جست‌وجوی استاد و پژوهشگر

• Wireless Communications
• Coded Caching
• Satellite Communications
• +۵ مورد دیگر

ZHAO Hui serves as a post-doctoral Research Fellow at EURECOM's Communication Systems Department, actively contributing to cutting-edge research in wireless communications. With recent publications extending through June 2025, Dr. Zhao maintains an active research profile focusing on theoretical and practical aspects of next-generation wireless systems. Dr. Zhao's research spans several interconnected domains within wireless communications, with particular emphasis on coded caching techniques , satellite communication systems , and MIMO technologies . The work demonstrates sophisticated mathematical analysis of wireless channel characteristics, with specific attention to stochastic modeling of satellite networks , physical layer security mechanisms , and resource allocation optimization . Recent publications show increasing focus on near-field communications and semantic communications , reflecting the evolution of 6G research directions. The research methodology combines theoretical analysis with practical implementation considerations, often addressing real-world constraints in wireless network design. Among notable recognitions, Dr. Zhao was selected as a Best Student Paper Finalist at the ACSSC 2021 conference, one of only eight papers chosen from the total submissions. This recognition highlights the quality and innovation of the research contributions in the field of wireless communications. Dr. Zhao actively collaborates with researchers including Dirk Slock, Petros Elia, and Antonio Bazco-Nogueras, suggesting participation in a well-established research group focusing on information theory and wireless communications. The consistent publication record across top-tier IEEE journals and conferences indicates successful grant funding and institutional support for this research line.

Jian Huمشاهده پروفایل

دانشیار

• Operations Research
• Risk Management
• Decision Making under Uncertainty
• +۶ مورد دیگر

Jian Hu is an Associate Professor in the Department of Industrial & Manufacturing Systems Engineering at the University of Michigan-Dearborn, where he leads the Smart Mobility Analytics Research and Training Lab. His academic journey includes a PhD in Industrial Engineering & Management Sciences from Northwestern University (2011), an M.Sc. in Applied Science & Systems Engineering from the University of Arkansas at Little Rock, an M.Eng. in Control Theory from the Chinese Academy of Sciences, and a B.Eng. in Information & Control Engineering from Xi'an Jiaotong University. PhD: Industrial Engineering & Management Sciences, Northwestern University M.Sc.: Applied Science & Systems Engineering, University of Arkansas at Little Rock M.Eng.: Control Theory, Chinese Academy of Sciences B.Eng.: Information & Control Engineering, Xi'an Jiaotong University Dr. Hu specializes in data-driven risk management and decision making under uncertainty. His research spans stochastic models in supply chains, transportation, logistics, finance, and healthcare, with a focus on robust optimization, interpretable machine learning, and multi-attribute decision frameworks. Current projects include EV charging infrastructure planning, autonomous driving safety enhancement, and resilient nurse scheduling under uncertainty. His publications demonstrate a trajectory in robust optimization , stochastic dominance , and preference modeling , often intersecting with applications in transportation, homeland security, and healthcare operations. He actively collaborates with Ford Motor Company, Argonne National Laboratory, and NSF-funded initiatives. Dr. Hu has advised 10 graduate students, including PhD candidates at the University of Michigan-Dearborn and Ford GDIA. He teaches graduate courses such as Advanced Stochastic Processes and Optimization.

Will Maمشاهده پروفایل

دانشیار

• E-commerce Optimization
• Supply Chain Management
• Revenue Management
• +۵ مورد دیگر

Will Ma is the Roderick H. Cushman Associate Professor of Decision, Risk, and Operations at Columbia Business School, Columbia University. He is an affiliated member of the Data Science Institute's Foundations of Data Science and Financial and Business Analytics centers. His academic appointment involves full-time research and teaching responsibilities with no indication of part-time status or retirement. Ma's research centers on e-commerce optimization, addressing both supply-side challenges (inventory management, fulfillment logistics) and demand-side opportunities (personalized product assortments). His work specializes in designing real-time algorithms that emphasize simplicity and robustness, with applications spanning revenue management, online matching, dynamic pricing, and data-driven decision-making. Key methodologies include stochastic optimization, algorithmic game theory, and combinatorial design. Analysis of his 15 most recent publications (2024-2025) reveals dominant themes in online optimization under uncertainty, including prophet inequalities, contention resolution schemes, assortment planning, and network revenue management. These contributions consistently bridge theoretical computer science and operations research, with practical applications in logistics, game design, and pricing strategies. Awards and Recognition: Operations Research Reviewer Meritorious Service Award (2025) While specific student advising relationships and grant details are not documented, Ma maintains an active research lab focused on algorithmic solutions for operational challenges. His industry background includes professional poker, video-game startups, and entrepreneurship, enriching his academic work with practical perspectives.

