Prathamesh Dharangutte
prathamesh.d (at) rutgers (dot) edu
I am a second year PhD student in Computer Science at Rutgers University. I am fortunate to be advised by Prof. Jie Gao and be a part of the Theory group.
My research interests lie (very) broadly in the fields of algorithms and learning theory. My current research involves topics in differential privacy.
Before joining Rutgers, I completed my Master’s in CS at NYU Tandon School of Engineering during which I was lucky to have the opportunity to work with Prof. Christopher Musco. I obtained my Bachelor’s in Computer Engineering from PICT, Pune after which I worked at HSBC, Pune as a software engineer.
Apart from research, I enjoy video games and traveling. I also have a new found fondness for hiking.
Publications
Author names appear in alphabetical ordering. Exceptions are marked with *.
Preprint
-
Differentially Private Range Queries with Correlated Input Perturbation
with Jie Gao, Ruobin Gong and Guanyang Wang. -
Metric Clustering and MST with Strong and Weak Distance Oracles
with MohammadHossein Bateni, Rajesh Jayaram and Chen Wang.
Conference Papers
-
Integer Subspace Differential Privacy
with Jie Gao, Ruobin Gong and Fang-Yi Yu.
AAAI Conference on Artificial Intelligence (AAAI 2023). -
A Tight Analysis of Hutchinson’s Diagonal Estimator
with Christopher Musco.
SIAM Symposium on Simplicity in Algorithms (SOSA 2023). -
Dynamic trace estimation
with Christopher Musco.
Conference on Neural Information Processing Systems (NeurIPS) 2021. -
Graph Learning for Inverse Landscape Genetics
with Christopher Musco.
AAAI Conference on Artificial Intelligence (AAAI 2021).
Short version in AI for Earth Sciences Workshop (NeurIPS 2020).
Workshop Paper
- An Energy-Based View of Graph Neural Networks*
with John Y. Shin.
Energy-Based Models Workshop (EBM-ICLR 2021).
Teaching
TA: Rutgers CS205 Intro to Discrete Structures I (Spring 2023)
TA: Rutgers CS461 Machine Learning Principles (Fall 2022)
TA: Rutgers CS210 Data Management for Data Science (Spring 2022)
TA: Rutgers CS501 Mathematical Foundations of Data Science (Fall 2021)
TA: NYU CS-UY 4563 Introduction to Machine Learning (Spring 2020)