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Ely PoratView profile

Associate Professor

• Algorithms
• Data Structures
• Streaming Algorithms
• +5 more

Ely Porat is an Associate Professor at the Department of Computer Science, Bar-Ilan University, where he has been since 2000. He holds visiting professorships at the University of Michigan and Tel Aviv University, and has worked at Google (Mountain View in 2007 and Tel Aviv in 2011). His academic contributions include redefining the BSc degree in Computer Science at Bar-Ilan University and significant involvement in teaching committees. Research Interests: Algorithms and Data Structures Streaming Algorithms Pattern Matching Coding Theory Compressed Sensing His work focuses on advancing efficient algorithms for data processing, with applications in information retrieval, signal processing, and bioinformatics. He has organized multiple conferences including Stringology (2009–2011), ICALP2011GT, and UM Coding. He has served on program committees for CPM, SPIRE, and ESA. Advising & Collaboration: Current advisees include Ariel Shiftan, Guy Feigenblat, and others. Former students include Ohad Lipsky and Klim Efremenko. He hosts short-term researchers from abroad and collaborates with institutions like Google and Weizmann Institute. Publications span FOCS , STOC , ICALP , and other top venues, emphasizing theoretical foundations and practical algorithmic solutions.

Mark NewmanView profile

Professor

• Network Science
• Statistical Physics
• Complex Systems
• +3 more

Mark Newman is the Anatol Rapoport Distinguished University Professor of Physics at the University of Michigan, affiliated with the Department of Physics within the College of Literature, Science, and the Arts. His research focuses on network science, statistical physics, and complex systems, with notable contributions to community detection algorithms, network topology analysis, and applications in epidemiology, social dynamics, and computational modeling. Newman's work has been widely recognized, including his election to the Royal Society (2022) and the 2024 Leo P. Kadanoff Prize. His research spans theoretical frameworks and practical applications, such as developing methods for inferring network structures from noisy data, analyzing core-periphery hierarchies in social systems, and exploring the interplay between luck, skill, and competition in games. He is also renowned for his cartogram mapping techniques, featured in the Washington Post for visualizing election data through network-based spatial distortions. Newman's academic contributions include foundational textbooks like *Networks: An Introduction* (2010), which remain pivotal in teaching and research. His recent work addresses challenges in drug-disease network analysis, Bayesian mixture models, and improving mutual information measures for classification tasks. Awards and honors include the Royal Society Fellowship and the Kadanoff Prize, underscoring his impact on both theoretical and applied physics.

Okan ArslanView profile

Associate Professor

• Operations Research
• Combinatorial Optimization
• Network Design
• +3 more

Okan Arslan is an Associate Professor in the Department of Decision Sciences at HEC Montréal. He holds memberships in the Interuniversity Research Centre on Enterprise Networks, Logistics and Transportation (CIRRELT) and the Group for Research in Decision Analysis (GERAD). His expertise lies in Operations Research, Combinatorial Optimization, and Large-Scale Network Design. Current research focuses on smart last-mile delivery systems. Education: Ph.D. in Industrial Engineering from Bilkent University. Research Interests: Operations Research, Combinatorial Optimization, Network Design, Logistics, Transportation Systems, and Last-Mile Delivery Optimization. Recent work explores facility location under uncertainty, selective routing frameworks, and crowd-based logistics solutions. Awards include the 2024 Transportation Science Meritorious Service Award (2025), Amazon Last Mile Routing Challenge Third Place (2021), and the HEC Montréal New Researcher Prize (2021). He has also received an Honorable Mention from INFORMS (2019) for work on evasive flow capturing. Teaching includes courses such as Fundamentals of Optimization and Distribution Management. He has supervised 4 master’s theses (e.g., Benders decomposition for facility location, routing optimization) and 2 project supervisions (e.g., workload forecasting at Hydro-Québec). His lab affiliations and collaborations emphasize applied research in transportation and logistics, with a focus on practical industrial applications and network resilience.

