جستجوی استاد راهنمای دانشگاهی

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
• +۴ مورد دیگر

Omri Abend is an Associate Professor at The Hebrew University of Jerusalem, affiliated with the School of Computer Science and Engineering and serving as Chair of the Department of Cognitive and Brain Sciences. His research lies at the intersection of Computational Linguistics, Natural Language Processing, and Cognitive Science, with a focus on semantic representation and language acquisition modeling. His primary research interests include: Computational modeling of child language acquisition Semantic representation frameworks, particularly Universal Conceptual Cognitive Annotation (UCCA) Statistical learning and machine translation Unsupervised grammar learning and lexical relation induction Cross-lingual and cross-domain alignment in language models Evaluation methodologies for NLP systems His recent publications demonstrate a strong trend toward analyzing large language models (LLMs), exploring human-like patterns in AI, improving evaluation metrics, and applying NLP to humanitarian domains such as Holocaust testimony analysis. His work combines theoretical linguistic insights with practical machine learning applications. Scientific awards include: Outstanding Paper Award at ACL 2017 Area Chair Award for Best Paper in the Track at ACL 2023 He has supervised and collaborated with numerous researchers and students across projects in semantic parsing, machine translation, and cognitive modeling. His work has been supported by community-wide initiatives such as the MRP shared tasks, which he co-organized. He also leads research on ethical AI, open human feedback, and the computational analysis of historical narratives. Omri Abend leads several research teams focused on: The development and application of the UCCA framework for semantic annotation Cross-lingual and cross-domain knowledge representation in LLMs Computational modeling of language acquisition Evaluation and improvement of NLP systems Application of NLP to digital humanities and historical testimony analysis

• Computational Linguistics
• Natural Language Processing
• Semantic Representation
• +۹ مورد دیگر

Prof. Roni Katzir is a faculty member in the Department of Linguistics at Tel Aviv University, affiliated with the School of Languages. He specializes in formal semantics, computational linguistics, and linguistic theory, with a focus on grammar induction, neural networks, and Minimum Description Length principles. His research integrates theoretical linguistics with cognitive science, exploring topics like large language models (LLMs), scalar implicatures, and phonological learning. He collaborates extensively with researchers such as Nur Lan, Emmanuel Chemla, and Danny Fox, contributing to venues like Linguistic Inquiry , Natural Language Semantics , and ACL conferences. Education details are not explicitly listed, but his work reflects advanced expertise in linguistics and computational methods. Research interests emphasize cognitive plausibility in grammar learning, communicative stability, and the application of computational models to linguistic theory. Publications span over two decades, with recent focus on LLMs' implications for linguistic cognition, neural network generalization, and MDL-based frameworks. His work bridges formal semantics, syntax, and phonology, addressing challenges in language learning and processing. Prof. Katzir's contributions include pioneering the use of Minimum Description Length in linguistic learning models and advancing understanding of logical operators' typological distribution. He maintains an active research lab and collaborates internationally, as evidenced by his co-authored works and conference presentations.

• Formal Semantics
• Computational Linguistics
• Linguistic Theory
• +۷ مورد دیگر

Shay Moran is an Associate Professor at the Faculty of Mathematics , Technion - Israel Institute of Technology, with affiliations to the Faculties of Computer Science and Data and Decision Sciences . They are also affiliated with Google Research in Tel Aviv . Shay's research interests center on mathematical problems inspired by learning theory and computer science , particularly in areas like machine learning theory, differential privacy, algorithmic stability, and online learning . Their work bridges abstract mathematics with practical algorithm design. Recent publications highlight trends in reductions between learning models, geometric interpretations of learning problems, and privacy-preserving algorithms . Key themes include sample compression, PAC learnability, boosting mechanisms, and adversarial robustness . Best Paper Runner-up , COLT 2021 Best Paper Award , COLT 2020 Final Award for Outstanding Paper in Machine Learning Shay has supervised numerous PhD and Master's students, including Vanessa Kosoy, Liza Nesterova, Hilla Schefler, Alexander Shlimovich, Tom Waknine , and Iska Tsubari . Former advisees include Idan Mehalel (PhD, co-advised with Yuval Filmus) and Zachary Chase (Postdoc) .

• Machine Learning Theory
• Computational Learning Theory
• Statistical Learning Theory
• +۷ مورد دیگر

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
• +۲ مورد دیگر

Dr. Gili Greenbaum is an Assistant Professor at The Hebrew University of Jerusalem's Department of Ecology, Evolution and Behavior. His research focuses on computational population genomics, addressing eco-evolutionary processes through genomic data analysis and mathematical modeling. Key areas include conservation genomics, gene drive technology, and human evolution under disease pressures. He leads a lab with interdisciplinary collaborations, advising students across biology, mathematics, and computer science disciplines. Research emphasizes genomic approaches for endangered species conservation, particularly fragmentation impacts on population genetics. Projects also explore gene drive deployment risks and ecological modeling of ancient human interactions with Neanderthals. His lab develops tools like DORA for ancient DNA visualization and modelRxiv for model sharing. Current projects include predicting conservation risks using machine learning, studying immune-related genomic signatures in human populations, and modeling social evolution dynamics. The lab collaborates with institutions globally and offers training opportunities for graduate students and postdocs in computational biology and theoretical ecology.

• Ecological genomics
• Evolutionary modeling
• Conservation genomics
• +۳ مورد دیگر

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.

