is currently a Research Staff Member at IBM
Research AI, MIT-IBM Watson AI Lab, Cambridge, USA. Prior to joining IBM, he obtained his Ph.D in Electrical and Computer Engineering from
University of California, Riverside in 2018. His primary research interests span thevareas of computer vision, machine learning and multimedia.
In particular, his current focus is on developing semi, weakly, unsupervised algorithms for solving different vision problems.
His work has been published intop-tier conferences such as CVPR, ICCV, ECCV, MM as well as high impact journals such as TIP and TMM.
is an Assistant Professor in the College of Computer and Information Science (CCIS) and is
the director of the Mathematical, Computational and Applied Data Science (MCADS) Lab at the Northeastern
University. Prof. Elhamifar is a recipient of the DARPA Young Faculty Award and the NSF CISE Career
Research Initiation Award on the topic of Big Data Summarization. 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. Prof. Elhamifars research areas are machine learning, computer vision and optimization.
He is interested in developing scalable and robust algorithms that can address challenges of complex and massive
high-dimensional data. Specifically, he uses tools from convex, nonconvex and submodular optimization, sparse
and low-rank modeling, deep learning and high-dimensional statistics 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.
is a research scientist at Google AI in Zurich, working under Prof. Vittorio Ferrari. Before
joining Google, Michael was the head of AI at gifs.com, leading the efforts to automate video editing through
summarization and highlight detection. In 2017 he obtained a PhD from ETH Zurich for his thesis on Interest-Based
Video Summarization via Subset Selection, under the supervision of Prof. Luc Van Gool.
Michael has published several papers at venues such as CVPR, ICCV, ECCV, ICML and MM.
is a research scientist at Google, Seattle and a remote principal investigator at ICSI, Berkeley.
His research in machine learning and computer vision focuses on modeling algorithms and visual recognition. Before joining Google in 2019, he worked in Tencent and
was a tenure-track Assistant Professor at the University of Central Florida (UCF). He received an NSF CRII award in 2016 and an NSF BIGDATA award in 2017,
both of which were the first of their kinds ever granted to UCF. He is/was a (senior) area chair of NeurIPS 2019, ICCV 2019, ICML 2019, AISTATS 2019, AAAI 2020, and WACV 2018--2020.
He received his Ph.D. in 2015 at the University of Southern California, where the Viterbi Fellowship partially supported his work.