Game Theoretic Methods Professors and Supervisors | ApplySharif

جست‌وجوی استاد و پژوهشگر

استاد راهنما، استاد دانشگاه یا پژوهشگر مناسب برای مسیر پژوهشی‌تان را پیدا کنید و با او ارتباط بگیرید.

نمایش ۶۷۶-۶۹۰ از ۴٬۴۸۱ استاد راهنما

اطلاعات این پروفایل‌ها از صفحات عمومی دانشگاه‌ها جمع‌آوری شده و ممکن است به‌روز نباشد.

Cezary Gajewskiمشاهده پروفایل

دانشیار

University of AlbertaCanada

Cezary Gajewski serves as an Associate Professor in the Department of Design Studies at the University of Alberta and coordinates the Art & Design Fundamentals program, with his office located in the Fine Arts Building (3-81). His academic credentials include: MFA in Sculpture from the University of Alberta (1998) MDes in Industrial Design from the University of Alberta (2003) PhD in Fine Arts, Applied Arts and Industrial Design from the Eugeniusz Geppert Academy of Fine Arts in Wrocław (2016) Gajewski's research integrates technological and traditional design methodologies with human-centered principles, focusing on 3D form, user experience, and seamless hardware-software interaction in systems like virtual reality environments. His work emphasizes the designer's role from conceptual sketch to physical prototype, translating complex user needs into intuitive, innovative products. Recent industry collaborations include the ViCCi I/II digital way-finding systems, N-Zone network gaming for Alberta's tech sector, and the Canola Bioproducts interactive exhibit for the Science Alberta Foundation. He teaches industrial design methodologies, product design, and Computer Aided Industrial Design (CAID), leveraging cutting-edge tools to bridge theoretical frameworks with practical application in design education.

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Edward Furmanمشاهده پروفایل

استاد

York UniversityCanada

Dr. Edward Furman is a Professor in the Department of Mathematics and Statistics at York University, leading the Actuarial Science Program and serving as Founding Director of the Risk and Insurance Studies Centre (RISC). He holds a Master's (Distinction) and PhD (Summa Cum Laude) in Actuarial Science and Probability Theory from the University of Haifa, Israel. His research focuses on distribution theory, risk measurement, insurance pricing, and systemic risk modeling. Notable achievements include winning the Fortis Chair Best Paper Prize (K. U. Leuven) for collaborative work and serving as an external consultant for institutions like the Central Bureau of Statistics (Israel) and the Society of Actuaries (U.S.). Dr. Furman's work bridges theoretical and applied domains, with funded research by NSERC, the Society of Actuaries, and the Casualty Actuarial Society. He is a Fellow of the Science Leadership Program at the University of Toronto and an Associate Editor of the Journal of Statistical Distributions and Applications (Springer). His interdisciplinary RISC unit explores holistic approaches to insurance risk, integrating actuarial science with statistical and economic frameworks. Research Themes: Dependence modeling, risk capital allocation, systemic risk measurement, and inclusive insurance design. Consulting Expertise: Collaboration with public and private sector entities on actuarial and statistical challenges. Grants: Supported by NSERC, SOA, and CAS for projects on risk aggregation and insurance pricing models.

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Luis Velez Quinteroمشاهده پروفایل

استادیار

Stockholm UniversitySweden

Luis Velez Quintero is an Assistant Professor at Stockholm University within the Department of Computer and Systems Sciences (DSV) . He is affiliated with the Data Science Research Group and the Stockholm Technology & Interaction Research (STIR) group, focusing on Human–Computer Interaction. His research spans adaptive immersive systems, affective computing, and physiological signal analysis in extended reality (XR) environments. Education & Background : Holds a PhD in Computer and Systems Sciences from Stockholm University (2023), MSc in Health Informatics from Karolinska Institutet (2019), and BSc in Electronics Engineering from the National University of Colombia (2015). He has led the startup PortalSense since 2018, developing VR solutions for real estate visualization. Research Interests : Combines data science and ML with XR technologies to create context-aware systems for healthcare, education, and professional training. Key themes include: Adaptive VR/AR systems using real-time physiological and behavioral signals Biosignal integration for personalized user experiences Immersive technologies for cognitive assessment and skill development Publications : Over 20 peer-reviewed articles, including work on affective databases (AVDOS-VR), biosignal frameworks (Excite-O-Meter), and XR applications in cybersecurity education. Recent efforts explore third-person locomotion in VR and early-stage Alzheimer’s detection via spatial navigation tasks. Awards & Grants : Wallenberg Foundation Grant (2023-2025): Analyzing non-verbal communication in psychotherapy Swedish Institute Scholarship (2017-2019): Fully funded Master’s and PhD studies Seed funding for PortalSense from Fondo Emprender SENA Colombia (2022-2023) Advising & Projects : Lead researcher in projects like AVDOS-VR and CS:NO . Co-designed the Excite-O-Meter open-source plugin for real-time physiological analysis in VR. Active in industry collaborations for scalable health interventions and immersive training systems. Labs & Teams : Collaborates with multidisciplinary teams at STIR and DSV, advancing human-centered AI and adaptive XR technologies.

