Meena Jagadeesan
I'm an incoming Assistant Professor at the University of Pennsylvania in the Computer and Information Science Department, starting in July. I'll be a member of the ASSET Center for trustworthy AI.
Before joining Penn, I'm spending this year as a SAIL postdoc fellow at Stanford, hosted by Tatsu Hashimoto and Sanmi Koyejo. I received my PhD in CS from UC Berkeley, where I was advised by Michael I. Jordan and Jacob Steinhardt. I've spent three summers at Microsoft Research, and I went to Harvard for undergrad where I was advised by Jelani Nelson.
I study machine learning ecosystems where ML models (such as LLMs) interact with other ML models, humans, and model-providers. I aim to steer how multi-agent interactions shape performance trends, safety, and societal outcomes. For example, we investigate scaling laws under competition, feedback loops with LLM agents, and data-driven barriers to entry.
Here's my CV and my contact info.
Before joining Penn, I'm spending this year as a SAIL postdoc fellow at Stanford, hosted by Tatsu Hashimoto and Sanmi Koyejo. I received my PhD in CS from UC Berkeley, where I was advised by Michael I. Jordan and Jacob Steinhardt. I've spent three summers at Microsoft Research, and I went to Harvard for undergrad where I was advised by Jelani Nelson.
I study machine learning ecosystems where ML models (such as LLMs) interact with other ML models, humans, and model-providers. I aim to steer how multi-agent interactions shape performance trends, safety, and societal outcomes. For example, we investigate scaling laws under competition, feedback loops with LLM agents, and data-driven barriers to entry.
Here's my CV and my contact info.
Group
I'm excited to work with students who have a strong technical skillset (either theoretical, empirical, or a mixture of the two) and are interested in studying modern machine learning ecosystems. See this information if you're interested in joining my group.Papers
(* denotes equal contribution, α-β denotes alphabetical ordering)Manuscripts
- Power and Limitations of Aggregation in Compound AI Systems
Nivasini Ananthakrishnan and Meena Jagadeesan.
[arXiv]
- Breaking Algorithmic Collusion in Human-AI Ecosystems
Natalie Collina, Eshwar Ram Arunachaleswaran, and Meena Jagadeesan.
To be presented at ESIF-AIML 2026 and the NeurIPS 2025 Workshop on Algorithmic Collective Action.
[arXiv]
- Generative AI Supply Chains: When do Technology Improvements lead to Disintermediation?
(α-β) S. Nageeb Ali, Nicole Immorlica, Meena Jagadeesan, and Brendan Lucier.
[arXiv] - Generative AI Supply Chains: When do Technology Improvements lead to Disintermediation?
- Incentivizing High-Quality Content in Online Recommender Systems
Xinyan Hu*, Meena Jagadeesan*, Michael I. Jordan, and Jacob Steinhardt.
FORC 2026.
[arXiv] - Safety versus Performance: How Multi-Objective Learning Reduces Barriers to Market Entry
Meena Jagadeesan, Michael I. Jordan, and Jacob Steinhardt.
PNAS, 2025.
[paper] [slides] - Accounting for AI and Users Shaping One Another: The Role of Mathematical Models
(α-β) Sarah Dean, Evan Dong, Meena Jagadeesan, and Liu Leqi.
TMLR, 2025. Survey Paper. (Presented under a different title at AAAI 2024 Workshop on Recommendation Ecosystems.)
[paper] - 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. (Presented at ESIF-AIML 2024 and EC 2024 Workshop on Foundation Models and Game Theory.)
[paper] [slides] [poster] - Feedback Loops With Language Models Drive In-Context Reward Hacking
Alexander Pan, Erik Jones, Meena Jagadeesan, and Jacob Steinhardt.
ICML 2024.
[paper] [poster] - 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. (Presented at ESIF-AIML 2024.)
[paper] [talk] [slides] [poster] - Supply-Side Equilibria in Recommender Systems
Meena Jagadeesan, Nikhil Garg, and Jacob Steinhardt.
NeurIPS 2023. (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.
[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
- Steering Machine Learning Ecosystems of Interacting Agents
Meena Jagadeesan.
PhD thesis from the CS Department at UC Berkeley, 2025.
[thesis] - The Performance of Johnson-Lindenstrauss Transforms: Beyond the Classical Framework
Meena Jagadeesan.
Undergraduate thesis from Harvard University, 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)
Joining my group
Prospective PhD students: please apply to the Penn CIS PhD program and list my name there. Unfortunately, I don't have the capacity to respond to individual inquiries outside of the formal application process.Prospective postdocs: if you are interested in working with me, please email me with a brief description of your research interests and two of your representative papers.
Current Penn PhD students: if you're interested in collaborating, feel free to email me.
Contact info
Email: meenaj [at] cis [dot] upenn [dot] edu[Google Scholar] [LinkedIn] [Twitter]