I'm a 4th 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, CLIMB, 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 competition and other strategic interactions. Recently, I've been particularly interested in how algorithmic decisions made by digital platforms interact with the marketplace of consumers, producers, and competing 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 virtually at Microsoft Research Redmond with Suriya Gunasekar and Ilya Razenshteyn. In the summer of 2023, I interned at Microsoft Research New England with Nicole Immorlica and Brendan Lucier.

Here's my CV and my contact info.

Papers

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

Preprints

• Incentivizing High-Quality Content in Online Recommender Systems
  Xinyan Hu*, Meena Jagadeesan*, Michael I. Jordan, and Jacob Steinhardt.
  [arXiv]
  

Publications

• Improved Bayes Risk Can Yield Reduced Social Welfare Under Competition
  Meena Jagadeesan, Michael I. Jordan, Jacob Steinhardt, and Nika Haghtalab.
  NeurIPS 2023.
  [arXiv]
  


• Supply-Side Equilibria in Recommender Systems
  Meena Jagadeesan, Nikhil Garg, and Jacob Steinhardt.
  NeurIPS 2023.
  [arXiv] [slides]
  


• 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.
  [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.
  [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]

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]

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]