Prathamesh Dharangutte

I am a third 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 *.

Preprints

• Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Time
  with Vladimir Braverman, Shreyas Pai, Vihan Shah and Chen Wang.

Conference Papers

• Learning-augmented Maximum Independent Set
  with Vladimir Braverman, Vihan Shah and Chen Wang.
  International Conference on Approximation Algorithms for Combinatorial Optimization Problems (APPROX 2024).
  

• Metric Clustering and MST with Strong and Weak Distance Oracles
  with MohammadHossein Bateni, Rajesh Jayaram and Chen Wang.
  Conference on Learning Theory (COLT 2024).
  

• 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 Papers

• Differentially Private Range Queries with Correlated Input Perturbation
  with Jie Gao, Ruobin Gong and Guanyang Wang.
  Theory and Practice of Differential Privacy (TPDP 2024).
  

• HeartInsightify: Interpreting Longitudinal Heart Rate Data for Health Insights through Conformal Clustering*
  Prathamesh Dharangutte, Zongxing Xie, Jie Gao, Elinor Schoenfeld, Yindong Hua, and Fan Ye.
  International Workshop on Biomedical and Health Informatics (BHI 2023).
  

• An Energy-Based View of Graph Neural Networks*
  John Y. Shin and Prathamesh Dharangutte.
  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)