• Causality
• Statistical Machine Learning
• Probabilistic Modeling
• +۱۲ مورد دیگر

Bryon Aragam is an Associate Professor and Topel Faculty Scholar at the Booth School of Business , University of Chicago . His work bridges causality , statistical machine learning , and probabilistic modeling , with applications in AI systems like ChatGPT and DALL-E. Key research themes include: Causal Structure Learning : Extracting latent causal graphs from multimodal data using nonparametric methods. Deep Generative Models : Analyzing overparametrization and variational inference for representation learning. Latent Variable Discovery : Using Markov boundaries and convex subset lattices to uncover hidden dependencies. Algorithm Design : Developing scalable methods like DAGMA for DAG learning and theoretical guarantees for GES/PC algorithms. His paper trends reveal a focus on nonparametric statistics , graphical models , and neural network theory , with recent work on transformer memory dynamics and identifiability in deep latent models . Papers frequently appear in top venues like NeurIPS , JMLR , and AOS , emphasizing theoretical rigor and practical validation.

Robert D. Kleinbergمشاهده پروفایل

دانشیار

• Algorithms
• Theoretical Computer Science
• Algorithmic Game Theory
• +۴ مورد دیگر

Robert D. Kleinberg is an Associate Professor in the Department of Computer Science at Cornell University, where he conducts research on algorithm design and analysis with applications in learning, economics, and networking. His work bridges theoretical foundations with practical implementations in large-scale systems. Education Ph.D. in Computer Science from MIT (2005) Prior industry experience at Akamai Technologies (3 years) Research Focus Kleinberg's research centers on algorithms and theoretical computer science, with emphasis on economic aspects of algorithms, online learning applications, and random processes in networks. His work explores how algorithms interact with strategic agents, optimize under uncertainty, and solve complex network problems. Recent investigations include fairness in prophet inequalities, recalibration methods for online predictors, and reconfigurable network architectures. Publication Trends His recent publications (2024-2017) reveal dominant themes in algorithmic game theory, network optimization, and online learning. Key patterns include resource-constrained bandit problems, calibration techniques for strategic forecasting, and theoretical limits of oblivious routing. His work consistently connects theoretical breakthroughs with real-world applications in networking and economic systems. Scientific Recognition Microsoft Research New Faculty Fellowship Alfred P. Sloan Foundation Fellowship NSF CAREER Award Best Paper Award at ACM Conference on Economics and Computation (EC 2014) Mentorship and Research Leadership Kleinberg has advised 13 PhD students including current advisees Raunak Kumar, Princewill Okoroafor, and Tegan Wilson, and former students such as Bruno Abrahao and Hyung-Chan An. He has mentored 8 postdocs including Yoav Kolumbus and Saeed Alaei. His research is supported by major grants including the NSF CAREER Award and Microsoft Research funding, with significant contributions to program committees for STOC, FOCS, and EC conferences. Collaborative Environment As part of Cornell's Algorithms and Theory group, Kleinberg collaborates extensively with researchers across computer science, operations research, and economics. His work with the Networks Lab and involvement in the Fall 2016 Simons Institute program on Algorithms and Uncertainty demonstrate his integrated approach to theoretical and applied challenges.

Rad Niazadehمشاهده پروفایل

دانشیار

• Online Algorithms
• Algorithmic Mechanism Design
• Data-Driven Decision Making
• +۴ مورد دیگر