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Fritz Buschمشاهده پروفایل

استاد

Technical University of MunichGermany

Prof. Dr.-Ing. Fritz Busch holds the Professorship of Traffic Engineering and Control at the Department of Civil, Geo and Environmental Engineering at Technische Universität München (TUM). His research focuses on urban transportation systems, traffic dynamics, and sustainable mobility solutions. He leads projects such as RASCH (RAdSCHnellwege) and RadOnTime, exploring efficient bicycle infrastructure and traffic management strategies. His work integrates advanced simulation techniques, data-driven methodologies, and cooperative vehicle-infrastructure systems to enhance urban mobility resilience and reduce environmental impacts. Educational Background: While specific academic qualifications are not detailed here, his title of Professor Dr.-Ing. indicates a doctoral degree in engineering and significant academic contributions. His professorship at TUM underscores expertise in traffic engineering and control systems. Research Interests: Busch’s research spans macroscopic traffic dynamics, bicycle and pedestrian traffic management, emissions reduction through electric vehicle integration, and the application of smart technologies (e.g., V2X communication) for traffic optimization. His projects often involve interdisciplinary collaboration, combining engineering, data science, and urban planning to address complex mobility challenges. Awards and Recognition: While no specific awards are listed, his prolific publication record and involvement in high-profile TUM initiatives reflect his scholarly impact and leadership in transportation engineering. Advising and Grants: Though specific grants are not mentioned, his active participation in projects like UR: BAN (Urban Road Network) and numerous collaborations suggest extensive grant-funded research. Advising activities are inferred through his role as a professor and project leader but are not explicitly detailed here. Labs and Teams: His work leverages the TUM-VT bicycle simulator and SUMO (Simulation of Urban MObility) tools for traffic simulation studies. He collaborates with interdisciplinary teams on projects such as eco-sensitive traffic management and automated vehicle integration.

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Daniel Redheadمشاهده پروفایل

استادیار

University of GroningenNetherlands

Roles and Affiliations: Daniel Redhead is an Assistant Professor at the University of Groningen (since 2023) within the Department of Sociology and the Inter-university Center for Social Science Theory and Methodology (ICS). He concurrently serves as a Guest Researcher at the Max Planck Institute for Evolutionary Anthropology's Department of Human Behavior, Ecology and Culture. Previously, he held roles including Group Leader (2020–2023) and Postdoctoral Research Fellow (2018–2020) at the same institute, and Visiting Researcher at the University of Illinois at Urbana-Champaign (2017–2018). Education: PhD in Psychology (2015–2018), University of Essex Masters by Research (MA) in Anthropology (2014–2015), Durham University BA (Hons) in Archaeology and Anthropology (2011–2014), Durham University Research Interests: Redhead focuses on cultural evolution, human behavioral ecology, social hierarchy, and social network analysis . His work examines how social structures, cooperation, and inequality emerge in diverse contexts—from rural Colombian communities to foraging societies in the Congo Basin. He employs advanced statistical methods and computational models to analyze complex social systems. Articles Trends: His recent publications emphasize methodological innovations (e.g., Bayesian network analysis, STRAND software), cross-cultural studies of cooperation and status, and interdisciplinary approaches bridging anthropology, evolutionary biology, and sociology. Awards: Human Behavior & Evolution Society Margo Wilson Award (2024) NSF HNDS Program Co-PI Grant ($400,000, 2022) Leakey Foundation Research Grant (2025) Teaching & Grants: Redhead teaches courses on Solidarity & Social Contexts and Methodology & Data Collection at the University of Groningen. He actively secures grants as PI/Co-I, including multi-million-dollar NSF and ESRC funding.

