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Aryeh Kontorovich is a Professor in the Computer Science Department at Ben-Gurion University. His research primarily focuses on theoretical machine learning, with expertise in probability, statistics, Markov chains, and metric spaces. His research interests span theoretical machine learning, with particular emphasis on: Probability theory and concentration inequalities Statistical learning theory Markov chains and mixing time estimation Metric space learning Kernel methods Sample compression schemes Professor Kontorovich's recent publications (2021-2025) demonstrate a continued focus on theoretical foundations of machine learning. His work shows strong trends in statistical estimation for Markov processes, distribution learning, metric space analysis, and sample compression. Many papers explore the intersection of probability theory and machine learning, particularly examining concentration inequalities, minimax optimality, and theoretical guarantees for learning algorithms. His research consistently bridges abstract mathematical theory with practical machine learning applications. Scientific awards and recognitions: Distinguished contribution award at MLG 2007 for "A Universal Kernel for Learning Regular Languages" Professor Kontorovich has advised numerous students and collaborated extensively with researchers in theoretical machine learning. His work spans both theoretical foundations and practical applications, with significant contributions to understanding the mathematical limits of learning algorithms. While specific grant information isn't provided in the source material, his extensive publication record in top venues suggests successful funding for his research programs. He maintains active collaborations with researchers worldwide, including prominent names like L. Gottlieb, D. Berend, and S. Hanneke.

• Theoretical Machine Learning
• Probability
• Statistics
• +4 more

Dan Garber is an Associate Professor at the Faculty of Data and Decision Sciences at the Technion - Israel Institute of Technology. He joined the faculty in 2017 after completing his Ph.D. at the Technion (2015) and a year as a Research Assistant Professor at Toyota Technological Institute in Chicago (2016). His research bridges machine learning, continuous optimization, and algorithm design, focusing on developing efficient methods for high-dimensional optimization with applications in data science and machine learning. Education: B.Sc. in Computer Engineering (2010), Technion M.Sc. in Computer Science (2012), Technion Ph.D. in Computer Science (2015), Technion Garber leads the Technion Optimization Lab (TOL) , which specializes in foundational research on continuous optimization methodologies and their applications in engineering, medicine, and industry. His work emphasizes theoretical guarantees for scalable algorithms, including those used in large-scale data analysis and sequential decision-making systems. Funding & Awards: Israel Science Foundation grants (2018-2022, 2022-2026, ~$250,000 each) ERC Consolidator Grant (2025-2030, ~1.785M Euro) Best Student Paper Award at COLT Conference (2022) with Ben Kretzu Garber supervises graduate students in optimization and machine learning, including M.Sc. and Ph.D. candidates. His laboratory supports research projects and disseminates advancements through workshops, schools, and seminars. Contact: dangar@technion.ac.il | Office: Bloomfield 406

• Machine Learning
• Continuous Optimization
• Large-Scale Data Analysis
• +2 more

Michael Elad is a Professor of Computer Science at the Technion - Israel Institute of Technology, where he has held a permanent faculty position since 2003. He also holds a courtesy appointment in the Technion's Electrical & Computer Engineering Department. 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 (2001-2003). His educational background includes: B.Sc. in Electrical Engineering from the Technion (1986) M.Sc. in Electrical Engineering from the Technion (1988), focusing on video compression algorithms under Prof. David Malah D.Sc. in Electrical Engineering from the Technion (1997), focusing on super-resolution algorithms for image sequences under Prof. Arie Feuer Michael Elad's research spans signal and image processing and machine learning, with specialization in inverse problems, sparse representations, deep learning, and generative models. He is particularly renowned for his work on sparse representations, having created the influential K-SVD algorithm together with Michal Aharon and Bruckstein. His 2010 book "Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing" is a leading publication in this field. Elad has also made significant contributions to diffusion models and generative AI, applying these concepts to solve complex problems in signal and image processing. His extensive publication record shows a clear evolution from foundational work on sparse representations to more recent applications in deep learning and generative models. While his early work focused on theoretical aspects of sparse coding and dictionary learning, his more recent publications demonstrate an integration of these concepts with modern deep learning techniques, particularly in the areas of image restoration, super-resolution, and generative modeling. Elad's scientific achievements have been recognized with numerous awards: Rothschild Prize in Engineering (2024) Member of the Israel Academy of Sciences and Humanities (2024) Weizmann Award for contributions in Sparse Modeling (2021) IEEE SPS Sustained Impact Paper Award (2018) IEEE SPS Best Paper Award (2018) IEEE SPS Technical Achievement Award (2018) Fellow of the Society for Industrial and Applied Mathematics (SIAM Fellow) (2018) IEEE Fellow (2012) ERC advanced grant (2013) Throughout his career, Elad has been actively involved in academic service and mentorship. He has served as an Associate Editor for several prestigious journals including IEEE Transactions on Image Processing, IEEE Transactions on Information Theory, and Applied Computational Harmonic Analysis. From 2016 to 2021, he was the Editor-in-Chief for SIAM Imaging Sciences. He has advised numerous students, including Michal Aharon and Yaniv Romano. Elad also headed the Rothschild-Technion Program for Excellence from 2015 to 2018, an undergraduate program for exceptional students. Elad maintains an active research laboratory at the Technion focused on advancing the theory and applications of sparse representations, deep learning, and generative models in signal and image processing. His team continues to push the boundaries of what's possible in image restoration, super-resolution, and other inverse problems in imaging.

