Shahin Shahrampour
Assistant Professor,
Mechanical and Industrial Engineering
Affiliated Faculty,
Electrical and Computer Engineering
Office
- 269 SN
- 617.373.5750
Lab
- 009 FR
Research Focus
- Optimization and Control - Multi-Agent Systems - Machine Learning - Reinforcement Learning
About
Shahin Shahrampour is currently an Assistant Professor in the Department of Mechanical & Industrial Engineering at Northeastern University. He was previously an Assistant Professor in the Departments of Industrial & Systems Engineering and Electrical & Computer Engineering (by courtesy) at Texas A&M University (TAMU) in 2018-2021. Before joining TAMU, he was a Postdoctoral Fellow in the School of Engineering and Applied Sciences at Harvard University. Prior to that, he received the Ph.D. degree in Electrical and Systems Engineering, the M.A. degree in Statistics (The Wharton School), and the M.S.E. degree in Electrical Engineering, all from the University of Pennsylvania, in 2015, 2014, and 2012, respectively.
Education
- PhD, Electrical & Systems Engineering, University of Pennsylvania – 2015
- AM, Statistics, The Wharton School – 2014
- MS, Electrical Engineering, University of Pennsylvania – 2012
- BS, Electrical Engineering, Sharif University of Technology – 2009
Honors & Awards
- NSF CAREER Award, 2025
- Best Paper Award in IEEE Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2022
-
TEES Engineering Genesis Award for Multidisciplinary Research, Texas A&M University, 2020
Research Overview
- Optimization and Control - Multi-Agent Systems - Machine Learning - Reinforcement Learning
Selected Research Projects
- Foundations of Scalable, Fast, and Online Decentralized Manifold Optimization in Multi-Agent Networks
- – Principal Investigator, National Science Foundation (NSF)
- Consensus and Distributed Optimization in Non-Convex Environments with Applications to Networked Machine Learning
- – Principal Investigator, National Science Foundation (NSF)
- Collaborative Online Optimization for Efficient Model-Based Learning
- – Principal Investigator, National Science Foundation (NSF)
- Real-Time Learning and Control of Stochastic Nanostructure Growth Processes Through in situ Dynamic Imaging
- – co-Principal Investigator, National Science Foundation (NSF)
Selected Publications
- E. Sahinoglu, Y. Sun, and S. Shahrampour “Finite-Time Analysis of Stochastic Nonconvex Nonsmooth Optimization on the Riemannian Manifolds”, Advances in Neural Information Processing Systems (NeurIPS), Dec. 2025
- Y. Sun, T. Liu, R. Zhou, PR Kumar, and S. Shahrampour “Provably Fast Convergence of Independent Natural Policy Gradient for Markov Potential Games”, Advances in Neural Information Processing Systems (NeurIPS), Dec. 2023
- Y. Wang, Y. Ding, and S. Shahrampour “TAKDE: Temporal Adaptive Kernel Density Estimator for Real-Time Dynamic Density Estimation”, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), Nov. 2023
- S. Chen, A. Garcia, M. Hong, and S. Shahrampour “Decentralized Riemannian Gradient Descent on the Stiefel Manifold”, International Conference on Machine Learning (ICML), 2021
- S. Shahrampour and A. Jadbabaie, “Distributed Online Optimization in Dynamic Environments Using Mirror Descent”, IEEE Transactions on Automatic Control (TAC), March 2018
Jun 23, 2025
NSF CAREER Award To Advance Multi-Agent Network Optimization Foundations
MIE Assistant Professor Shahin Shahrampour was awarded a $515,000 NSF CAREER grant for “Foundations of Scalable, Fast, and Online Decentralized Manifold Optimization in Multi-Agent Networks.” The project takes a substantial step toward the development and adoption of decentralized manifold optimization in large-scale, multi-agent optimization. Manifold optimization is instrumental in control and engineering applications.
Jun 12, 2023
Advancing Distributed Optimization for Non-Convex Problems
MIE Assistant Professor Shahin Shahrampour received a $500,000 NSF grant, in collaboration with Texas A&M University, to address “Consensus and Distributed Optimization in Non-Convex Environments with Applications to Networked Machine Learning.” The project will transform the understanding of consensus and coordination in non-convex environments, and will include educational components to introduce distributed optimization as a practical tool for the next generation of engineers.
Jul 12, 2022
Best Paper Award at IEEE ICASSP 2022
MIE Assistant Professor Shahin Shahrampour and his collaborators from Vanderbilt, INRIA, and Telecom Paris received the best paper award at the 2022 IEEE International Conference on Acoustics, Speech and Signal […]
Jul 09, 2021
New Faculty Spotlight: Shahin Shahrampour
Shahin Shahrampour joins the Mechanical and Industrial Engineering department in July 2021 as an Assistant Professor.