Shahin Shahrampour

About

Shahin Shahrampour is currently an Assistant Professor in the Department of Mechanical & Industrial Engineering at Northeastern University. He previously served as an Assistant Professor in the Departments of Industrial & Systems Engineering and Electrical & Computer Engineering (by courtesy) at Texas A&M University from 2018 to 2021. Before his tenure at Texas A&M, he was a Postdoctoral Fellow in the School of Engineering and Applied Sciences at Harvard University. He holds a Ph.D. in Electrical and Systems Engineering from the University of Pennsylvania, along with a Master’s degree in Statistics from The Wharton School and a Master’s degree in Electrical Engineering from the University of Pennsylvania. His educational background also includes a Bachelor’s degree in Electrical Engineering from Sharif University of Technology. His research focuses on optimization and control, multi-agent systems, machine learning, and reinforcement learning. He has been recognized with several awards, including the NSF CAREER Award in 2025, the Martin W. Essigmann Outstanding Teaching Award in 2026, and the Best Paper Award at IEEE ICASSP in 2022. His work involves developing scalable, fast, and online decentralized manifold optimization in multi-agent networks, as well as advancing distributed optimization in non-convex environments with applications to networked machine learning. Shahrampour’s research aims to contribute to control and engineering applications through innovative…

Research topics

• Computer Science
• Artificial Intelligence
• Mathematics
• Pure mathematics
• Combinatorics
• Statistics
• Mathematical analysis
• Computer Security
• Geometry
• Discrete mathematics

Selected publications

• On the Local Linear Rate of Consensus on the Stiefel Manifold

  IEEE Transactions on Automatic Control · 2023-11-06 · 10 citations

  articleOpen accessSenior author

  Coordinated group behavior arising from purely local interactions has been successfully modeled with distributed consensus-seeking dynamics, where the local behavior is aimed at minimizing the disagreement with neighboring peers. However, it has been recently shown that when constrained by a manifold geometry, distributed consensus-seeking dynamics may ultimately fail to converge to a global consensus state. In this article, we study discrete-time consensus-seeking dynamics on the Stiefel manifo…

  PublisherOA PDFDOI

• Decentralized Riemannian Gradient Descent on the Stiefel Manifold

  International Conference on Machine Learning · 2024 · 9 citations

  Senior authorCorresponding

  We consider a distributed non-convex optimization where a network of agents aims at minimizing a global function over the Stiefel manifold. The global function is represented as a finite sum of smooth local functions, where each local function is associated with one agent and agents communicate with each other over an undirected connected graph. The problem is non-convex as local functions are possibly non-convex (but smooth) and the Steifel manifold is a non-convex set. We present a decentraliz…

• Regret Analysis of Distributed Online LQR Control for Unknown LTI Systems

  IEEE Transactions on Automatic Control · 2023-07-27 · 5 citations

  articleSenior author

  Online optimization has recently opened avenues to study optimal control for time-varying cost functions that are unknown in advance. Inspired by this line of research, we study the distributed online linear quadratic regulator (LQR) problem for linear time-invariant (LTI) systems with unknown dynamics. Consider a multi-agent network where each agent is modeled as a LTI system. The network has a global <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xli…

  PublisherDOI

• A sparse expansion for deep Gaussian processes

  IISE Transactions · 2023-05-05 · 5 citations

  articleSenior author

  In this work, we use Deep Gaussian Processes (DGPs) as statistical surrogates for stochastic processes with complex distributions. Conventional inferential methods for DGP models can suffer from high computational complexity, as they require large-scale operations with kernel matrices for training and inference. In this work, we propose an efficient scheme for accurate inference and efficient training based on a range of Gaussian Processes, called the Tensor Markov Gaussian Processes (TMGP). We…

  PublisherDOI

• On the Stability Analysis of Open Federated Learning Systems

  2023-05-31 · 5 citations

  articleSenior author

  We consider the open federated learning (FL) systems, where clients may join and/or leave the system during the FL process. Given the variability of the number of present clients, convergence to a fixed model cannot be guaranteed in open systems. Instead, we resort to a new performance metric that we term the stability of open FL systems, which quantifies the magnitude of the learned model in open systems. Under the assumption that local clients’ functions are strongly convex and smooth, we theo…

  PublisherDOI

Recent grants

• Collaborative Online Optimization for Efficient Model-Based Learning

  NSF · $500k · 2019–2021

Frequent coauthors

• Ali Jadbabaie

  36 shared

• T. T. Chang

  19 shared

• Youbang Sun

  Northeastern University

  17 shared

• Alexander Rakhlin

  16 shared

• Yinsong Wang

  16 shared

• Vahid Tarokh

  14 shared

• Shixiang Chen

  Chang'an University

  8 shared

• Alfredo García

  7 shared

Awards & honors

• Martin W. Essigmann Outstanding Teaching Award, 2026
• NSF CAREER Award, 2025
• Best Paper Award in IEEE Conference on Acoustics, Speech, an…
• TEES Engineering Genesis Award for Multidisciplinary Researc…

Similar researchers at Northeastern University

• Nian X. Sunh-75
• Yongmin Liuh-63
• Hanumant Singhh-56