About Me

I am a Postdoctoral Research Scientist at Columbia University working with Rachel Cummings. I recently completed my Ph.D. in Computer Science at Purdue University where I was fortunate to work with Elena Grigorescu and Jeremiah Blocki. During my Ph.D. studies I was a Research Intern at Analog Devices and a Student Researcher at Google Research.
I am broadly interested in working on problems that fall under the areas of data privacy, and theoretical computer science. My dissertation focused on designing efficient differentially-private algorithms in resource-constrained settings, where the resource can be time/space.

Research

Publications (authors listed in alphabetical order).

  • How to Make Your Approximation Algorithm Private: A Black-Box Differentially-Private Transformation for Tunable Approximation Algorithms of Functions with Low Sensitivity [arxiv ][RANDOM 2023] [Poster presentation at TPDP 2023]
    Jeremiah Blocki, Elena Grigorescu, Tamalika Mukherjee, and Samson Zhou
  • Differentially Private L2-Heavy Hitters in the Sliding Window Model [arxiv] [ICLR 2023] (selected for spotlight presentation)
    Jeremiah Blocki, Seunghoon Lee, Tamalika Mukherjee, and Samson Zhou
  • Privately Estimating Graph Parameters in Sublinear time [arxiv] [ICALP 2022][Poster presentation at TPDP 2022]
    Jeremiah Blocki, Elena Grigorescu, and Tamalika Mukherjee
  • Differentially Private Sublinear-Time Clustering [arxiv] [ISIT 2021]
    Jeremiah Blocki, Elena Grigorescu, and Tamalika Mukherjee
  • P4-free Partition and Cover numbers [ eprint ] [ITC 2021]
    Alexander R. Block, Simina Branzei, Hemanta K. Maji, Himanshi Mehta, Tamalika Mukherjee, and Hai H. Nguyen
  • Lattice Reduction for Modules, or How to Reduce ModuleSVP to ModuleSVP [eprint] [CRYPTO 2020]
    Tamalika Mukherjee and Noah Stephens-Davidowitz
  • Estimating Gaps in Martingales and Applications to Coin-Tossing: Constructions and Hardness [arxiv] [TCC 2019]
    Hamidreza Amini Khorasgani, Hemanta K. Maji, and Tamalika Mukherjee

Drafts/Preprints (authors listed in alphabetical order).

  • Differentially Private Clustering in Data Streams [arxiv ][Poster presentation at TAGML 2023 and TPDP 2023]
    Alessandro Epasto, Tamalika Mukherjee, and Peilin Zhong

Honors and Awards

Presentations

How to Make Your Approximation Algorithm Private: A Black-Box Differentially-Private Transformation for Tunable Approximation Algorithms of Functions with Low Sensitivity Differentially Private Sublinear Algorithms Privately Estimating Graph Parameters in Sublinear-time Differentially Private Sublinear-Time Clustering Lattice Reduction for Modules, or How to Reduce ModuleSVP to ModuleSVP Estimating Gaps in Martingales and Applications to Coin-Tossing
  • Talk at Karlsruhe Institute of Technology (Germany), Ruhr-University Bochum (Germany), EPFL (Switzerland) (December, 2019).
  • Poster presentation at STOC in Phoenix, AZ, thanks to TCS Women (June, 2019).
  • Poster presentation at Midwest Theory Day hosted by Purdue University, West Lafayette, IN (April, 2019).

Teaching

  • Spring 2021: Head TA for CS381 - Introduction to Analysis of Algorithms
  • Fall 2020: Graduate TA for CS381 - Introduction to Analysis of Algorithms
  • Spring 2019, Fall 2018, Fall 2017: Graduate TA for CS355 - Introduction to Cryptography
  • Spring 2017: Course Coordinator for CS180- Problem-Solving and Object Oriented Programming in Java
  • Fall 2016: Graduate TA for CS180- Problem-Solving and Object Oriented Programming in Java

Engagement

I have organized the Theory Reading Group at Purdue University from 2021-2022. I have also served as the President of the Computer Science Graduate Student Association at Purdue University for the academic terms of 2018 and 2019.