Meena Jagadeesan

PhD Student

UC Berkeley

I'm a 3rd 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 and the Theory Group. I'm supported by an Open Philanthropy AI Fellowship, a PD Soros Fellowship for New Americans, and a Berkeley Fellowship.

My research centers around the theoretical foundations of machine learning and algorithmic decision-making, including in the presence of economic interactions. Recently, I've been particularly interested in how algorithmic decisions made by digital platforms interact with the marketplace of consumers, producers, and other platforms.

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. In the summer of 2020, I interned at Microsoft Research in the Machine Learning and Optimization Group.

Here's my CV and my contact info.


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

  • Supply-Side Equilibria in Recommender Systems
    Meena Jagadeesan, Nikhil Garg, and Jacob Steinhardt.

  • Can Probabilistic Feedback Drive Misalignment in Online Platforms?
    (α-β) Jessica Dai, Bailey Flanigan, Nika Haghtalab, Meena Jagadeesan, and Chara Podimata.
    [coming soon]
  • 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. (Preliminary version 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.
    [paper] [slides] [talk at MIT]

  • Individual Fairness in Advertising Auctions through Inverse Proportionality
    (α-β) Shuchi Chawla and Meena Jagadeesan.
    ITCS 2022. (Preliminary version 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.
    Accepted to Journal of the ACM.
    [paper] [poster] [slides]

  • Alternative Microfoundations for Strategic Classification
    Meena Jagadeesan, Celestine Mendler-Dünner, and Moritz Hardt.
    ICML 2021.
    [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. (Preliminary version 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. (Preliminary version 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

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