Michael Elad is a distinguished professor and leading authority in computational imaging and representation theory. He currently serves as a Full Professor in the Computer Science Department at the Technion - Israel Institute of Technology where he has held a permanent faculty position since 2003. Professor Elad received his B.Sc. (1986) M.Sc. (1988) and D.Sc. (1997) in Electrical Engineering from the Technion followed by a research associate position at Stanford University from 2001 to 2003. His career trajectory demonstrates a consistent commitment to advancing the theoretical foundations of signal processing while maintaining strong connections to practical applications.
Professor Elad's groundbreaking contributions to sparse representation theory have fundamentally transformed the fields of signal and image processing with his seminal work on the K-SVD algorithm becoming a cornerstone methodology for dictionary learning. His 2010 book Sparse and Redundant Representations From Theory to Applications in Signal and Image Processing established a comprehensive theoretical framework that has been widely adopted by researchers worldwide. His research on inverse problems and multi-layer convolutional sparse coding provided critical theoretical foundations that later informed developments in deep learning architectures. More recently his work on diffusion models and generative AI has positioned him at the forefront of the latest advances in machine learning for image processing applications.
Beyond his individual research achievements Professor Elad has played a pivotal role in shaping his field through his service as Editor-in-Chief of the SIAM Journal on Imaging Sciences since 2016 where he has guided the publication of cutting-edge research for nearly a decade. His recognition as a SIAM Fellow in 2018 and recipient of the prestigious Rothschild Prize in Engineering in 2024 along with his election to the Israel Academy of Sciences and Humanities attest to his profound impact on the mathematical sciences community. Professor Elad continues to advance the frontiers of computational imaging through his active research group at the Technion where he mentors the next generation of scholars while pursuing innovative approaches to image restoration and generative modeling. His current research focuses on developing theoretically grounded methods for real-world image processing challenges ensuring that his work maintains both mathematical rigor and practical relevance.