Laurent CharlinView profile

Associate Professor

• Machine Learning
• Artificial Intelligence
• Recommender Systems
• +3 more

Roles & Affiliations Associate Professor at HEC Montréal (Department of Decision Sciences) with adjunct ties in Computer Science. Holder of a Canada CIFAR AI Chair. Member of MILA, the Chair Data Science for Real-Time Decision Making, and the Centre de recherche en mathématiques. Education Ph.D. in Computer Science (University of Toronto), MMath (Computer Science, University of Waterloo). Research Interests Focuses on machine learning applications in decision-making, including reinforcement learning for traffic control, energy systems, and recommender systems. Specializes in topics like continual learning, graph-based methods, and operations research integration. Develops foundational models such as collaborative topic Poisson factorization and neural combinatorial optimization approaches. Publications Overview Recent work includes reinforcement learning for sustainable energy use (2025), traffic signal control innovations (2024-2022), and foundational contributions like CTPF (2014). His research bridges theory and application, emphasizing scalable solutions for complex systems. Awards & Recognition Canada CIFAR AI Chair in Artificial Intelligence Advising & Grants Supervised over 30 students across PhD and Master's programs, with notable projects in traffic optimization, electricity pricing, and toxic content analysis. Active in grant-funded research through collaborations with industry partners like Beneva and academic networks like MILA. Labs & Collaborations Core member of MILA and affiliated with HEC's decision science research groups. Leads projects in real-time decision-making and data science applications across domains.

Aleksander ByrskiView profile

Professor

• Nature-inspired Computing
• Agent-based Distributed Computation
• Artificial Intelligence
• +3 more

Aleksander Byrski is a Full Professor at AGH University of Science and Technology, serving as Deputy Dean for Research and Cooperation in the Faculty of Computer Science, Electronics and Telecommunications. He is also an active member of the Council of Information Technology and Telecommunications and Deputy President of the Try IT Foundation. His research focuses on nature-inspired computing, agent-based distributed computation, artificial intelligence, and parallel/distributed computing and simulations. Byrski's work spans optimization algorithms, high-performance computing frameworks, and applications in urban traffic simulation, healthcare, and engineering. His research interests emphasize scalable agent-based systems, socio-cognitive metaheuristics, and their practical implementations in solving complex real-world problems. Key contributions include advancements in ant colony optimization, evolutionary multi-agent systems, and distributed computing paradigms. Byrski actively bridges theoretical computer science with applied domains such as traffic management, epidemic modeling, and smart city technologies. Articles from 2023–2025 highlight his focus on hybrid metaheuristics, socio-cognitive algorithms, and optimization for time-delay systems. Notable themes include improving algorithm efficiency through migration strategies, leveraging two-dimensional pheromone models, and exploring open/closed source frameworks for large-scale optimization. His work often addresses scalability challenges in HPC environments and real-time systems. Byrski has received no explicitly mentioned scientific awards but is recognized for his leadership roles in academic and industry collaborations. His advising and grants are not detailed here but reflect ongoing research in agent-based computing and functional programming paradigms. He contributes to interdisciplinary teams advancing computational intelligence and societal applications such as epidemic simulation and smart infrastructure.

Szemerédi EndreView profile

Professor

• Discrete mathematics
• Combinatorics
• Theoretical computer science
• +1 more

Endre Szemerédi is a Hungarian mathematician and computer scientist, currently serving as a Permanent Research Fellow at the Alfréd Rényi Institute of Mathematics (Hungarian Academy of Sciences) and holding the New Jersey Professor of Computer Science position at Rutgers University since 1986. His career spans prestigious institutions including Stanford, Caltech, and the Institute for Advanced Study. Expertise: Discrete mathematics, combinatorics, theoretical computer science, and number theory. Education: M.Sc. (1965, Eötvös Loránd University), Ph.D. (1970, Moscow State University under Israel Gelfand). Research legacy encompasses foundational contributions to combinatorics, graph theory, and computational complexity. His work on Hamiltonian cycles in random graphs , parallel sorting networks , and the Blow-up Lemma revolutionized extremal combinatorics and algorithm design. Scientific honors include: 2012 Abel Prize and Széchenyi Prize 2008 Rolf Schock Prize and Steele Prize 1982 Member of Hungarian Academy of Sciences , 2010 Member of US National Academy of Sciences 1975 Pólya Prize , 1973 Rényi Prize , multiple Grünwald Prizes .

