Michael (Miki) Elad (Hebrew: מיכאל אלעד; born December 10, 1963 in Haifa, Israel) is an Israeli computer scientist and a professor of Computer Science at the Technion – Israel Institute of Technology. Associated with the Technion and Stanford University, his academic work spans engineering, computer science, mathematics, and statistics, with connections to Arie Feuer and Michal Aharon. His work includes contributions in the fields of sparse representations and generative AI, and the deployment of these ideas to algorithms and applications in signal processing, image processing, and machine learning, including topics such as Sparse Representations, K-SVD, Image Super-Resolution, and Diffusion Models.

Michael Elad received his B.Sc. (1986), M.Sc. (1988), and D.Sc. (1997) in Electrical Engineering from the Technion - Israel Institute of Technology. His master's thesis, supervised by Prof. David Malah, focused on video compression algorithms, and his doctoral dissertation on super-resolution algorithms for image sequences was supervised by Prof. Arie Feuer. Between 1997 and 2001, he conducted industrial research at Hewlett-Packard Laboratories (HP Labs Israel) and Jigami. From 2001 to 2003, Elad held a research associate position at Stanford University, working closely with Prof. Gene Golub (CS-Stanford), Prof. Peyman Milanfar (EE-UCSC), and Prof. David Donoho (Statistics-Stanford).

In 2003, Elad joined the faculty of the Computer Science Department at the Technion as a senior lecturer in a tenure-track position. He was granted tenure and promoted to associate professor in 2007, and was promoted to full professor in 2010. His fields of interest are signal processing, image processing, and machine learning, with his research focusing on inverse problems, sparse representations, deep learning, and generative models in artificial intelligence. In 2010, his book "Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing" was published by Springer.

Throughout his academic career, Elad has engaged in editorial activities for scientific journals, serving as associate editor for IEEE Transactions on Image Processing (2007–2011), SIAM Imaging Sciences – SIIMS (2010–2015), IEEE Transactions on Information Theory (2011–2014), and Applied Computational Harmonic Analysis (2012–2015), as well as senior editor for IEEE Signal Processing Letters (2012–2014). From 2016 to 2021, he served as Editor-in-Chief of SIAM Imaging Sciences (SIIMS), the central platform for publications on applied mathematics for image processing applications. He was elected an IEEE Fellow in 2012 and a SIAM Fellow in 2018. Elad won the Weizmann Prize for Research in the Exact Sciences in 2021, and in 2024 he won the Rothschild Prize and was elected a member of the Israel Academy of Sciences and Humanities.^[8]

Elad works in the fields of signal processing, image processing, and machine learning, specializing in particular on inverse problems, sparse representations, and generative AI. He has authored hundreds of technical publications in these fields. Among these, he is the creator of the K-SVD algorithm, together with Michal Aharon and Bruckstein, and he is also the author of the 2010 book "Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing".^[1] In 2017, Elad and his PhD student Yaniv Romano created a specialized MOOC on sparse representation theory, given under edX.

During the years 2015–2018, Elad headed the Rothschild-Technion Program for Excellence. This is an undergraduate program at the Technion, meant for exceptional students with emphasis on tailored and challenging study tracks.^[2]

In 2018, Elad became a SIAM Fellow.^[3] In 2024, he won the Rothschild Prize in Engineering and became a member of the Israel Academy of Sciences and Humanities.^[4,5]