جست‌وجوی استاد و پژوهشگر

• Transportation Engineering
• Data Science
• Urban Planning
• +۴ مورد دیگر

S. van Cranenburgh is a researcher at the Delft University of Technology in the College of Technology, Policy and Management , specializing in the Transport and Logistics department. Their work bridges transportation engineering, data science, and urban analytics through innovative methodologies in machine learning and spatial-temporal modeling. Research Focus Transportation behavior modeling (e.g., residential location choice). Advanced collision risk prediction using naturalistic driving data. Integration of computer vision and discrete choice models. Psychological impacts of urban noise pollution on health. Proactive risk learning from large-scale driving datasets. Collective perception in historic urban landscapes. Recent Work Trends Their publications from 2025 emphasize deep learning applications in transportation safety (neural networks, contrastive learning), spatial-temporal data analysis , and social media-driven urban dynamics studies. Key themes include data-driven policy modeling and interdisciplinary approaches combining engineering, psychology, and social sciences. Collaborative Engagement Active collaboration with institutions like TU Delft's 4TU.ResearchData. Contributions to open-access datasets and software repositories. Participation in academic workshops and seminars (e.g., 2025 collective perception workshop). Media engagement on urban mobility topics (2016 coverage in Dutch outlets).

David L. Kellyمشاهده پروفایل

استاد

• Environmental Economics
• Climate Change Policy
• Sustainable Business
• +۳ مورد دیگر

David L. Kelly is a Professor of Economics at the University of Miami 's Miami Herbert Business School , where he also serves as the Academic Director of the MS in Sustainable Business and Co-Chair of the Sustainable Business Research Cluster. His research and teaching focus on environmental economics, climate policy, and sustainable business practices. Education: Ph.D. in Economics, Carnegie Mellon University (1995) M.S. in Economics, Carnegie Mellon University (1991) B.S. in Mathematical Economics (Honors), Wake Forest University (1989) Research Interests: Kelly's work centers on environmental economics , particularly the design of climate policies under uncertainty, the economics of natural disasters, and the intersection of sustainability and business strategy. He explores how learning and uncertainty affect macroeconomic and financial outcomes, with applications to regulation, risk management, and long-term environmental planning. Publications & Impact: His recent publications address solar geoengineering, coastal adaptation strategies, and the economic valuation of climate resilience infrastructure. A recurring theme is the integration of fat-tailed risk distributions into environmental policy design, highlighting the importance of precautionary approaches in the face of potentially catastrophic climate outcomes. Awards & Recognition: Multiple Excellence in Teaching Awards (2020–2024) Roberta Bosey Fullbright Foote Prize for Sustainability (2024) Excellence in Research Award, UM School of Business (2000) William Larimer Mellon Fellowship (1989–1992) Advising & Grants: Kelly has supervised six PhD students to completion and currently advises Kevin Sterling. His funded research includes ULINK grants on coastal resilience and climate gentrification, as well as projects on sustainable banking and environmental regulation. Teaching & Outreach: Beyond his research, Kelly teaches MBA and MS-level courses in managerial economics, game theory, and sustainable business. He is an active contributor to the Association of Environmental and Resource Economists (AERE), having hosted its 2020 summer conference and served on its program committee for over a decade.

Panayotis Mertikopoulosمشاهده پروفایل

استادیار

• Game Theory
• Machine Learning
• Optimization
• +۴ مورد دیگر

Dr. Panayotis Mertikopoulos is a CNRS researcher (chargé de recherche) at the Laboratoire d'Informatique de Grenoble, part of Université Grenoble Alpes. He is affiliated with the Inria/LIG joint team POLARIS and has held visiting positions at UC Berkeley, EPFL, LUISS University of Rome, and NKUA. His academic journey includes completing his PhD at the University of Athens in 2010 on "Stochastic perturbations in game theory and applications to networks" and his Habilitation à Diriger des Recherches (HDR) in 2019 on "Online optimization and learning in games: Theory and Applications". Dr. Mertikopoulos' research spans several interconnected fields at the intersection of mathematics, computer science, and economics. His primary research interests include: Game theory and its applications to network design and resource allocation Online learning algorithms and their convergence properties Optimization methods for non-convex and stochastic problems Applications to machine learning, signal processing, and wireless networks Quantum game theory and quantum computing applications His extensive publication record shows a clear evolution from foundational work in game dynamics and learning theory toward increasingly sophisticated applications in machine learning and network optimization. Recent work demonstrates growing interest in quantum game theory, non-convex optimization, and the mathematical foundations of deep learning. His research consistently bridges theoretical insights with practical applications, particularly in communication networks and distributed systems. Among his notable achievements is receiving the INFORMS best paper award in the network analytics section in 2022 for his work on "Robust power management via learning and game design". His publications have appeared in top venues including NeurIPS, ICML, COLT, IEEE Transactions, and leading economics and operations research journals. Dr. Mertikopoulos has supervised numerous PhD students and postdoctoral researchers, though specific names are not listed in the available information. He has secured research funding for projects at the intersection of game theory, optimization, and machine learning, with applications to network design and resource allocation. His collaborative work spans multiple institutions across Europe and North America. As a member of the POLARIS research team at Inria/LIG, he contributes to a vibrant research environment focused on parallel and distributed systems. His work often intersects with colleagues researching optimization algorithms, machine learning theory, and network science, creating opportunities for cross-disciplinary collaboration on complex computational problems.

