Prof. Yair Weiss is a faculty member at the School of Computer Science and Engineering, The Hebrew University of Jerusalem . He holds a PhD in Brain and Cognitive Sciences from MIT and an MSC in Applied Mathematics from Tel-Aviv University. Education: MSc in Applied Mathematics, Tel-Aviv University (1993) PhD in Brain and Cognitive Sciences, MIT (1998) His research focuses on Human and Machine Vision , Machine Learning , Bayesian Methods , and Neural Computation . Recent work explores adversarial examples, generative models, and robustness in neural networks. Recent publications highlight trends in: Understanding neural network representations Advancements in GANs and adversarial training Image restoration and translation techniques Perceptual distance modeling Bayesian approaches to computer vision Mathematical analysis of deep learning architectures
Lihi Zelnik-Manor is a Professor at the Faculty of Electrical and Computer Engineering at the Technion - Israel Institute of Technology . Her research focuses on digitizing the sense of touch, integrating Haptics , Robotics , and Computer Vision to create digital representations of physical properties and develop haptic feedback devices for virtual interactions. Executive Vice President for Innovation and Industry Relations (2023-2026) Vice Dean for Graduate Studies (2022-2023) General Chair: CVPR’21, ECCV’22 Her work spans Neural Architecture Search (NAS) , 3D Reconstruction , and Image Processing , with recent publications on haptic devices (2025), diffusion models (2025), and soft-tissue simulation (2024). She actively contributes to academic leadership through roles in top conferences and community initiatives like the Schmidt Postdoctoral Award steering committee.
Jonathan Kahana is a Researcher in the Computer Science department at the Hebrew University of Jerusalem . His research spans Machine Learning and Computer Vision , focusing on Weight Space Learning , Representation Learning , and Zero-Shot Model Search . He develops methods for probing neural network weights to extract information, including ProbeGen and Spectral DeTuning . His recent work includes mapping model weights into shared embedding spaces (ProbeX), recovering pre-fine-tuning weights of generative models, and improving zero-shot labeling with distribution priors. He contributes to open-source implementations, such as the ProbeGen GitHub repository. Research trends from his publications emphasize: Weight space analysis (ProbeGen, DSiRe) Model retrieval and classification (ProbeLog, Model Atlas) Disentanglement and invariance (Contrastive Objective, Red PANDA) Efficiency in probing (30-1,000x FLOPs reduction in ProbeGen) His work has been accepted at top-tier conferences including ICML , ICLR , and ECCV , with arXiv preprints covering topics like dataset size recovery and model tree analysis.
Tomer Michaeli is an Associate Professor at the Faculty of Electrical and Computer Engineering, Technion – Israel Institute of Technology. His research focuses on interdisciplinary areas bridging computer vision, machine learning, and signal processing methodologies. Research Interests Computer Vision Machine Learning Image Processing Signal Processing
Prof. Alex Bronstein is a Professor at the Henry and Marilyn Taub Department of Computer Science, Technion – Israel Institute of Technology, where he holds the Dan Broida Academic Chair and heads the VISTA Lab and the Center for Intelligent Systems. He concurrently serves as a Visiting Professor at the Austrian Institute of Science and Technology. His research spans computer vision, machine learning, computational geometry, signal processing, and bioinformatics, with a focus on geometric data analysis and AI applications in science. He has held significant industry roles including Principal Engineer at Intel Corporation, co-founding startups such as Invision (acquired by Intel), VideoCites, and Sibylla. His work bridges academia and industry, emphasizing practical applications of theoretical insights. Research interests include foundational AI models for molecular biology, robust machine learning systems, and interdisciplinary applications in healthcare and robotics. Notable projects include protein structure prediction using AlphaFold integration, adversarial robustness frameworks, and medical imaging innovations like T1-PILOT for MRI acceleration. He actively collaborates with global institutions, maintaining labs in Haifa, Vienna, and Sardinia. Recent publications highlight advancements in quantum computing simulations, wearable health monitoring systems, and AI-driven biomedical solutions. While no explicit awards are listed, his leadership roles and industry impact underscore significant contributions to computational science. He mentors graduate students in his VISTA Lab, focusing on cutting-edge projects in AI, computer vision, and computational chemistry.
