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 interned 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 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 recent work characterizes how content creator competition in recommender systems can impact supply-side diversity, how competition between model-providers can induce non-monotonic scaling laws, and how an algorithmic platform can leverage market power to shape users.
I've also investigated the interplay between ML models and competitive pressures in other domains such as strategic classification, matching markets with transfers, and language model agents at test-time.
Papers
(* denotes equal contribution, α-β denotes alphabetical ordering)Preprints
- Impact of Decentralized Learning on Player Utilities in Stackelberg Games
(α-β) Kate Donahue, Nicole Immorlica, Meena Jagadeesan, Brendan Lucier, and Aleksandrs Slivkins.
To be presented at ESIF-AIML 2024.
[arXiv] - Feedback Loops With Language Models Drive In-Context Reward Hacking
Alexander Pan, Erik Jones, Meena Jagadeesan, and Jacob Steinhardt.
[arXiv] - 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.
[arXiv] - Incentivizing High-Quality Content in Online Recommender Systems
Xinyan Hu*, Meena Jagadeesan*, Michael I. Jordan, and Jacob Steinhardt.
[arXiv]
- 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] - 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]
- 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.
[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.
[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.
[paper]
- 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.
[paper] - Mobius Polynomials of Face Posets of Convex Polytopes
Meena Jagadeesan and Susan Durst.
Communications in Algebra, 2016.
[paper]
- 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]
Theses
- The Performance of Johnson-Lindenstrauss Transforms: Beyond the Classical Framework
Meena Jagadeesan.
Undergraduate thesis, 2020. Awarded Hoopes Prize.
[thesis]
Selected Awards
- Open Philanthropy AI Fellowship (2021-2025)
- PD Soros Fellowship for New Americans (2020-2022)
- Berkeley Fellowship (2020-2023)
- CRA Outstanding Undergraduate Researcher Award (2020)
- Siebel Scholar (2019-2020)
- Barry Goldwater Scholar (2018-2020)
- Intel STS 2nd Place Prize in Basic Research (2016)
Contact Info
Email: mjagadeesan [at] berkeley [dot] edu[Twitter] [Google Scholar] [LinkedIn]