Milad Haghaniمشاهده پروفایل

مدرس ارشد

• Transport Safety
• Human Factors in Transport
• Crowd Dynamics
• +۴ مورد دیگر

Dr. Milad Haghani is a Senior Lecturer at the School of Civil and Environmental Engineering , UNSW Sydney , and holds an Australian Research Council (ARC) Discovery Early Career Researcher Award (DE210100440) for his work in crowd evacuation planning. He earned his PhD in Transport Engineering from the University of Melbourne and has held fellowships at the University of Sydney and University of Melbourne. His research spans transport safety, human factors, and meta-science, with particular emphasis on pedestrian dynamics and disaster preparedness. Education: PhD in Transport Engineering, University of Melbourne MSc in Transport Engineering, Sharif University of Technology BSc in Civil Engineering, Iran University of Science and Technology Research Themes: Crowd safety and evacuation modeling Transport psychology and road safety Econometric and choice modeling Meta-research and science of science Disaster mitigation and urban planning Behavioral interventions in safety Scientific Recognition: ARC DECRA Fellow (DE210100440, 2021-2024) His publications (15 most recent) cover topics including pedestrian behavior , crowd accident analysis , emergency evacuation optimization , and transport safety policy . As a DECRA Fellow, he leads projects on behavioral interventions in crowd management. His work intersects transport engineering , human psychology , and disaster response systems , with applications in urban design, public security perception, and emergency logistics. Dr. Haghani contributes to science of science through bibliometric analyses, examining research trends in fields like coronavirus studies , wildfire science , and digital health technologies . His methodological expertise spans discrete choice modeling and simulation-based research , including virtual reality applications for safety training.

Taha Hossein Rashidiمشاهده پروفایل

استاد

• Travel behaviour analysis
• Transportation planning
• Activity-based travel demand modelling
• +۸ مورد دیگر

Professor Taha Hossein Rashidi is a leading expert in Transport Engineering at the School of Civil and Environmental Engineering, University of New South Wales (UNSW), and a member of the Research Centre for Integrated Transport Innovation (rCITI). His work bridges disciplines like economics, statistics, urban design, and sustainability to advance smart-city solutions. Education : PhD, University of Illinois, Chicago (2011); MS Civil Engineering, Sharif University of Technology (2005); BS Civil Engineering, Sharif University of Technology (2003). His research focuses on travel behaviour analysis, activity-based travel demand modelling, integrated land use and transportation models, and autonomous driving technologies. He leads the rCITI Travel Behaviour Modelling Team, which includes 3 Post-docs, 7 PhD, and 3 MSc students. Recent work explores shared autonomous vehicles, social media data integration for transport models, and dynamic ride-sharing systems. His publications span topics like pedestrian demand modeling, residential relocation dynamics, and pandemic-related travel restrictions. Scientific Awards Fred Burggraf Award (TRB, 2008) Dwight Eisenhower Fellow (2008) ASCE Freeman Fellowship (2009) NSERC PDF Award (2012) Industrial RAND Fellowship (2012) Vice Chancellor’s Award for Teaching Excellence (2015, Team) Award for Engineering Education Engagement (2015, Team) Outstanding Paper (TRB Analytics Contest, 2017) He has secured over $1.2 million in research funding since 2007, including ARC DECRA and Linkage Grants. His teaching includes courses on geometric design, urban transport modeling, and transport econometrics.

Fabian Torresمشاهده پروفایل

مدرس

• Transportation Systems
• Operations Research
• Statistical Modeling
• +۳ مورد دیگر

Fabian Torres is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Engineering ( ENAC ). He works in the Transportation and Mobility Laboratory (TRANSP-OR) and SGC-ENS (Civil Engineering Teaching). His research spans transportation systems, operations research, and algorithm design. Research Focus : Modeling driver behavior and decision support in transportation Statistical modeling of transportation systems Algorithm development for pricing and routing optimization Scientific Contributions : Recent work includes continuous pricing algorithms, crowdshipping logistics, stochastic vehicle routing, and elevator dispatching optimization. His publications emphasize integrating advanced discrete choice models with operational challenges in transportation.

