I work on the theoretical foundations of machine learning and algorithmic decision-making, including intersections with economics. Recently, I've been particularly interested in how algorithmic decisions made by digital platforms impact and are impacted by 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.
- Competition, Alignment, and Equilibria in Digital Marketplaces
Meena Jagadeesan, Michael I. Jordan, and Nika Haghtalab.
- Performative Power
(α-β) Moritz Hardt, Meena Jagadeesan, and Celestine Mendler-Dünner.
[paper] [slides] [talk] [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.
[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.
[paper] [talk] [slides] [poster]
- Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine
Subarna Chatterjee, Meena Jagadeesan, Wilson Qin, and Stratos Idreos.
- 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.
[paper] [full version] [slides from reading group]
- Understanding Sparse JL for Feature Hashing
NeurIPS 2019. Oral Presentation.
[paper] [full version] [talk] [slides] [coverage]
- Simple Analysis of Sparse, Sign-Consistent JL
[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.
- 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.
- The Performance of Johnson-Lindenstrauss Transforms: Beyond the Classical Framework
Undergraduate thesis, 2020. Awarded Hoopes Prize.
- 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)
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