Portrait of Jana Kulkarni

Jana Kulkarni

Researcher

About

Hi! I got my Ph.D. from the department of Computer Science at Duke University. My adviser was (is) Kamesh Munagala. I am now at Microsoft Research, Redmond.

I am interested in the design of algorithms with provable performance guarantees. In particular, I study resource allocation and scheduling problems, with constraints on fairness, energy minimization, strategyproofness, that arise in large scale distributed data centers. My research involves using concepts and techniques from the fields of approximation algorithms, online algorithms and game theory. I am also broadly interested in the intersection of online algorithms and online learning theory. Much of my work is theoretical, but I spend non-trivial amount of time converting my equations into code.

If you want to know more about my work, take a look at my papers or my research statement or these slides or the abstract of my thesis.

Projects

Publications

Videos

Other

Research Interests

  • Approximation algorithms
  • Online algorithms
  • Online learning
  • Game theory
  • Differential privacy
  • Data-analytics.

Professional Activities

  • Committee: MAPSP’17
  • Reviewer/subreviewer for : STOC, FOCS, SODA, JACM, SICOMP, etc.

Education

Selected Academic Awards

  • Outstanding PhD award, Duke CS, 2015
  • Selected For China Theory Week, 2014
  • Outstanding Prelim award, Duke, 2013
  • Outstanding Teaching Assistant award, Duke, 2012
  • Gold medal, Indian Institute of Science, 2010

Selected Invited Talks

  • Online Flow-Time Optimization. Simons Institute For Theory of Computing, Berkely. Sept’16.
  • A Market View of Multidimensional Scheduling. University of Wisconsin-Madison, April’16
  • Price of Anarchy Analysis Via Duality. MSR Theory Day, Redmond. March’16.
  • The Design of Scheduling Algorithms Using Equilibrium Principles. Washington University in St.Louis, March’16
  • Minimizing Flow-time on Unrelated Machines. University of Washington, Seattle. Oct’15
  • Dual-Fitting Framework For Non-Clairvoyant Scheduling. Workshop on Online Algorithms and Learning, Lorentz Center, NL. Nov’14
  • Dual-Fitting Framework For Non-Clairvoyant Scheduling. IISc, Bangalore, Sept 2014.
  • Non-Clairvoyant Scheduling To Minimize Weighted Flow-Time. China Theory Week, 2014.
  • Non-Clairvoyant Scheduling To Minimize Weighted Flow-Time. IBM T.J.Watson Research Center, NYC. Apr’14
  • Dual Fitting Framework for Scheduling and Routing Games. Google Research, NYC. Oct 14.

Preprints

(If you are curious about any of the results below, send me an email and I will send you a copy.)

  • Minimum Birkhoff-von Neumann Decompositionswith Euiwoong Lee, Mohit Singh (submitted)
  • Minimizing General Delay Costs on Unrelated Machineswith Nikhil Devanur (submitted)
  • New Results on Online Vector Schedulingwith Sungjin Im, Nat Kell, Debmalya Panigrahi, Maryam Shadloo (submitted)
  • Optimal Tradeoff Between Fairness and Efficiencywith Nikhil Devanur, Srikanth Kandula, Ishai Menache (submitted)

Internships and Visits