I am a PhD student in Electrical Engineering at Stanford University [since September 2017] and am grateful to have been supported by a Stanford Graduate Fellowship [2017-2020].
I am advised by Tsachy (Itschak) Weissman and Greg Valiant, and have also had the singular pleasure of working with Mary Wootters. Before this, I received a B. Tech in EE from IIT-Bombay [also in 2017].
In the recent past, my work has focused on the computational complexity theory of statistical inference, and I visited the Simons Institute from Aug-Dec 2021 to participate in the CCSI program. In the less recent past, I have also worked in information and coding theory (spanning from theoretical to practical work).
Logspace Reducibility From Secret Leakage Planted Clique [arxiv]
Finding Planted Cliques in Sublinear Time [arxiv]
Jay Mardia, Hilal Asi, Kabir Aladin Chandrasekher
Concentration Inequalities for the Empirical Distribution of Discrete Distributions : Beyond the Method of Types [arxiv] [Information and Inference]
Jay Mardia, Jiantao Jiao, Ervin Tánczos, Robert D. Nowak, Tsachy Weissman
Repairing Multiple Failures for Scalar MDS Codes [arxiv] [IEEE Transactions on Information Theory] (Part of a line of work excellently presented in this [talk] by Mary Wootters)
Jay Mardia, Burak Bartan, Mary Wootters
Overcoming high nanopore basecaller error rates for DNA storage via basecaller-decoder integration and convolutional codes [biorxiv] [GitHub] [ICASSP 2020]
Shubham Chandak, Joachim Neu, Kedar Tatwawadi, Jay Mardia, Billy Lau, Matthew Kubit, Reyna Hulett, Peter Griffin, Mary Wootters, Tsachy Weissman, Hanlee Ji
Improved read/write cost tradeoff in DNA-based data storage using LDPC codes [biorxiv] [Allerton 2019] ([Slides], [poster], and [talk] by Shubham at ISMB / ECCB 2019)
Shubham Chandak, Kedar Tatwawadi, Billy Lau, Jay Mardia, Matthew Kubit, Joachim Neu, Peter Griffin, Mary Wootters, Tsachy Weissman, Hanlee Ji
Implementation and analysis of stabilizer codes in pyQuil [code] [report] (Spring 2019, CS269Q)
Shubham Chandak, Jay Mardia, Meltem Tolunay
Stabilizer codes form a large family of quantum error correcting codes that includes well-known codes such as the Shor code, Steane code, CSS codes and toric codes. In this work, we build a framework for encoding and decoding of general stabilizer codes on pyQuil and test specific single qubit codes with standard quantum noise models.
Visits: Simons Institute - Computational Complexity of Statistical Inference (Aug-Dec 2021) and EPFL / ETH Zurich - Swiss Winter School on Lower Bounds and Communication Complexity (Feb 2020)
Journal Reviewer: Journal of Machine Learning Research, Information and Inference, IEEE Communications Letters, IEEE Transactions on Information Theory
Conference Reviewer: STOC, ITW, SODA, ICASSP, NeurIPS ITML, ISIT
Teaching at Stanford: TA for Randomized Algorithms and Probabilistic Analysis (CS 265) (Autumn 2020-21), Information Theory (EE 276) (Winter 2020-21), and Information-theoretic Lower Bounds in Data Science (EE 378C) (Spring 2020-21)
Grading: Grader for Scaling Blockchains EE 374 (Spring 2019-2020)