I'm a 2nd year PhD student in Computer Science at UC Berkeley. My advisors are Moritz Hardt, Michael I. Jordan, and Jacob Steinhardt, and I’m affiliated with the Berkeley AI Research Lab and the Theory Group. I’m broadly interested in the theoretical foundations of machine learning and algorithms, including its intersections with economics. I'm supported by an Open Philanthropy AI Fellowship, a PD Soros Fellowship for New Americans, and a Berkeley Fellowship.

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

Preprints

• Learning Equilibria in Matching Markets from Bandit Feedback
  Meena Jagadeesan*, Alexander Wei*, Yixin Wang, Michael I. Jordan, and Jacob Steinhardt.
  [arXiv]


• Inductive Bias of Multi-Channel Linear Convolutional Networks with Bounded Weight Norm
  Meena Jagadeesan, Ilya Razenshteyn, and Suriya Gunasekar.
  Preliminary version at ICML 2021 Workshop on Overparameterization.
  [arXiv] [talk at MIT]


• Individual Fairness in Advertising Auctions through Inverse Proportionality
  Shuchi Chawla* and Meena Jagadeesan*.
  Preliminary version at FORC 2021.
  [arXiv] [talk]

Publications

• Alternative Microfoundations for Strategic Classification
  Meena Jagadeesan, Celestine Mendler-Dünner, and Moritz Hardt.
  ICML 2021
  [paper] [slides] [poster]
  
  
• Cosine: A Cloud-Cost Optimized Self-Designing Key-Value Storage Engine
  Subarna Chatterjee, Meena Jagadeesan, Wilson Qin, and Stratos Idreos.
  VLDB 2022
  
  
• 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
  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
  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]

(* denotes alphabetical ordering)

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)

Related: see press / media appearances.

Contact Info

Email: mjagadeesan [at] berkeley [dot] edu

[Twitter] [Google Scholar] [LinkedIn]