• signal processing
• image processing
• machine learning
• +7 more

Shoham Sabach is an Associate Professor at the William Davidson Faculty of Data and Decision Sciences, Technion–Israel Institute of Technology. As of August 2022, he is on an extended sabbatical at Amazon Research in the Bay Area, CA, USA. His research focuses on Continuous Optimization, including theory, algorithms, and applications in Engineering and Science. Research Interests : Dr. Sabach specializes in developing and analyzing optimization methods for large-scale problems, with applications across engineering and scientific domains. His work emphasizes bridging theoretical foundations with practical implementations. Awards : Recipient of the 2017 SIAG/Optimization Prize for the PALM paper (co-authored with Marc Teboulle and Jerome Bolte). Advising & Sabbatical : Currently on sabbatical at Amazon Research, no specific grants or advising details are provided in the text. His professional website is hosted at https://sites.google.com/view/ssabach .

• Continuous Optimization: Theory and Algorithms
• Development and Analysis of Optimization Methods
• Applications of Optimization Methods in Engineering and Science

Prof. Amir Boag is a Professor in the Physical Electronics Department of the School of Electrical Engineering at Tel Aviv University. He received his B.Sc. and B.A. degrees Summa Cum Laude in 1983, M.Sc. in 1985, and Ph.D. in 1991, all from the Technion - Israel Institute of Technology. His academic journey includes faculty positions at the Technion (1991-1992), a Visiting Assistant Professorship at the University of Illinois at Urbana-Champaign (1992-1994), and industry experience at Israel Aircraft Industries (1994-1999) before joining Tel Aviv University in 1999. Prof. Boag's research focuses on computational electromagnetics and acoustics, specializing in numerically efficient algorithms, beam representations of fields, radar imaging techniques including Synthetic Aperture Radar, quantum-electromagnetic simulations, and antenna/nano-antenna design. His work bridges theoretical developments with practical applications in electromagnetic and acoustic systems. His research group comprises over ten graduate students working on cutting-edge problems in wave physics. Prof. Boag has published more than 130 journal articles and presented over 300 conference papers. He has been instrumental in organizing the Tel Aviv University Antenna Symposium and Underwater Acoustics Symposium, with the 13th Underwater Acoustics Symposium scheduled for October 23, 2025. IEEE Fellow (2008) for contributions to integral equation based analysis, design, and imaging techniques Fellow of the Electromagnetics Academy Former Associate Editor for IEEE Transactions on Antennas and Propagation Holder of approximately ten patents in electromagnetics and antenna design Prof. Boag's research demonstrates strong continuity in developing efficient numerical methods for electromagnetic and acoustic problems, with increasing focus on quantum-electromagnetic interactions and nano-scale applications in recent years. His work maintains strong connections between fundamental theory and practical engineering applications, particularly in radar and imaging systems.

• Computational Electromagnetics
• Computational Acoustics
• Fast Multilevel Algorithms
• +5 more