Rad Niazadeh is an Associate Professor of Operations Management at the University of Chicago Booth School of Business and holds a courtesy appointment at the Toyota Technological Institute at Chicago (TTIC) . He previously served as a visiting researcher at Google Research NYC and a Motwani postdoctoral fellow at Stanford University's Computer Science Department. He earned his PhD in Computer Science (with a minor in Applied Mathematics) from Cornell University. Research Interests: Focus on data-driven online decision-making and algorithmic mechanism design for platforms and non-profit operations. Key areas include fair resource allocation, dynamic matching algorithms, and socially-aware AI. Recent Publications: 15+ works on topics spanning online algorithms (e.g., refugee resettlement, reusable resource matching), fairness in AI (e.g., equity-constrained search), and market design (e.g., pricing with cancellations, combinatorial Bernoulli factories). Scientific Awards: Recipient of the Asness Junior Faculty Fellowship (2023), multiple INFORMS Rothkopf Junior Researcher Prizes , and IJCAI Distinguished Paper Award (2024). His co-authored works have won best paper honors at INFORMS and MSOM. Advising: Mentoring PhD students Mohammad Reza Aminian (awarded INFORMS Service Science Best Student Paper), Soonbong Lee (INFORMS AMD First Place), and Yueyang Zhong . Collaborates with principal researchers Yiding Feng and Pranav Nuti .

Chaitanya Swamyمشاهده پروفایل

استاد

• Theoretical Computer Science
• Combinatorial Optimization
• Approximation Algorithms
• +۵ مورد دیگر

Chaitanya Swamy is a Professor at the Department of Combinatorics & Optimization , University of Waterloo , and holds the University Research Chair. He earned his PhD in Computer Science from Cornell University in 2004, with David Shmoys as his advisor, and completed postdoctoral work at Caltech . His research focuses on theoretical computer science , particularly combinatorial optimization , approximation algorithms , algorithmic game theory , and stochastic optimization . Education: B. Tech. in Computer Science, Indian Institute of Technology, Delhi PhD in Computer Science, Cornell University Swamy's research interests include algorithm design for optimization problems with uncertainty, network design, scheduling, and game-theoretic models. His recent work explores minimum-norm optimization , regret-bounded vehicle routing , and stochastic programming , with applications in logistics and mechanism design. His publications span top conferences like STOC, FOCS, SODA, ICALP, and IPCO, with a focus on approximation guarantees and algorithmic frameworks for complex optimization. Scientific Awards: University of Waterloo Alumni Gold Medal Outstanding Achievement for Graduate Studies (Ph.D) award, Faculty of Mathematics, Waterloo Best Student Paper Award at SODA 2004 Honorable Mention research paper citation Swamy has advised numerous PhD and Master’s students , including David Aleman Espinosa , Sharat Ibrahimpur , and Andre Linhares , many of whom hold prominent positions in academia and industry. He has served on program committees of leading conferences and as Associate Editor for SIAM Journal on Computing and Discrete Optimization .

• Theoretical Computer Science
• Statistics
• Algorithms
• +۴ مورد دیگر

Tselil Schramm is an Assistant Professor at Stanford University , with courtesy appointments in the Department of Computer Science and Department of Mathematics . Her research bridges Theoretical Computer Science and Statistics , focusing on algorithmic tools for high-dimensional estimation and information-computation tradeoffs. PhD : UC Berkeley (advised by Prasad Raghavendra and Satish Rao) Postdoc : Harvard and MIT (hosted by Boaz Barak, Jon Kelner, Ankur Moitra, Pablo Parrilo) Her work spans algorithms , optimization , and computational complexity in statistical contexts. Recent articles explore semidefinite programming , approximate message passing , and random geometric graphs , reflecting her focus on bridging discrete and continuous optimization for statistical inference. Scientific Awards : NSF CAREER award Stanford Gabilan Fellowship Microsoft Research Fellow Google Research Fellow Teaching : She has taught courses like Machine Learning Theory , Probability Theory , and The Sum-of-Squares Algorithmic Paradigm in Statistics at Stanford since 2021.

