Anamay Chaturvedi

(he/him/his)

PhD Student

Anamay Chaturvedi
  • MS in Mathematics, National University of Singapore – Singapore
  • BS in Mathematics, Indian Institute of Science – India

Anamay Chaturvedi is a doctoral student at the Khoury College of Computer Sciences at Northeastern University, advised by Huy Lê Nguyen. His field of study is theoretical computer science and he is primarily interested in differential privacy, algorithms and data structures with a focus on optimization, and more generally in theoretical machine learning.

Anamay obtained his undergraduate degree with a major in mathematics at the Indian Institute of Science (IISc), Bangalore, before moving to the National University of Singapore (NUS) to complete his masters in mathematics. During this time he gradually transitioned from pure math to theoretical computer science. After completing his masters degree and before joining Northeastern, he worked for a few months as a research assistant with Professor Jonathan Scarlett at the School of Computing, NUS, during which time they worked primarily on graphical model selection and other topics in machine learning.

  • Differentially Private Decomposable Submodular Maximization

    Citation: Anamay Chaturvedi, Huy Lê Nguyễn, and Lydia Zakynthinou. Differentially Private Decomposable Submodular Maximization. In Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.

  • Learning Gaussian Graphical Models via Multiplicative Weights

    Citation: Chaturvedi, A. ; Scarlett, J.. (2020). Learning Gaussian Graphical Models via Multiplicative Weights. Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 108:1104-1114 Available from https://proceedings.mlr.press/v108/chaturvedi20a.html.

  • Differentially Private k-Means via Exponential Mechanism and Max Cover

    Citation: Nguyen, H. L., A. Chaturvedi, and E. Z. Xu. “Differentially Private K-Means via Exponential Mechanism and Max Cover”. Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 10, May 2021, pp. 9101-8, https://ojs.aaai.org/index.php/AAAI/article/view/17099.