Sriram SankaranarayananView profile

Professor

• Cyber-Physical Systems
• Formal Methods
• Robotics
• +3 more

Sriram Sankaranarayanan is a Professor of Computer Science and the Associate Dean for Digital Education in the College of Engineering at the University of Colorado Boulder. He earned his PhD in Computer Science from Stanford University in 2005 under Zohar Manna and Henny Sipma. Prior to joining CU Boulder in 2009, he worked as a research staff member at NEC Laboratories America. His research focuses on formal methods for cyber-physical systems, including hybrid dynamical systems analysis, safety verification, and applications in robotics, medical devices (e.g., artificial pancreas systems), and human-autonomy interaction. He leads the Programming Languages & Verification group at CU Boulder and teaches courses in programming languages, algorithms, and control systems. Recent work emphasizes neural network-based control systems, robust safety guarantees, and anticipatory algorithms for stochastic games. His contributions span theoretical advancements (e.g., control barrier functions) and practical tools like the Sherlock verification framework. He has advised numerous PhD students, including Emily Jensen, Monal Narasimhamurthy, and Kandai Watanabe. Publications highlight innovations in physics-informed neural networks (PINNs), polyhedral control Lyapunov functions, and anticipate-act strategies for human-robot collaboration. Research spans domains from energy policy modeling to healthcare technology, reflecting his interdisciplinary approach to system-level challenges.

Kaushik MallikView profile

Assistant Professor

• Controller Synthesis
• Cyber-Physical Systems
• Combinatorial Games on Graphs
• +4 more

Kaushik Mallik is an Assistant Professor at the IMDEA Software Institute in Madrid, Spain. His research focuses on formal methods for Cyber-Physical Systems (CPS), algorithmic fairness in AI, and combinatorial game theory. He holds a PhD from the Max Planck Institute for Software Systems (MPI-SWS) and has been recognized with the 2023 ETAPS Doctoral Dissertation Award. Education: B.Tech in Electrical Engineering, Meghnad Saha Institute of Technology, 2012 M.Tech in Systems and Control, IIT Roorkee, 2015 (with DAAD-funded research at TU Berlin) PhD in Computer Science, MPI-SWS, 2022 Research Interests: Verification & synthesis of CPS controllers Bidding games and algorithmic game theory Runtime fairness monitoring for AI systems Symbolic control for stochastic systems Awards: 2023 ETAPS Dissertation Award CONCUR 2024 Best Paper Nomination IIT Master-Sandwich-Programme DAAD Scholarship (2014) Current Projects: Developing fairness shields for AI decision-making systems Advancing bidding game theory for CPS control Runtime verification frameworks for dynamic fairness His work bridges formal methods with emerging AI challenges, focusing on verifiable safety and ethical algorithm design. Tools like Mascot-SDS and BOCoSy reflect his contributions to practical controller synthesis.

Ivan GavranView profile

Researcher

• Formal Verification
• Reinforcement Learning
• Multi-Robot Systems
• +3 more

Ivan Gavran is a Researcher at the Max Planck Institute for Software Systems (MPI-SWS), focusing on foundational and applied aspects of computer science. His primary research interests include formal verification, reinforcement learning, multi-robot systems, and cyber-physical systems. He explores the intersection of formal methods with machine learning and robotics, aiming to create robust, provably correct systems. His work emphasizes practical applications such as smart contract verification, human-robot collaboration, and distributed task planning. He has developed tools like Lassie for interactive theorem proving and Antlab for multi-robot task coordination. Gavran’s contributions bridge theoretical computer science with real-world systems, addressing challenges in security, reliability, and scalability. While no formal awards are listed in the provided text, his publications reflect significant engagement with leading conferences in formal methods and robotics. His research often involves collaborative projects, leveraging MPI-SWS’s interdisciplinary environment to tackle complex problems in distributed systems and artificial intelligence.