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.
Dr. Chaim Baskin is a researcher at the School of Electrical and Computer Engineering , part of the Faculty of Engineering Sciences at Ben-Gurion University of the Negev . His work focuses on advancing computational learning and deep learning algorithms for real-world applications.
Associate Professor Tamir Hazan is a faculty member at Technion - Israel Institute of Technology, where he joined in 2015. His research focuses on theoretical and practical aspects of machine learning, with applications spanning computer vision, natural language processing, and computational biology. His work bridges mathematical foundations with real-world problem solving in complex systems. Professor Hazan received his Ph.D. from the Hebrew University in 2009. His academic trajectory has established him as a leading researcher in machine learning theory and its applications, with a particular emphasis on developing mathematically rigorous approaches to modern AI challenges. Professor Hazan's research centers on mathematically founded solutions to problems demonstrating non-traditional statistical behavior. His work encompasses perturbation models for efficient learning of high-dimensional statistics, deep learning of infinite networks, and primal-dual optimization for high-dimensional inference problems. His research program spans three major interconnected areas: attention models that improve prediction interpretability, perturbation frameworks that integrate optimization and sampling through extreme value statistics, and convex duality approaches to message-passing in graphical models. His work demonstrates both theoretical depth and practical relevance across multiple domains. Analysis of Professor Hazan's recent publications reveals an evolving research trajectory with increasing emphasis on interpretable machine learning, causal modeling, and applications in medical imaging and behavioral science. His work consistently bridges theoretical foundations with practical implementations, with recent publications showing strong connections between perturbation theory, attention mechanisms, and optimization frameworks. The interdisciplinary nature of his research is evident in applications ranging from pedestrian navigation using smartphone sensors to video-text matching systems and medical image analysis. Professor Hazan has mentored numerous students throughout his career, including: Alex Schwing, now Assistant Professor at UIUC Alon Cohen, now Associate Professor at Tel Aviv University Idan Schwartz, currently Postdoc at Tel Aviv University Current Ph.D. students: Guy Lorberbom, Itai Gat, and Hedda Cohen Multiple M.Sc. students including Adi Manos, Ram Yazdi, and others Professor Hazan's research group maintains an active program with several key focus areas: Attention models for interpretable and improved prediction processes in visual question answering and multimodal applications Perturbation models that enable efficient statistical reasoning in complex systems with exponential configuration spaces Markov random fields, convex duality, and message-passing algorithms for structured prediction and distributed computing
Vadim Indelman serves as an Associate Professor in the Department of Aerospace Engineering at the Technion – Israel Institute of Technology, affiliated with the Technion Autonomous Systems Program (TASP) and Machine Learning and Intelligent Systems (MLIS) Center. He earned his B.A. (Cum Laude) in Computer Science and B.Sc. (Summa Cum Laude) in Aerospace Engineering from the Technion in 2002, completed his PhD in Aerospace Engineering there in 2011 under Pini Gurfil, Ehud Rivlin, and Hector Rotstein, and conducted postdoctoral research at Georgia Tech's Institute of Robotics and Intelligent Machines (2012-2014). His research focuses on probabilistic perception and state estimation for autonomous systems operating in uncertain dynamic environments, with core contributions in SLAM, vision-aided navigation, distributed information fusion, and belief-space planning for multi-agent systems. This work enables reliable real-time operation through probabilistic graphical models and active sensing methodologies. Indelman holds significant editorial roles including Associate Editor for IEEE Robotics & Automation Letters since 2017, Senior Editor for IROS (2021-2023), Area Chair for MRS 2021, and co-chair of IEEE RAS Technical Committee on Planning and Control Algorithms since 2019.
