Syamantak Kumar

I am a second year PhD student studying computer science at UT Austin advised by Prof. Purnamrita Sarkar and Prof. Kevin Tian . Broadly, my research interests lie at the intersection of statistics and machine learning. I am interested in designing machine learning algorithms with provable guarantees of convergence and correctness. I also want to work on quantifying the computational and statistical aspects of state-of-the-art deep learning methods. I deeply enjoy studying the applications of high-dimensional statistics, optimization and probability theory to real-world problems. Previously, I was an undergraduate student at the Computer Science and Engineering Department, IIT Bombay .

I previously worked with Prof. Suyash Awate on Medical Imaging and with Prof. Preethi Jyothi on NLP for Code-switching at IIT Bombay. I have also worked with Prof. Thomas Deserno on Medical Informatics and Imaging, back in 2018.

Email  /  CV  /  Github  /  Google Scholar  /  LinkedIn

• [Sept 2023]   Our paper "Streaming PCA for Markovian Data" accepted as a Spotlight (Top ~3%) at NeurIPS 2023.
• [Aug 2023]   Started PhD CS at UT Austin.
• [Aug 2021]   Started MS CS at UT Austin.
• [Jan 2021]   Our paper "From Machine Translation to Code-Switching : Generating High-Quality Code-Switched Text" accepted in ACL-IJCNLP 2021.
• [July 2020]   Joined Google, Bangalore as a software engineer in the Google Maps team.
• [Jun 2019]   Working at Google, Bangalore as a software engineering intern.
• [Feb 2019]   Our paper "A comparison of open source libraries ready for 3D reconstruction of wounds" got accepted in SPIE Medical Imaging 2019.

We propose a nearly-optimal streaming algorithm for performing PCA under Markovian dependence among the samples.

In this work, we adapt a state-of-the-art neural machine translation model to generate Hindi-English code-switched sentences starting from monolingual Hindi sentences.

We propose an android application for real-time three dimensional scanning of surface wounds to aid effective diagnosis of wounds remotely and present a comparative study of open-source libraries available for performing this task.

I worked at Google, Bangalore with the Maps team on a model for predicting the correctness of an edit made by a user on Maps. This model is used to ensure that data displayed on Google Maps is true to the accuracy it claims.

Used Generative Adversarial Networks (GANs) to generate adversarial examples for keyword spotting systems, improving their robustness.

Designed an algorithm for fully-sequential sampling in a Probably-Approximately-Correct (PAC) setting to determine top-K players in a tournament, given pariwise preferences

Implemented an AI for playing Chinese Checkers using the Minimax algorithm with alpha-beta pruning. (Implemented in Racket)