• Computer Vision
• Machine Learning
• Signal Processing
• +۵ مورد دیگر

Liron Cohen is an Assistant Professor at the Department of Computer Science , Ben-Gurion University , Israel. He earned his PhD at Tel Aviv University under Arnon Avron and was a Fulbright postdoctoral researcher at Cornell University hosted by Robert Constable. Currently on sabbatical at Cornell during 2024-2025, his research focuses on connections between proofs, computation, and mathematics. Education: PhD (Tel Aviv University), MSc (Tel Aviv University) Research Interests Cohen's work bridges logic, type theory, and computational mathematics. Key areas include: Type systems and computational models Theorem proving and automated reasoning Constructive and intuitionistic logic Effectful computation Cyclic proof systems Formal verification Scientific Awards Pazy Memorial Research Award ETAPS Distinguished Paper Award ETAPS Distinguished Artifact Award

• Type Systems
• Computational Models
• Theorem Proving
• +۶ مورد دیگر

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
• +۷ مورد دیگر

Professor Noam Goldberg is a faculty member in the Department of Industrial Engineering and Management at the Faculty of Engineering Sciences, Ben-Gurion University of the Negev, joining the university as part of the 2024-2025 academic roster. His appointment marks a return to the Negev region where he spent part of his childhood in Beersheba and Omer. His educational background includes: Undergraduate studies in business administration and computer science at York University and the University of Toronto Master's degree in operations research from Tel Aviv University Doctoral degree from Rutgers University (New Jersey, USA) Postdoctoral research at the Technion, National Institute of Energy (University of Chicago), and Carnegie Mellon University Goldberg's research centers on optimization methods with dual focus on theoretical rigor and practical implementation. His work in sparse optimization develops techniques to identify minimal-variable explanations for complex phenomena, directly applicable to machine learning model simplification and statistical regression analysis. A significant applied focus involves collaborating with oncologists to optimize tumor radiation therapy planning, addressing challenges like biological uncertainty and patient movement through advanced computational models that balance multiple constraints under volatile conditions. He emphasizes the critical synergy between theoretical frameworks and real-world applications, drawing inspiration from Egon Blas' perseverance in mathematical optimization despite extreme adversity. Outside academia, Goldberg values family time and pursues culinary passions including quality coffee, hummus, and wine, occasionally traveling considerable distances for exceptional examples of these specialties.

• Optimization
• Operations Research
• Sparse Optimization
• +۳ مورد دیگر

Dr. Itay Safran is a faculty member in the Department of Computer Science at Ben-Gurion University of the Negev, Faculty of Natural Sciences. He completed his BSc in computer science and mathematics at Ben-Gurion University, followed by MSc and PhD studies at the Weizmann Institute, and postdoctoral research at Princeton and Purdue Universities in the US. His academic journey reflects a strong foundation in theoretical and applied computer science. Research interests focus on artificial intelligence and deep learning, particularly on developing theoretical foundations to understand the mechanisms behind deep learning technologies and their potential improvements. He aims to establish a laboratory at Ben-Gurion University to advance this research. His academic career emphasizes inspiring students to achieve high professional and human standards while fostering flexibility in research environments. Personal interests include travel, influenced by his postdoctoral experiences in the US, and he expresses a desire to continue exploring Israel.

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +۳ مورد دیگر

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

• Machine Learning
• Theoretical Machine Learning
• Computer Vision
• +۶ مورد دیگر

Prof. Yonatan Loewenstein is a Professor in the Department of Neurobiology at the Hebrew University of Jerusalem. His research focuses on computational neuroscience and cognition, particularly the neural mechanisms underlying decision-making, reinforcement learning, and sensory processing. He leads an interdisciplinary laboratory exploring how learning principles govern behaviors in both biological and artificial systems. Key contributions include studies on somatosensory cortex organization, neuronal homeostasis, and human-machine synergy in decision-making. He co-authored the book Computational Models in Cognition , blending theoretical frameworks with empirical findings. Education details are not explicitly provided in the text, but his affiliations suggest advanced training in neurobiology and computational sciences. Research interests span decision-making biases, reinforcement learning dynamics, and the interplay between brain structure and function. His recent work addresses topics like idiosyncratic choice stability, value modulation in impulsivity, and abstract reasoning in neural networks. Publications highlight collaborations in fields ranging from cognitive dissonance to schizophrenia diagnostics. While no specific awards are listed, his involvement in high-impact journals and interdisciplinary projects underscores his academic contributions. The lab’s work is housed in the Goodman Faculty building, with active group members and experimental facilities.

• Decision-making
• Reinforcement Learning
• Neurobiology
• +۳ مورد دیگر

Prof. Ran Nathan is a Professor in the Department of Ecology, Evolution & Behavior at The Hebrew University of Jerusalem. His research focuses on movement ecology, spatiotemporal plant population dynamics, and ornithology. He holds a Ph.D. and serves as the Chief Editor of Movement Ecology . His work integrates theoretical and empirical approaches to study dispersal, migration, and animal movement mechanisms. Key research interests include the ecology and evolution of dispersal, mechanisms underlying organism movements, and the application of tracking technologies. Notable contributions include discoveries on cognitive navigation in bats and the role of movement in microbial community composition in barn owls. Prizes & Honors: 2019 Distinguished Scientist Award from the National Academy of Sciences (China) Landau Award Prof. Nathan leads research groups exploring movement ecology paradigms, lifetime tracks of organisms, and invasive plant spread. He actively seeks PhD students for studies on raven psychometrics in desert ecosystems and collaborates on projects involving big data analysis in movement ecology.

• Movement ecology
• Spatiotemporal plant population dynamics
• Ornithology
• +۳ مورد دیگر

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
• +۵ مورد دیگر