with Bounded Weight Norm
    Meena Jagadeesan, Ilya Razenshteyn, and Suriya Gunasekar.
    COLT 2022. (Presented at ICML 2021 Workshop on Overparameterization.)
    [paper] [slides] [talk at MIT]

  • Individual Fairness in Advertising Auctions through Inverse Proportionality
    (α-β) Shuchi Chawla and Meena Jagadeesan.
    ITCS 2022. (Presented at FORC 2021.)
    [paper] [talk] [slides]

  • Learning Equilibria in Matching Markets from Bandit Feedback
    Meena Jagadeesan*, Alexander Wei*, Yixin Wang, Michael I. Jordan, and Jacob Steinhardt.
    NeurIPS 2021. Spotlight Presentation.
    Full version in Journal of the ACM.
    [paper] [poster] [slides]

  • Alternative Microfoundations for Strategic Classification
    Meena Jagadeesan, Celestine Mendler-Dünner, and Moritz Hardt.
    ICML 2021. (Presented at NeurIPS 2021 Workshop on Learning in Presence of Strategic Behavior.)
    [paper] [talk] [slides] [poster]

  • Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine
    Subarna Chatterjee, Meena Jagadeesan, Wilson Qin, and Stratos Idreos.
    VLDB 2021.

  • Multi-Category Fairness in Sponsored Search Auctions
    Christina Ilvento*, Meena Jagadeesan*, and Shuchi Chawla.
    ACM FAT* 2020. (Presented at EC 2019 Workshop on Mechanism Design for Social Good.)
    [paper] [slides] [talk]

  • Individual Fairness in Pipelines
    (α-β) Cynthia Dwork, Christina Ilvento, and Meena Jagadeesan.
    FORC 2020.
    [paper] [full version] [slides from reading group]

  • Understanding Sparse JL for Feature Hashing
    Meena Jagadeesan.
    NeurIPS 2019. Oral Presentation.
    [paper] [full version] [talk] [slides] [coverage]

  • Simple Analysis of Sparse, Sign-Consistent JL
    Meena Jagadeesan.
    RANDOM 2019.
    [paper] [full version] [slides]

  • Varying the Number of Signals in Matching Markets
    Meena Jagadeesan* and Alexander Wei*.
    WINE 2018. (Presented at EC 2018 Workshop on Frontiers of Market Design.)
    [paper] [full version]

  • Dyson's partition ranks and their multiplicative extensions
    (α-β) Elaine Hou and Meena Jagadeesan.
    The Ramanujan Journal, 2018.

  • Mobius Polynomials of Face Posets of Convex Polytopes
    Meena Jagadeesan and Susan Durst.
    Communications in Algebra, 2016.
Short papers
  • From Worst-Case to Average-Case Analysis: Accurate Latency Predictions for Key-Value Storage Engines
    Meena Jagadeesan* and Garrett Tanzer*.
    SIGMOD 2020 (2-Page Extended Abstract). Winner of SIGMOD SRC.
  • [short paper]


  • The Performance of Johnson-Lindenstrauss Transforms: Beyond the Classical Framework
    Meena Jagadeesan.
    Undergraduate thesis, 2020. Awarded Hoopes Prize.

Selected Awards

Contact Info

Email: mjagadeesan [at] berkeley [dot] edu

[Google Scholar] [LinkedIn]