Vadim SokolovView profile

Associate Professor

• Systems Engineering
• Operations Research
• Transportation Systems Analysis
• +6 more

Vadim Sokolov is an Associate Professor in the Department of Systems Engineering and Operations Research at George Mason University (GMU), with affiliate status in the College of Engineering and Computing (CEIE). His research focuses on developing robust methodologies for large-scale systems analysis, combining agent-based modeling, Bayesian statistical techniques, and computational experimentation. He co-developed Polaris, an open-source mobility simulator widely used in transportation network analysis by government entities. Previously, Sokolov served as a Principal Computational Scientist at Argonne National Laboratory, a Fellow at the University of Chicago's Computation Institute, and a Lecturer at the University of Chicago. He holds a Ph.D. in Computational Mathematics from Northern Illinois University. His research interests span agent-based modeling, Bayesian analysis, transportation systems, and computational software development. Notable contributions include work on stochastic computer models, deep learning applications in traffic flow prediction, and quantum Bayesian computation. He has published in top-tier journals such as the Annals of Applied Statistics and Transportation Research Part C. Though no awards are explicitly listed, his extensive publications and industry collaborations highlight his impactful work in computational systems and transportation analysis.

Yue ZhangView profile

Associate Professor

• Service and Healthcare Operations
• Supply Chain Design
• Facility Location
• +4 more

Dr. Yue Zhang is an Associate Professor in Operations Management at the John B. and Lillian E. Neff College of Business and Innovation , University of Toledo . His research focuses on service and healthcare operations management, supply chain design, facility location, and logistics optimization. Education: Ph.D. in Operations Management, Desautels Faculty of Management, McGill University (2009) M.Sc. in Management Science and Engineering, Tsinghua University (2003) B.Sc. in Management Information Systems, Tsinghua University (2001) His work spans healthcare facility network design , patient choice modeling , long-term care capacity planning , and retail network optimization . Publications appear in journals like Operations Research , European Journal of Operational Research , and Health Care Management Science . Recent research includes multi-criteria surgery scheduling , streetlight location optimization , and preventive healthcare network design , emphasizing stochastic modeling , congestion management , and equity in healthcare access . Scientific Awards: Brooks Insurance Research Fellowship (2016-2018) Simonetti Graduate Teaching Award (2015) URAF Summer Research Award (2013) Junior Faculty Research Award (2012) He teaches courses in business statistics , prescriptive analytics , and supply chain management , and has contributed to book chapters on healthcare location models and business analytics.

• Combinatorics
• Probabilistic Methods
• Random Graphs
• +4 more

Rajko Nenadov is a Lecturer in Theoretical Computer Science at the University of Auckland, New Zealand. Previously, he earned his PhD from ETH Zurich in 2016 under Angelika Steger. He held postdoctoral positions at Monash University (Australia) and ETH Zurich, focusing on combinatorics and graph theory. Between 2018–2022, he worked as a software engineer at Google Zurich, contributing to search ranking algorithms. Returning to academia in 2023, his research emphasizes probabilistic methods, random structures, and applications in theoretical computer science. His education includes a PhD in Computer Science from ETH Zurich (2016). Notable career milestones include postdoctoral research on expanders, pseudorandomness, and Ramsey theory, followed by industry experience in algorithmic development. Rajko’s research interests revolve around combinatorics, with a focus on probabilistic methods, random graphs, extremal graph theory, and Ramsey theory. His work bridges foundational combinatorics with practical applications in computer science, particularly in algorithm design and computational complexity. His recent publications explore topics like hypergraph universality, container theorems, and extremal subgraph counting. These contributions highlight advancements in probabilistic combinatorics and structural graph theory. No scientific awards or grants are explicitly listed in the provided information. His career transition from academia to industry and back underscores his interdisciplinary expertise in theory and applied computing.

