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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 and summarization for massive complex datasets, high-dimensional multi-manifold data analysis, learning nonlinear dynamic models, deep learning
  • Computer Vision: motion and activity segmentation, object recognition and detection, video summarization, active learning for visual data
  • Optimization: sparse and low-rank recovery algorithms, convex programming, submodular 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 College of Computer and Information Science and is affiliated with the Department of Electrical and Computer Engineering at Northeastern University. Previously, he was a postdoctoral scholar in the Electrical Engineering and Computer Sciences Department at the University of California, Berkeley. He obtained his PhD in Electrical and Computer Engineering from the Johns Hopkins University in 2012. Dr. Elhamifar obtained two Master’s degrees, one in Electrical Engineering from Sharif University of Technology in Iran in 2006 and another in Applied Mathematics and Statistics from the Johns Hopkins University in 2010. He was a visiting researcher at Stanford University, University of Minnesota and Duke University for several months during 2011 and 2012.

Prof. Elhamifar’s research areas are machine learning, computer vision, optimization and algorithms. He is broadly interested in developing efficient, robust and provable algorithms that can address challenges of complex and large-scale high-dimensional data. He works on applications of these tools in computer vision and robotics. Specifically, he uses tools from convex geometry and analysis, optimization, sparse and low-rank modeling, high-dimensional statistics and graph theory to develop algorithms and theory and applies them to solve real-world problems, including motion and activity segmentation in videos, object detection and recognition, video summarization, active learning and more.