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Ted Ralphsمشاهده پروفایل

استاد

Lehigh UniversityUnited States

Ted Ralphs is a Professor of Industrial and Systems Engineering at Lehigh University’s Rossin College of Engineering. He serves as co-founder and director of the Computational Optimization Research at Lehigh (COR@L) Laboratory, and chairs the INFORMS Computing Society. His research focuses on large-scale computation and optimization, bridging theoretical and practical applications through high-performance computing and mathematical techniques. Ralphs holds a Ph.D. in Operations Research from Cornell University and advanced degrees in Mathematics and Applied Mathematics from Carnegie Mellon University. His expertise spans Supply Chain Management, Grid Computing, Mathematical Optimization, Financial Engineering, and Algorithm Development. He teaches courses in computational methods, discrete optimization, financial optimization, and algorithms in systems engineering. Ralphs has received notable honors including the 2021 Rossin College Outstanding Doctoral Student Advising Award and election as an INFORMS Fellow in 2023. His research contributions include advances in bilevel optimization, decomposition methods, and open-source optimization software (e.g., COIN-OR’s Cbc solver). He has led collaborative projects such as a Naval grant-funded initiative with the University of Pittsburgh on bilevel optimization. Ralphs’ work emphasizes scalable algorithms and their real-world applicability in energy markets, logistics, and combinatorial problems.

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Philip Shawمشاهده پروفایل

دانشیار

Fordham UniversityUnited States

Dr. Philip Shaw is an Associate Professor of Economics at Fordham University's Department of Economics. His research focuses on computational macroeconomics, nonparametric econometrics, and the economic impacts of corruption. He holds a Ph.D. from the University of Connecticut and specializes in macroeconomic theory, economic growth, and computational methods. Shaw teaches advanced courses such as Macroeconomic Theory II, Monetary Policy, and Nonparametric Econometrics. His work explores topics like educational corruption's effects on economic growth, nonparametric instrumental variable estimation, and stochastic general equilibrium models. He has published extensively in journals like the Journal of Econometric Methods, Contemporary Economic Policy, and Economics Letters. His teaching emphasizes computational tools like R and MATLAB for econometric analysis. Shaw's research bridges theoretical frameworks with practical computational solutions in macroeconomics and finance. Education: Ph.D., University of Connecticut Key Research Themes: Nonparametric econometric methods Computational solutions to DSGE models Educational corruption and its macroeconomic implications Instrumental variable estimation techniques Teaching Responsibilities: Graduate courses: Computational Economics, Econometrics I & II, Monetary Policy Undergraduate courses: Introductory Econometrics Recent Articles: Shaw's recent work includes analyzing corruption's role in educational choices (2023), nonparametric approaches to stochastic growth models (2014), and the determinants of educational corruption in Ukraine (2015). His methods often integrate advanced computational techniques to address econometric challenges.

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Jacob Goereeمشاهده پروفایل

استاد

University of New South WalesAustralia

Scientia Professor Jacob Goeree is a faculty member at the University of New South Wales (UNSW), affiliated with the UNSW Business School and the Department of Economics. His research focuses on economic theory, experimental economics, and game theory, with notable contributions to auction design, quantal response equilibrium, and market mechanisms. He has published extensively, including influential works on spectrum auctions, mechanism design, and stochastic game theory. Professor Goeree's research explores the intersection of theoretical models and experimental validation, particularly in understanding strategic behavior in complex economic settings. His work on quantal response equilibrium has advanced behavioral game theory by integrating bounded rationality into equilibrium analysis. Recent contributions include tropical analysis applications to resource allocation and studies on equilibrium synthesis in non-convex economies. Publications span topics such as spectrum auction mechanisms, risk aversion testing, and duality in economic systems. Notable projects include experimental comparisons of auction formats and the design of combinatorial exchanges for resource rights. While no explicit awards are listed, his prolific output reflects significant academic recognition in his field. He is actively involved in research partnerships, leveraging UNSW's infrastructure for collaborative projects. His work bridges theoretical insights with practical market design, influencing policy and industry applications in areas like emissions trading and telecommunications spectrum allocation.