Prof. Beatus Tsevi is an associate professor at the Hebrew University of Jerusalem, affiliated with the Benin School of Computer Science and Engineering and the Silberman Institute of Life Sciences. He holds dual appointments in Computer Science and Life Sciences, and is a member of the Bio-Engineering Center. His research focuses on understanding insect flight control mechanisms and developing bio-mimetic robotics. Education: B.Sc. in Physics and Computer Science, Hebrew University Ph.D. in Physics (Weizmann Institute, microfluidic droplet dynamics) Postdoc in Physics (Cornell University, insect flight control) Research Interests: Flight control dynamics in insects using ultra-fast imaging Multi-modal sensory integration during flight Neural and genetic bases of flight behavior Development of insect-inspired micro-robots Advising & Lab: Current students: 4 PhD/MS candidates and 6 undergraduate researchers Alumni: 12 graduates who transitioned to industry/academia Labs: Micro-Flight Laboratory (Silberman Institute) and Computer Science Office (Rothberg Building) His work bridges engineering, biology, and physics to create novel bio-inspired technologies.
Prof. Ehud Zohary is a Professor of Neurobiology at The Hebrew University of Jerusalem's Edmond and Lily Safra Center for Brain Sciences (ELSC). His primary research focuses on visual perception, neural mechanisms underlying scene analysis, and visual recovery after early blindness. His lab investigates how the brain integrates sensory information to form coherent visual representations, particularly in unique populations such as children recovering from congenital cataracts. Research Themes: Visual perception, neuroplasticity, social cognition, and multisensory integration Key Projects: Vision recovery studies in Ethiopian children (Project EyeOpener), neural correlates of shape perception, and social scene understanding His work combines functional MRI, psychophysics, and computational modeling to explore how visual information is processed across hierarchical brain regions. Recent studies highlight deficits in social action understanding and gaze following in newly sighted individuals, underscoring the critical role of early visual experience. Lab Members: Includes PhD students Ilana Naveh, Sara Attias, Asael Sklar, and postdocs such as Maayan Raveh. Collaborations include multidisciplinary teams studying neuroplasticity and developmental vision.
Ami Wiesel is a Professor at The Rachel and Selim Benin School of Computer Science and Engineering at The Hebrew University of Jerusalem. His research focuses on statistical signal processing, machine learning, and covariance estimation. Previously, he completed his postdoctoral studies at the University of Michigan with Professor Alfred Hero, earned his PhD in Electrical Engineering from Technion under Professors Yonina Eldar and Shlomo Shamai, and obtained his MSc and BSc in Electrical Engineering from Tel Aviv University. His research interests include robust covariance estimation , statistical learning , signal detection , and MIMO communications . Wiesel has made significant contributions to the field of structured covariance estimation, particularly in elliptical distributions and Tyler's estimator. His work bridges theoretical statistics with practical applications in radar systems, wireless communications, and hyperspectral imaging. Wiesel's publications show a clear trend toward integrating deep learning with traditional statistical signal processing methods. His recent work explores unbiased estimation using neural networks, fair principal component analysis, and deep learning applications for target detection with constant false alarm rate. His research spans theoretical foundations in covariance estimation to practical implementations in radar and communications systems. Among his notable scientific achievements are: Young Author Best Paper Award (2019) for 'Learning to Detect' Young Author Best Paper Award (2006) for 'Linear precoding via conic optimization for fixed MIMO receivers' Student Paper Award (2017) for 'Deep MIMO detection' Wiesel has advised numerous graduate students who have gone on to publish significant work in the field. His research has been supported by various grants focusing on statistical signal processing, machine learning applications, and radar systems. His monograph 'Structured Robust Covariance Estimation' (2015) has become a reference in the field. He maintains an active research group focusing on the intersection of statistical learning and signal processing, with applications in communications, radar, and medical imaging.
