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 *.

Conference Papers

• Hardness and Approximation Algorithms for Balanced Districting Problems
  with Jie Gao, Shang-En Huang and Fang-Yi Yu.
  Foundations of Responsible Computing (FORC 2025).
  

• Fully Dynamic Adversarially Robust Correlation Clustering in Polylogarithmic Update Time
  with Vladimir Braverman, Shreyas Pai, Vihan Shah and Chen Wang.
  Conference on Artificial Intelligence and Statistics (AISTATS 2025).
  

• Differentially Private Range Queries with Correlated Input Perturbation
  with Jie Gao, Ruobin Gong and Guanyang Wang.
  Conference on Artificial Intelligence and Statistics (AISTATS 2025).
  Short version in Theory and Practice of Differential Privacy (TPDP 2024).
  

• 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

• 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)