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Ali Ramezani-Kebryaمشاهده پروفایل

دانشیار

Swiss Federal Institute of Technology in LausanneSwitzerland

Ali Ramezani-Kebrya is currently an Associate Professor (with tenure) in Computer Science at the University of Oslo. Previously, he was a postdoctoral fellow at the Laboratory for Information and Inference Systems (LIONS) at EPFL and the Vector Institute in Canada. His research focuses on large-scale and distributed machine learning, optimization, privacy/security, reinforcement learning, and communication/networking aspects of machine learning algorithms. He holds a Ph.D. from the University of Toronto. Key research interests include developing robust federated learning frameworks, addressing label shift in distributed systems, and improving the generalization capabilities of stochastic gradient descent methods. His work bridges theoretical foundations with practical applications in distributed optimization and privacy-preserving machine learning. Recipient of the NSERC Postdoctoral Fellowship (equivalent to NSF fellowship in the US) His publications emphasize advancements in distributed learning systems, robust optimization techniques, and theoretical guarantees for federated learning under covariate shifts. Current research trends explore Nash equilibrium-based approaches for robustness and communication-efficient algorithms in distributed settings.

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Simon Lacoste-Julienمشاهده پروفایل

دانشیار

École Normale SupérieureFrance

Simon Lacoste-Julien is an Associate Professor at Université de Montréal, affiliated with the Department of Computer Science and Operations Research (DIRO). He also serves as the Associate Scientific Director of Mila – Quebec Institute of Artificial Intelligence and holds the position of Vice President Lab Director at Samsung SAIT AI Lab Montreal (SAIL). His research focuses on machine learning, optimization, and their applications in areas like deep learning, generative models, causality, and computer vision. Lacoste-Julien has held academic positions at INRIA in Paris and has a PhD from UC Berkeley, with postdoctoral work at the University of Cambridge. He teaches advanced graduate courses on probabilistic graphical models and structured prediction. His work includes contributions to optimization algorithms (e.g., Frank-Wolfe methods), causal discovery, and generative models. Lacoste-Julien has supervised numerous students and postdocs, and his awards include being a CIFAR Fellow and Canada CIFAR AI Chair. His research spans theoretical foundations and practical applications, with a strong emphasis on scalable and efficient machine learning techniques.

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Andreea Enacheمشاهده پروفایل

استادیار

Stockholm School of EconomicsSweden

Andreea Enache is an Assistant Professor at the Department of Economics, Stockholm School of Economics (SSE), with affiliations to the House of Governance and Public Policy, Center for Resilient Health, and Karl-Adam Bonnier Center for Governance. She holds a PhD in Mathematical Economics and Econometrics from Paris School of Economics (2015) and has held academic positions at institutions like Bocconi University, the European University Institute, and the University of Chicago. Her research focuses on structural econometrics, industrial organization, auctions, health economics, digital economics, and cultural economics. Education: PhD in Mathematical Economics and Econometrics, Paris School of Economics, 2011–2015 MSc in Economics, Toulouse School of Economics, 2010–2011 MSc in Applied Econometrics and Statistics, University of Orléans, 2009–2010 Bachelor’s Degree in Finance, Academy of Economic Studies (Bucharest), 2005–2008 Research Interests: Her work integrates econometric methods with applications in auctions, healthcare markets, digital platforms, and cultural industries. She explores topics like market competition dynamics, pricing strategies, and policy impacts using structural models and advanced statistical techniques. Publications: Recent work includes studies on Swedish primary care ownership structures, mobile app consumer behavior, and auction models. These contributions highlight her expertise in linking theoretical frameworks to real-world economic phenomena. Awards: Recipient of the PhD Thesis Prize 'Agguire-Basualdo' (2015), recognizing her doctoral research on structural econometrics and nonlinear inverse problems. Advisees & Grants: While no formal advisee list is provided, her collaborations span institutions like CEPR and involve co-authored research on healthcare, digital markets, and cultural economics. Active participation in workshops like SweRIE (2025) underscores her engagement with academic communities. Labs/Teams: Affiliated with interdisciplinary centers like the Center for Resilient Health and Karl-Adam Bonnier Center for Governance, focusing on policy-relevant research in health systems and governance structures.

