About: I am an Assistant Professor in the Department of Computer Science (CS)  at Wayne State University (WSU). Previously, I was a Postdoctoral Associate in the Department of Electrical & Computer Engineering (ECE), University of Minnesota (UMN) jointly advised by Prof. Mingyi Hong and Prof. Jia (Kevin) Liu (Assistant Professor of Electrical & Computer Engineering (ECE), The Ohio State University (OSU)). Prior to joining UMN, I was a member of the Sensor Fusion Lab headed by Prof. Pramod K. Varshney. I defended my Ph.D. thesis in 2019 from the Department of Electrical Engineering & Computer Science (EECS), Syracuse University. 

I am looking for self-motivated Ph.D. students with an interest in optimization. Interested candidates with a strong background in mathematics, signal processing, statistics, or computer science are encouraged to contact me via email at khanduri.prashant [at] wayne.edu with a copy of their CV and transcripts.

Research Interests: Optimization and Machine Learning, Federated Learning, Robust Optimization, Statistical Learning, Statistical Signal Processing, and Information Theory.

Recent News

• [Aug 2023] Our survey paper "An Introduction to Bi-Level Optimization: Foundations and Applications in Signal Processing and Machine Learning," is now available on arXiv.

• [July 2023] Two papers accepted by Asilomar, 2023.

  • "FedAvg for Minimizing Polyak-Lojasiewicz Objectives: The Interpolation Regime," Asilomar, 2023.

  • "Stochastic Perturbation Based Smoothing for Linearly Constrained Bilevel Optimization," Asilomar (Invited), 2023.

• [June 2023] One paper accepted by AdvML-Frontiers, ICML Workshop, 2023., and one by MICCAI, 2023.

  • "FocalUNETR: A Focal Transformer for Boundary-aware Segmentation of CT Images," MICCAI, 2023.

  • "Proximal Compositional Optimization for Distributionally Robust Learning," AdvML-Frontiers, ICML Workshop, 2023.

• [June 2023] Delivered an invited talk on "Linearly Constrained Bilevel Optimization: An Implicit Gradient Approach," at the SIAM Conference on Optimization (OP23) in Seattle, WA.

• [April 2023] Three papers including one joint first-author paper accepted by ICML, 2023.

  • "Linearly Constrained Bilevel Optimization: A Smoothed Implicit Gradient Approach," ICML, 2023.

  • "FedAvg Converges to Zero Training Loss Linearly for Overparameterized Multi-Layer Neural Networks," ICML, 2023.

  • "Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning," ICML, 2023.

• [February 2023] Paper titled "An Implicit Gradient Method for Constrained Bilevel Problems using Barrier Approximation," accepted by ICASSP, 2023.

• [January 2023] Our work "Fairness-aware Vision Transformer via Debiased Self-Attention," is now available on arXiv.

• [December 2022] Paper titled "DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization," accepted by IEEE INFOCOM, 2023.

• [November 2022] Paper titled "With a Little Help from My Friend: Server-Aided Federated Learning with Partial Client Participation," accepted by FL-NeurIPS, 2022.

• [October 2022] Delivered an invited talk on "Unconstrained and Constrained Bilevel Optimization: An Implicit Gradient Approach" at the Informs Annual Meeting in Indianapolis, IN. 

• [September 2022] Delivered an invited talk on "Bilevel Optimization: Algorithms and Guarantees" at the Great Lakes Section of SIAM (GLSIAM) annual meeting hosted by Wayne State University, MI. 

• [August 2022] Joined the Department of Computer Science (CS) at Wayne State University as Tenure Track Assistant Professor.

• [July 2022] Paper titled "INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks," accepted by MobiHoc, 2022.

• [May 2022] Two papers accepted by ICML, 2022.

  • "Anarchic Federated Learning," ICML, 2022. (Long Presentation)

  • "Revisiting and Advancing Fast Adversarial Training Through the Lens of Bi-Level Optimization," ICML, 2022.

• [January 2022] One first-author paper accepted by ICLR, 2022, and one paper accepted by ICASSP, 2022.

  • "Decentralized Learning for Overparameterized Problems: A Multi-Agent Kernel Approximation Approach," ICLR, 2022.

  • "An Implicit Gradient-Type Method for Linearly Constrained Bilevel Problems," ICASSP, 2022.

• [November 2021] Paper titled "Byzantine Resilient Non-Convex SCSG with Distributed Batch Gradient Computations," accepted by IEEE Trans. Signal Inf. Process. Netw., 2021.

• [October 2021] Two first-author papers accepted by NeurIPS, 2021.

  •  "A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum,” NeurIPS, 2021.

  • "STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning," NeurIPS, 2021.