• Combinatorics
• Probability Theory
• Statistical Physics
• +3 more

Bhargav Narayanan is an Associate Professor in the Department of Mathematics at Rutgers University. His research focuses on combinatorics, probability theory, statistical physics, and theoretical computer science, with a specialization in combinatorial models and probabilistic methods. He organizes a weekly seminar at Rutgers exploring these topics. Previously, he held a research fellowship at St John's College, Cambridge, and completed his PhD under Béla Bollobás at Cambridge University. Education: PhD in Mathematics from the University of Cambridge. Career: Postdoctoral research at St John's College, Cambridge before joining Rutgers. Research Interests: Combinatorics, probability theory, statistical physics, and theoretical computer science. He has published extensively in top-tier journals such as the Annals of Mathematics and the Journal of the European Mathematical Society. Selected publications highlight contributions to antichain codes, random graph bisections, threshold phenomena, and combinatorial optimization. His work often bridges discrete mathematics and applications in physics and computer science. He has taught graduate courses on additive combinatorics, spectral methods, and probabilistic methods in combinatorics. No scientific awards explicitly listed in the provided texts. Advising: No students listed explicitly in the text. Grants: No specific grants mentioned. Lab/Team: Organizes a weekly research seminar at Rutgers.

• Actuarial Mathematics
• Risk Theory
• Ruin Theory
• +5 more

Chadjiconstantinidis Stathis is a Professor at the Department of Statistics and Insurance Science, University of Piraeus. He earned his Doctorate in Statistics (1990) and Diploma in Mathematics (1985) from Aristotle University of Thessaloniki. His academic roles include undergraduate and postgraduate teaching across multiple institutions including the University of Athens and Athens University of Economics and Business. Research Focus: His work spans actuarial mathematics, risk theory, and statistical optimization. Primary domains include: Ruin theory and bankruptcy modeling in insurance contexts Collective risk models with dependencies Optimal experimental designs for statistical efficiency Reliability theory and stochastic pattern analysis Publications: His 15 most recent articles show consistent focus on risk modeling (compound processes, ruin probabilities) and statistical design optimization (D-optimal cyclic designs). Actuarial mathematics dominates newer works (2005–2013), while earlier publications emphasize experimental design combinatorics. Academic Leadership: Department President (2007–2009) Director of Master’s Program in Actuarial Science & Risk Management (2007–2011) Deputy Chairman of Actuary Licensing Committee (Ministry of Finance) Senate Member at University of Piraeus (1997–1998, 2007–2009) Doctoral Supervision: Mentored dissertations on risk theory and statistics pedagogy. Current advisees not specified.

• Discrete Optimization
• Algorithm Design
• Computer Vision
• +3 more

Prof. Vladimir Kolmogorov is a faculty member at the Institute of Science and Technology Austria (IST Austria), specializing in discrete optimization and algorithm design. He holds a Ph.D. in Computer Science from Cornell University and has held positions at Microsoft Research and University College London. His research focuses on combinatorial optimization, MAP inference in graphical models, and applications in computer vision. Educations: M.S. in Applied Mathematics and Physics, Moscow Institute of Physics and Technology Ph.D. in Computer Science, Cornell University Research Interests: Dr. Kolmogorov's work spans algorithmic optimization, including complexity analysis of constraint satisfaction problems, graph algorithms, and machine learning applications. His contributions include foundational work on graph cuts for computer vision and the development of efficient optimization methods for discrete problems. Publications: His recent work includes advancements in parallel algorithms for Gibbs distributions, semidefinite programming, and combinatorial optimization. These contributions highlight his expertise in bridging theoretical computer science with practical applications. Awards: Royal Academy of Engineering/EPSRC Research Fellowship (2006–2011) ERC Consolidator Grant (2014–2020) Best Paper Award at ECCV 2002 Outstanding Student Paper Award (NIPS 2007) Best Paper Honorable Mention (CVPR 2005) Advising and Grants: He has advised multiple PhD students and leads a research team at IST Austria. His grants include significant funding for exploring optimization in machine learning and discrete systems. Labs/Teams: His lab focuses on theoretical and applied discrete optimization, collaborating with institutions globally. Current projects include developing faster algorithms for graph problems and advancing Gibbs distribution analysis.