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.
Papers
(* denotes equal contribution, α-β denotes alphabetical ordering)Preprints
- Supply-Side Equilibria in Recommender Systems
Meena Jagadeesan, Nikhil Garg, and Jacob Steinhardt.
[arXiv] - Performative Power
(α-β) Moritz Hardt, Meena Jagadeesan, and Celestine Mendler-Dünner.
[arXiv]
- 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 (given to ~10% of accepted papers).
[paper] [poster] - 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 2022.
[paper]
- 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 (given to ~3% of accepted papers).
[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.
[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]