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Ehsan Elhamifar

Assistant Professor

Affiliate Faculty with the Department of Electrical and Computer Engineering


Office Location

440 Huntington Avenue
310E West Village H
Boston, MA 02115

Mailing Address

Northeastern University
ATTN: Ehsan Elhamifar, 202 WVH
360 Huntington Avenue
Boston, MA 02115

Research Interests

  • Machine Learning: subset selection, zero and few shot learning, manifold clustering, high-rank matrix completion, nonlinear dynamical models, deep neural networks
  • Computer Vision: procedure learning, video summarization, large-scale multi-label recognition, motion and activity segmentation, active learning for visual data
  • Optimization: sparse and low-rank recovery, structured submodular maximization, convex and non-convex optimization


  • PhD in Electrical and Computer Engineering, Johns Hopkins University
  • MS in Applied Mathematics and Statistics, Johns Hopkins University
  • MS in Electrical Engineering, Sharif University of Technology


Ehsan Elhamifar is an Assistant Professor in the Khoury College of Computer Sciences and is the director of the Mathematical, Computational and Applied Data Science (MCADS) Lab at Northeastern University. He is affiliated with the Electrical and Computer Engineering Department at Northeastern. Prof. Elhamifar is a recipient of the DARPA Young Faculty Award and the NSF CISE Career Research Initiation Initiative Award. Previously, he was a postdoctoral scholar in the Electrical Engineering and Computer Science department at UC Berkeley. Prof. Elhamifar obtained his PhD from the Electrical and Computer Engineering department at the Johns Hopkins University.

Prof. Elhamifar’s research areas are machine learning, computer vision and optimization. He is interested in developing scalable, robust and provable algorithms that can address challenges of complex and massive high-dimensional data. He works on applications of these tools in computer vision and robotics among others. Specifically, he uses tools from convex, nonconvex and submodular optimization, sparse and low-rank modeling, deep learning, high-dimensional statistics and graphical models to develop algorithms and theory and applies them to solve real-world challenging problems, including big data summarization, procedure learning from instructional data, large-scale recognition with small labeled data and active learning for visual data.