About: I am an Assistant Professor in the Department of Computer Science (CS)  at Wayne State University (WSU), MI. Previously, I was a Postdoctoral Associate in the Department of Electrical & Computer Engineering (ECE), University of Minnesota (UMN), MN, jointly advised by Prof. Mingyi Hong and Prof. Jia (Kevin) Liu (Assistant Professor of Electrical & Computer Engineering (ECE), The Ohio State University (OSU), OH). 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, NY. 

At this time, I am not actively recruiting new Ph.D. students. Thank you for your interest. Please feel free to check back in the future for updates.

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

Recent News

• [July 2025] Organized two sessions on "Recent Algorithmic Advances in Multiagent Optimization" and  "Advances in Bilevel Optimization: Algorithms and Applications" with Prof. Haibo Yang at the Rochester Institute of Technology, NY, and Prof. Mingyi Hong at the University of Minnesota, MN at the 2025 International Cconference in Continuous Optimization (ICCOPT) in University of Southern California, Los Angeles, CA.

• [July 2025] Two papers accepted by ECAI, 2025.

  • "GeoSAM: Fine-tuning SAM with Multi-Modal Prompts for Mobility Infrastructure Segmentation," ECAI, 2025.

  • "Automatic Calibration for Membership Inference Attack on Large Language Models," ECAI, 2025.

• [June 2025] Our work "Not All Tokens Are Meant to Be Forgotten," is now available on arXiv.

• [May 2025]  Our work "A Discretization Approach for Bilevel Optimization with Low-Dimensional and Non-Convex Lower-Level," is now available on arXiv.

• [April 2025] Our work "A Doubly Stochastically Perturbed Algorithm for Linearly Constrained Bilevel Optimization," is now available on arXiv.

• [March 2025] Paper titled  "Interpretability-Aware Vision Transformer," accepted by IJCNN, 2025.

• [November 2024] Paper titled "Linear Convergence of Decentralized FedAvg for PL Objectives: The Interpolation Regime," accepted by TMLR, 2024. 

• [October 2024] Two papers accepted by WACV, 2025.

  • "AutoProSAM: Automated Prompting SAM for 3D Multi-Organ Segmentation," WACV, 2025. 

  • "MulModSeg: Enhancing Unpaired Multi-Modal Medical Image Segmentation with Modality-Conditioned Text Embedding and Alternating Training," WACV, 2025.

• [July 2024] Paper titled  "Fairness-aware Vision Transformer via Debiased Self-Attention," accepted by ECCV, 2024.

• [July 2024] Paper titled "SHARE: A Distributed Learning Framework For Multivariate Time-Series Forecasting," accepted by IEEE SPAWC, 2024.

• [June 2024] Attended AIMACCS Workshop 2024 organized by AI Edge Institute at The Ohio State University, Columbus, OH. 

• [May 2024] Paper titled "Understanding Server-Assisted Federated Learning in the Presence of Incomplete Client Participation," accepted by ICML, 2024. 

• [April 2024] Paper titled "Toward Byzantine-robust Decentralized Federated Learning," accepted by ACM CCS 2024. 

• [March 2024] Delivered a talk on "Minimax problems with Coupled Constraints," at the 2024 INFORMS Optimization Society Conference in Houston, TX.

• [March 2024] Organized two sessions on "Exploring Frontiers of Bilevel Optimization" and  "Recent Advances in Bilevel Optimization" with Prof. Mingyi Hong at the University of Minnesota, MN, and Dr. Jeongyeol Kwon at the University of Wisconsin-Madison, WI at the 2024 INFORMS Optimization Society Conference in Houston, TX.

• [February 2024] Survey paper titled "An Introduction to Bi-Level Optimization: Foundations and Applications in Signal Processing and Machine Learning," accepted by IEEE Signal Process. Magazine, 2024.

• [November 2023] Our work "GeoSAM: Fine-Tuning SAM with Sparse and Dense Visual Prompting for Automated Segmentation of Mobility Infrastructure," is now available on arXiv.

• [August 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.