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Julie Marxمشاهده پروفایل

استادیار

Copenhagen Business SchoolDenmark

Dr. Julie Marx is an Assistant Professor in the Department of Finance at Copenhagen Business School (CBS), affiliated with the Center for Big Data in Finance (BIGFI). She holds a PhD from CBS and specializes in real estate economics, behavioral finance, and decision-making processes in housing markets. Her research integrates experimental methods with empirical analysis to investigate topics such as reference dependence, negotiation behavior, and household financial decisions. Her work has been published in top journals like the American Economic Review and Journal of Real Estate Finance and Economics . Notable contributions include studies on gender differences in real estate negotiations and how market downturns influence homeownership decisions. She has also contributed to working papers with institutions like NBER and CEPR. Dr. Marx's research emphasizes bridging theoretical models with real-world market behaviors. She is involved in the research project Risky Decisions: Revealing Economic Behavior and maintains an active academic profile through presentations at conferences like the European Finance Association Annual Meeting.

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Marcello Pelilloمشاهده پروفایل

استاد

Ca' Foscari University of VeniceItaly

Marcello Pelillo is a **Full Professor** at the Department of Environmental Sciences, Computer Science and Statistics at Ca' Foscari University of Venice. His research focuses on machine learning, pattern recognition, computer vision, and adversarial machine learning. He has contributed significantly to graph-based methods, clustering algorithms, and security in machine learning systems. Pelillo is affiliated with the European Center for Living Technology (ECLT) and the Research Institute for Complexity Communications . His work spans theoretical advancements in graph theory and practical applications in cultural heritage digitization, climate science (e.g., ice core analysis), and AI security. Recent projects include entropy-guided graph clustering, backdoor poisoning defenses, and benchmark datasets for puzzle-solving tasks. Publications emphasize interdisciplinary applications, such as AI-assisted historical document digitization and energy-latency attacks in networks. He has supervised multiple collaborative projects involving institutions like the Research Institute for Complexity and the European Interuniversity Research Center.

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Gavin Simمشاهده پروفایل

استاد

University of Central LancashireUnited Kingdom

Dr. Gavin Sim is a Reader in Human Computer Interaction at the University of Central Lancashire's School of Computing, Engineering and Physical Sciences. His research focuses on Human Computer Interaction (HCI) and Child Computer Interaction, emphasizing participatory design, usability evaluations, and serious games. He explores how technologies can be designed collaboratively with children for contexts like education, health, and gaming. Notable work includes studies on cross-cultural serious game design (e.g., Uganda-UK collaboration), heuristic evaluation methodologies for educational tools, and ethical frameworks for child participation in research. Sim also teaches undergraduate and postgraduate courses, integrating practical and theoretical insights into HCI practices. His contributions span academic publications on child-centred ethics, evaluation tools like the CHUX Toolkit, and frameworks such as CHECk for ethical design processes. He actively engages in global HCI conferences and initiatives, advocating for inclusive and ethical technology development.

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Geunyeong Byeonمشاهده پروفایل

استادیار

Arizona State UniversityUnited States

Geunyeong Byeon is an Assistant Professor in the School of Computing and Augmented Intelligence at Arizona State University (ASU). She previously held postdoctoral positions at Argonne National Laboratory and Los Alamos National Laboratory. Her research focuses on large-scale optimization methodologies for decision-making in energy systems and interdependent infrastructure networks. Byeon teaches courses in operations research, optimization, and data-driven methods at both undergraduate and graduate levels. Education Ph.D. in Industrial & Operations Engineering (2020), University of Michigan, Ann Arbor M.S. in Industrial Engineering (2016), Seoul National University, South Korea B.S. in Industrial Management Engineering (2014), Korea University, South Korea Research Interests Byeon specializes in developing optimization frameworks for complex systems, including energy grids and interdependent infrastructures. Her work emphasizes distributionally robust optimization , Benders decomposition , and federated algorithms , with applications to power systems, resilient distribution networks, and multi-agent decision-making under uncertainty. She bridges theoretical advancements with practical computational implementations. Teaching Highlights Optimization I (2022-2024) Computing for Data-Driven Optimization (2021-2025) Probability and Statistics for Engineering (2021) Awards MICDE Fellowship (University of Michigan, 2018) Rising Stars in Computational & Data Sciences Nominee (2020) Richard & Eleanor Towner Prize for Outstanding Ph.D. Research (Honorable Mention, 2019) Professional Service Byeon has reviewed for prestigious journals including INFORMS Journal on Computing , Management Science , and IEEE Transactions on Power Systems , and conferences like the Power Systems Computation Conference.

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