Clément Canonneمشاهده پروفایل

مدرس ارشد

• Distribution Testing
• Property Testing
• Learning Theory
• +۵ مورد دیگر

Clément Canonne is a Senior Lecturer in the School of Computer Science at The University of Sydney, where he is a member of the Sydney Algorithms and Computing Theory (SACT) group. He holds an ARC DECRA Fellowship and has established himself as a leading researcher in theoretical computer science with a focus on distribution testing and learning theory. His educational background includes: Ph.D. in Computer Science from Columbia University (advised by Prof. Rocco Servedio) M.Sc. in Computer Science from the Parisian Master of Research in Computer Science Engineering degree from École Centrale Paris Canonne's research primarily centers on distribution testing (a branch of property testing), learning theory, and randomized algorithms within the theory of machine learning. He investigates the computational aspects of learning and statistical inference under various resource or information constraints, with particular emphasis on data privacy and differential privacy. His work bridges theoretical computer science, statistics, and information theory to develop rigorous approaches for modern data analysis challenges. His recent publications reveal a strong trajectory in theoretical foundations of data science, with increasing focus on privacy-preserving techniques and constrained statistical inference. The publications span top venues including FOCS, STOC, NeurIPS, and COLT, demonstrating consistent contributions to both theoretical foundations and practical applications of distribution testing. A significant portion of his recent work addresses differential privacy, communication constraints, and the interplay between computational and statistical aspects of learning. Among his notable recognitions are: 2024 Caspar Bowden PET Award for work on the Discrete Gaussian mechanism ARC DECRA Fellowship Canonne actively mentors the next generation of researchers, currently advising three PhD students (Kenny Chen, Abigail Gentle, and Joy Yang) and having previously supervised Yun Li to completion of an MPhil. He serves in significant leadership roles including General Chair of FOCS 2025, Program Committee co-Chair for ITW 2025, and Program Committee member for SODA 2026. His service extends to editorial roles, including membership on the Theory of Computing editorial board. He is actively involved with the Sydney Algorithms and Computing Theory (SACT) group at the University of Sydney, contributing to both research and community-building initiatives such as co-organizing the Australasian Summer School on Recent Trends in Algorithms.

Nicolas Macrisمشاهده پروفایل

استاد مدعو

• Statistical Mechanics
• Information Theory
• Error Correcting Codes
• +۳ مورد دیگر

Nicolas Macris is an Adjunct Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He is affiliated with multiple groups including the Information Processing group and the Lab for Statistical Mechanics of Inference in Large Systems . His academic career spans roles at the Faculty of Basic Science at EPFL and visiting appointments at institutions like the University College Dublin and ETH Zürich. Macris received his PhD in theoretical physics from EPFL and conducted postdoctoral research at the mathematics department of Rutgers University. His research bridges statistical mechanics , information theory , and quantum information , with a focus on error-correcting codes, graphical models, and learning theory. His work often employs methods from mathematical and statistical physics, probability, and discrete mathematics. His recent publications analyze phase transitions in high-dimensional models, quantum algorithms, and spatial coupling techniques. He has received recognition through IBM Quantum contest awards for his contributions. He actively teaches courses in quantum information processing and mentors PhD students in interdisciplinary research areas. 2019: 2nd place, IBM Quantum contest (Best Paper) 2018: 2nd place, IBM Quantum contest (Best Paper) Macris's work is supported by the Swiss National Foundation for Science , and he collaborates with labs such as the Communication Theories Laboratory and Information Processing group .

Francesco Carrabsمشاهده پروفایل

استاد

• Operations Research
• Combinatorial Optimization
• Graph Theory
• +۵ مورد دیگر

Francesco Carrabs is a Full Professor of Operations Research in the Department of Mathematics at the University of Salerno, Italy, a position he has held since 2024. Previously, he served as Associate Professor (2020-2024) and Assistant Professor (2008-2020) at the same institution, demonstrating a steady progression through the academic ranks. Dr. Carrabs earned his PhD in Computer Science from the University of Salerno in 2006 and graduated cum laude in Computer Science from the same university in 2002. His international research experience includes a visiting scholar position at the Centre de recherche sur les transports (CRT) at the University of Montréal, Canada (2004-2006) under Professors Gilbert Laporte and Jean-François Cordeau, followed by a post-doctoral fellowship at HEC Montréal (October 2006-March 2007). His research focuses on theoretical study, development and implementation of exact and heuristic approaches for optimization problems on graphs. His primary research areas include Routing problems, Variants of Spanning Tree problems, Traveling Salesman Problems, Labeled Graph problems, and Wireless Sensor Networks problems. This program has resulted in approximately 50 publications across various formats. His recent publications reveal a strong emphasis on constrained graph problems, particularly those involving conflict constraints in spanning trees and set covering problems. His methodological contributions span exact approaches like branch-and-cut algorithms and heuristic methods including GRASP and genetic algorithms, often accompanied by publicly available datasets and source code. Dr. Carrabs has participated in numerous national and international research projects and has served on scientific committees for various conferences. His teaching activities at the University of Salerno focus on Operations Research and Optimization courses within the SSD MATH-06/A classification. With an h-index of 16 and over 800 citations (excluding approximately 13% self-citations), Dr. Carrabs has established himself as a respected researcher in combinatorial optimization and operations research.

