Michael Elad

Michael Elad
Michael Elad 2017
Born (1963-12-10) 10 December 1963
Haifa, Israel
Nationality Israel
Alma mater Technion
Known for Sparse Representations, K-SVD, Image Super-Resolution
Scientific career
Fields Engineering, Computer Science, Mathematics, Statistics
Institutions Technion
Stanford University
Doctoral advisor Arie Feuer
Doctoral students Jeremias Sulam, Yaniv Romano, Vardan Papyan, Raja Giryes, Boaz Ophir, Javier Turek, Tomer Peleg, Amir Adler, Idan Ram, Joseph Shtok, Ron Rubinstein, Matan Protter, Michal Aharon

Michael Elad (born December 10, 1963) is a professor of Computer-Science at the Technion, Israel Institute of Technology. His work includes fundamental contributions in the field of sparse representations, and deployment of these ideas to algorithms and applications in signal processing, image processing and machine learning.

Academic biography

Michael Elad holds a B.Sc. (1986), M.Sc. (1988) and D.Sc. (1997) in Electrical-Engineering from the Technion, Israel Institute of Technology. His M.Sc. under the guidance of Prof. David Malah focused on video compression algorithms, and his D.Sc. centered around super-resolution algorithms for image sequences, guided by Prof. Arie Feuer.

After several years (1997-2001) in industrial research in Hewlett-Packard Lab Israel and in Jigami, Michael took a research-associate position at Stanford University during the years 2001-2003, working closely with Prof. Gene Golub (CS-Stanford), Prof. Peyman Milanfar (EE-UCSC), and Prof. David L. Donoho (Statistics-Stanford).

Since 2003 Michael Elad holds a tenure-track faculty position in the Computer-Science department at the Technion. He was tenured and promoted to Associate Professorship in 2007, and promoted to full-professorship in 2010.

Research

Michael Elad works in the fields of signal processing and image processing, specializing in particular on inverse problems and sparse representations. The field of sparse representations introduces a universal dimensionality reduction model for data sources and signals based on "sparsity", along with various theoretical and practical tools for implementing it. Prof. Elad has authored hundreds of technical publications in this field, many of which have led to exceptional impact. Among these, he is the creator of the K-SVD algorithm,[1] together with Aharon and Bruckstein, and he is also the author of the 2010's book,[2] "Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing". In 2017, Prof. Elad and Yaniv Romano (his Ph.D. student) created a specialized MOOC on sparse representation theory, given under edX.

Professional Roles and Honors

Prof. Elad has served on several journals' editorial boards over the years:

Since January 2015 Prof. Elad is heading the Rothschild-Technion Program for Excellence. This is a flagship undergraduate program at the Technion, meant for exceptional students, with emphasis on tailored and challenging study tracks for each of the ~50 students enrolled, along with an exposure to research.

Michael is the recipient of the 2008 and 2015 Henri Taub Prize for academic excellence, the 2010 Hershel-Rich prize for innovation, and the 2017 Yanai prize for excellence in teaching. His 2009 SIAM Review paper[3] with Donoho and Bruckstein received the SIAG Imaging-Science Prize in 2014. Michael is an IEEE Fellow since 2012 (for contributions to sparsity and redundancy in image processing). He was awarded the prestigious ERC advanced grant during the years 2013-2018.

Prof. Elad appeared in the highly cited researchers list for the years 2015, 2016 and 2017, published by Clarivate Analytics (formerly Thompson-Reuters). These lists include the ~3500 world’s most influential minds in science, covering various disciplines, from Immunology and Agriculture, through Chemistry and Physics, all the way to Computer Sciences and Engineering.

He was named a SIAM Fellow in 2018.[4]

References

  1. ↑ Aharon, M.; Elad, M.; Bruckstein, A.M. (2006), "The K-SVD: An Algorithm for Designing of Overcomplete Dictionaries for Sparse Representation" (PDF), IEEE Transactions on Signal Processing, 11 (54): 4311–4322 .
  2. ↑ Elad, Michael (2010), Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing .
  3. ↑ Bruckstein, A.M.; Donoho, D.L.; Elad, M. (2009), "From Sparse Solutions of Systems of Equations to Sparse Modeling of Signals and Images" (PDF), SIAM Review, 2 (51): 34–81 .
  4. ↑ "SIAM Announces Class of 2018 Fellows", SIAM News, March 29, 2018
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