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Ehsan Elhamifar

Ehsan Elhamifar, PhD

Assistant Professor
Khoury College of Computer Sciences
Electrical and Computer Engineering, College of Engineering (Affiliated)
Northeastern University

Email: eelhami [at] ccs [dot] neu [dot] edu
Office: 310E West Village Hall (WVH)

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Ehsan Elhamifar is an Assistant Professor in the Khoury College of Computer Sciences and is the director of the Mathematical Data Science (MCADS) Lab at the 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 (EECS) department at the University of California, Berkeley. Prof. Elhamifar obtained his PhD from the Electrical and Computer Engineering (ECE) department at the Johns Hopkins University. He obtained two Masters degrees, one in Electrical Engineering from Sharif University of Technology in Iran and another in Applied Mathematics and Statistics from 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 graph theory 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.


  • Dr. Elhamifar's team has received a $3M Award from DARPA to develop intelligent AI-AR task assistants. Read more here.

  • Two papers on Weakly-Supervised Action Segmentation and Zero-Shot Human-Object Interaction Recognition are accepted to ICCV 2021.

  • Two papers on Unsupervised Action Segmentation in Instructional Data (Oral Presentation) and Human Trajectory Prediction are accepted to CVPR 2021.

  • Congratulations to my PhD student Dat Huynh for receiving the J.P. Morgan PhD Fellowship Award.

  • New paper on Compositional Zero-Shot Learning is accepted to NeurIPS 2020. The code is also released.

  • The code of the ECCV20 paper on Self-Supervised Multi-Task Procedure Learning is released.

  • New paper on Procedure Completion by Learning from Partial Summaries is accepted (oral presentation) to BMVC 2020.

  • New paper on Self-Supervised Multi-Task Procedure Learning from Instructional Videos is accepted to ECCV 2020.

  • THREE papers, including an oral presentation, are accepted to CVPR 2020.

  • Dr. Elhamifar is organizing the “Workshop on Learning from Instructional Videos” in CVPR 2020 in Seattle, Washington. In addition to invited speakers, we are accepting regular paper submissions (best paper award sponsored by MIT-IBM Watson AI Lab) and extended abstracts that will be presented in the workshop.

  • The ProceL Dataset for instructional video understanding and procedure learning is released.

  • New paper on Deep Supervised Subset Selection with Applications to Learning from Instructions accepted to NeurIPS 2019.

  • New paper on Unsupervised Procedure Learning via Joint Dynamic Summarization accepted for Oral Presentation at ICCV 2019.

  • Gave a talk on Dynamic Subset Selection and Applications to Procedure Learning in the CVPR tutorial on Recent Advances in Visual Data Summarization.

  • New paper on Sequential Facility Location: Approximate Submodularity and Greedy Algorithm accepted to ICML 2019.

  • New paper on “High-Rank Matrix Completion with Side Information” accepted to AAAI 2018.

  • Dr. Elhamifar served as a Senior Program Committee of AAAI 2018.

  • Dr. Elhamifar received the CISE Career Research Initiation Initiative Award from the National Science Foundation (NSF).

  • Prospective PhD Students: I am always looking for strong PhD students who are excited about doing research in machine learning, computer vision and optimization. If you are interested in joining my Lab, please send me an email with the subject “PhD Application to Northeastern” and attach your CV.