Mingxi Zhuمشاهده پروفایل

استادیار

• Multi-Agent Systems
• Federated Learning
• Optimization Algorithms
• +۱۲ مورد دیگر

Mingxi Zhu is an Assistant Professor in the Information Technology Management group at the Scheller College of Business, Georgia Institute of Technology. He holds a Ph.D. from Stanford Graduate School of Business in Operations, Information & Technology, where he was co-advised by Professors Haim Mendelson and Yinyu Ye. His academic journey includes a Master's in Economics from Duke University and dual Bachelor's degrees in English Literature and Economics from Beijing Foreign Studies University. Education: Ph.D., Operations, Information & Technology, Stanford Graduate School of Business M.A., Economics, Duke University B.A., English Literature, Beijing Foreign Studies University B.Ec., Economics, Beijing Foreign Studies University Mingxi's research centers on multi-agent collaborated learning, focusing on optimization algorithms, economics, and mechanisms in federated learning environments. His work spans machine learning, data sharing policies, information acquisition, and applications in online finance and auctions. He investigates how distributed agents can effectively learn and make decisions collectively, particularly under constraints of privacy, information asymmetry, and strategic behavior. His recent publications and projects address topics such as randomization in ADMM algorithms, benefits of minimal data sharing in distributed learning, pitfalls of Shapley values in federated learning, and dynamic pricing in large networks. These works reflect a strong trend toward algorithmic design, economic mechanisms, and empirical analysis in digital platforms, combining theoretical rigor with practical relevance. Scientific Awards and Fellowships: Best Paper Award, NeurIPS Workshop (2022) Institutional Venture Partners Fellowship (2020–2021) David S. Tappan Jr. Fellowship (2019–2020) Robert J. and Doreen D. Marshall Scholarship (2018–2019) George A. and Barbara C. Jedenoff Fellowship (2017–2018) Mingxi has served as an instructor for courses including Information System & Digital Transformation and Special Issues of Information Systems at Georgia Tech. He has also been a Teaching Assistant at Stanford for optimization and electronic business courses. He has advised undergraduate students in research and diversity programs and has provided peer review services for top journals such as Management Science and Mathematics of Operations Research . His service includes board membership in the Stanford GSB Greater China Business Club and participation in diversity and inclusion initiatives. He is actively engaged in research that bridges computer science, economics, and business, with ongoing work in federated learning mechanisms, information disclosure in auctions, and dynamic pricing. His projects often involve collaboration with leading scholars and have practical implications for online platforms and financial systems.

Pavel Dvurechenskyمشاهده پروفایل

پژوهشگر ارشد

• Optimization algorithms
• Stochastic optimization
• Optimal transport
• +۷ مورد دیگر

Dr. Pavel Dvurechensky is a Research Fellow at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) since 2015 and a member of Math+, the Berlin cluster of excellence. His research focuses on theoretical and applied aspects of optimization algorithms, particularly first- and second-order methods for convex and non-convex large-scale problems. 2023: Habilitation in mathematics at Humboldt University Berlin 2014-2015: Research assistant at Institute for Information Transmission Problems, Moscow 2009-2015: Junior researcher at Moscow Institute of Physics and Technology His work addresses stochastic optimization, optimal transport, distributed computing, and applications in machine learning, energy systems, and traffic modeling. He has contributed to complexity analysis of mirror descent variants, barrier methods for non-convex problems, and high-probability bounds in heavy-tailed noise scenarios. Recent publications appear in top venues like ICML, NeurIPS, and Mathematical Programming. Selected Scientific Awards: Habilitation in mathematics (2023) He has taught courses on modern optimization at Humboldt University and Higher School of Economics, covering gradient methods, mirror descent, and optimal transport. Collaborations include projects with Math+ cluster on energy system equilibria and brain signal analysis.

