Meena Jagadeesan



PhD Student

UC Berkeley

I'm a 1st year PhD student in Computer Science at UC Berkeley. In May 2020, I graduated with an A.B. and S.M. from Harvard, where I studied computer science, math, and statistics. This summer, I interned at Microsoft Research in the Machine Learning and Optimization Group.

I'm broadly interested in algorithms and machine learning, and I currently focus on theoretical foundations and societal impact. I'm supported by a PD Soros Fellowship for New Americans and a Berkeley Fellowship.

Here's my CV and my contact info.

Publications and Preprints

  • Fairness in ad auctions through inverse proportionality
    Shuchi Chawla* and Meena Jagadeesan*.
    Manuscript under submission.
    [arXiv]

  • 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] [video]

  • 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

Related: see press / media appearances.

Contact Info

Email: mjagadeesan [at] berkeley [dot] edu

LinkedIn: meena.jagadeesan