Eleni Katiforiمشاهده پروفایل

دانشیار

• Biological Transport Networks
• Biophysics
• Soft Matter Physics
• +۵ مورد دیگر

Eleni Katifori is an Associate Professor and Undergraduate Affairs Chair in the Department of Physics and Astronomy at the University of Pennsylvania's School of Arts & Sciences. She previously served as an Assistant Professor at Penn (2015-2020), was a Visiting Professor at Université Paris Diderot (2017), led an independent research group at the Max Planck Institute for Dynamics and Self-Organization (2012-2014), and was a Fellow at Rockefeller University's Center for Physics and Biology (2008-2012). Her educational background includes a Ph.D. in Physics from Harvard University (2008) and a B.S. from the University of Athens, Greece (2002). These foundational experiences established her interdisciplinary approach bridging physics and biology. Dr. Katifori's research focuses on the theoretical physics of biological transport networks, examining how systems like plant vasculature and animal circulatory systems achieve optimal functionality through evolutionary pressures. Her work investigates the topological properties of these networks, their resilience to damage, and the physical principles governing their development. She employs mathematical modeling to understand how hierarchical structures in biological flow systems balance competing demands of cost, efficiency, and robustness. Her approach integrates concepts from soft matter physics, network theory, and biophysics to uncover universal principles applicable across biological scales. Analysis of her publication record reveals a consistent focus on the mathematical foundations of biological flow networks, with increasing sophistication in topological analysis methods. Her work demonstrates how network architecture determines functional capabilities and resilience properties, with significant contributions to understanding hierarchical organization in biological systems and the physical constraints shaping network evolution. Simons Investigators in the Mathematical Modeling of Living Systems (2018) NSF Career Award (2016) Burroughs Wellcome Career Award at the Scientific Interface (2011) 2021 APS Early Career Award in Soft Matter Research APS DSOFT Early Career Award Member at Large, APS Topical Group on Soft Matter (2018-2021) Dr. Katifori actively mentors students and postdoctoral researchers, with several notable advisees including Henrik Ronellenfitsch, Sean Fancher, and Yongtian Luo. Her research is supported by multiple prestigious grants, including her Simons Investigator award which provides sustained research funding for mathematical modeling of living systems. She has established a productive research group that combines theoretical physics with biological applications, fostering interdisciplinary collaboration across physics, biology, and mathematics. Her laboratory focuses on developing mathematical frameworks to analyze biological transport networks, with particular emphasis on leaf venation patterns, vascular systems, and the physical principles underlying their evolution and function. The group employs computational modeling, topological analysis, and theoretical physics approaches to uncover fundamental principles governing biological flow systems.

• Analysis of Algorithms
• Data Analysis
• Massive Data Sets
• +۱۵ مورد دیگر

Robert Krauthgamer is the Harry Weinrebe Professor of Computer Science and currently serves as Department Head in the Department of Computer Science & Applied Mathematics at the Weizmann Institute of Science , within the Faculty of Mathematics and Computer Science . He is a leading researcher in theoretical computer science, particularly in the analysis of algorithms. Research Interests: His research focuses on Analysis of Algorithms , with deep expertise in Data Analysis and Massive Data Sets , Combinatorial Optimization , Approximation Algorithms , Hardness of Approximation , Embeddings of Finite Metrics , and Routing and Peer to Peer Networks . He also maintains a broad interest in Discrete Mathematics and High-Dimensional Geometry . His recent publications highlight work in graph algorithms, parameterized complexity, streaming algorithms, and metric embeddings. Publication Trends: His most recent work, including papers from SODA 2016, demonstrates a strong trend in the design and analysis of efficient algorithms for fundamental problems in graph theory, optimization, and data streams. Key themes include kernelization and sampling techniques for dynamic graph streams, subexponential parameterized algorithms, deterministic derandomization of the polynomial method, and structural results for graph modification problems. His research often bridges theoretical insights with applications in computational biology and network science. Service and Recognition: Journal Editorial: Editor-in-Chief of SIAM Journal on Computing (2019–2025), Associate Editor (2012–2017); Managing Editor of Theory of Computing (2007–2018), and current Editorial Board Member. Conference Leadership: Program Committee Chair for SODA 2016 and HALG 2018; Steering Committee member for SODA, ESA, and HALG; and committee member for the Gödel Prize (2019–2021). Workshops: Organizer of numerous workshops on sublinear algorithms, fine-grained complexity, and high-dimensional data. Teaching and Mentorship: He regularly teaches advanced courses such as Randomized Algorithms and Sublinear Time and Space Algorithms . He advises a large group of MSc and PhD students and hosts postdoctoral researchers, demonstrating a strong commitment to training the next generation of computer scientists. His former students have gone on to successful academic and research careers. Laboratories and Research Groups: He is a key member of the Foundations of Computer Science (theory) seminar at Weizmann and has organized the TheoryLunch and Reading Group in Algorithms, fostering a vibrant research community within the department.