Matthias Grossglauserمشاهده پروفایل

استاد

• Machine Learning
• Stochastic Networks
• Discrete Choice Models
• +۴ مورد دیگر

Matthias Grossglauser is a Full Professor at the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he co-directs the Information and Network Dynamics lab. He serves on the Federal Communications Commission (ComCom), Switzerland's telecommunications regulatory authority, and previously directed EPFL's Doctoral School in Computer and Communication Sciences (2016-2019). His career includes positions at Nokia Research Center (Internet Laboratory lead), AT&T Research, and EPFL (Assistant Professor). Education Ph.D. in Computer Science from Sorbonne Universités M.Sc. in Electrical Engineering from Georgia Institute of Technology Engineering degree in Communication Systems from EPFL Research Focus Grossglauser's research integrates machine learning, stochastic networks, and discrete choice models to address challenges in artificial intelligence, network science, computational social sciences, and recommender systems. His work emphasizes both theoretical foundations and practical applications, including political forecasting, climate communication, and network dynamics. Publication Trends Recent articles demonstrate strong focus on causal inference, optimal learning algorithms, and social network analysis. Dominant themes include reinforcement learning optimization, graph-based modeling, and NLP applications in political science. Methodological innovations in matrix factorization, Bayesian modeling, and stochastic processes recur throughout. Awards & Honors Fellow of IEEE and ELLIS Cor Baayen Award (1998) CoNEXT/SIGCOMM Rising Star Award (2006) Best Paper Awards: ACM COSN (2014), IEEE INFOCOM (2001) Nokia Mobile Data Challenge Winner (2012) Academic Leadership Has advised 16+ PhD students to completion and currently supervises 4 doctoral candidates. Secured research funding for projects including dynamic recommender systems, network alignment algorithms, and computational social science tools (e.g., Predikon.ch vote prediction platform). Leads the Information and Network Dynamics lab, focusing on AI-driven network analysis.

• Sustainable Food Consumption
• Sustainable Land Use
• Environmental Economics
• +۶ مورد دیگر

Liesbet Vranken is an Associate Professor at the Department of Earth and Environmental Sciences, Faculty of Bioscience Engineering, KU Leuven. She leads the Society-Environment Interactions Research Group , focusing on land resource scarcity, sustainable food systems, and environmental economics. Research: Sustainable food consumption & production, land use optimization, natural hazard economics, agroforestry, and citizen science Projects: Klimaatmitigatie trajecten , TealHelix , and Socio-economische solidariteitsmechanismes in Uganda/Ethiopia Methodology: Interdisciplinary integration of environmental economics, experimental economics, and spatial analysis Her work emphasizes optimizing land use configurations, guiding consumer behavior, and evaluating policy instruments for sustainable transitions. She supervises doctoral researchers in global projects related to climate resilience and food security.

• Transportation Systems Optimization
• Operations Research
• Machine Learning
• +۷ مورد دیگر

Emma Frejinger is a full Professor at the Department of Computer Science and Operations Research, Faculty of Arts and Science, Université de Montréal. She holds the Canada Research Chair in Demand-Driven Optimization of Transport Systems and the CN Chair in Railway Operations Optimization. Her work bridges operations research and statistical learning to solve large-scale transportation challenges. Ph.D. in Mathematics, École Polytechnique Fédérale de Lausanne, Switzerland Her research focuses on transportation network optimization , demand forecasting , and discrete choice modeling . She develops data-driven methodologies for railway operations, EV charging infrastructure, and traffic prediction, emphasizing scalability and real-world applicability. Recent publications highlight trends in integrating machine learning with combinatorial optimization , particularly for MIPs , competitive facility location , and stochastic transport systems . These works span freight logistics , urban mobility , and reinforcement learning applications . Scientific Awards: Two-time INFORMS Transport Science and Logistics Society best Ph.D. thesis award 2017 Grand Prix d'excellence en transport (freight category) She has supervised 21+ graduate students in topics ranging from locomotive routing to electric vehicle adoption , with projects funded by NSERC, FRQ, and industry partners like CN Rail and Purolator. Her lab affiliations include CIRRELT and OPTIM , focusing on simulation and optimization.

Fiona Sauerbierمشاهده پروفایل

پژوهشگر

• Logistics
• Transport
• Production
• +۴ مورد دیگر

Fiona Sauerbier serves as a Scientific Associate at the Institute for Logistics, Transport and Production within the University of Hamburg Business School. Her research focuses on choice-based optimization under high-dimensional systems, directly contributing to advancements in logistics, transport, and production management through computational modeling and data-driven solutions. Her educational background includes a Master of Science degree. Core research interests encompass Logistics, Transport, Production, Optimization, Machine Learning, Crowd Management, and Traffic Analysis. She develops sophisticated frameworks for resource allocation in complex systems, particularly addressing crowd dynamics and transportation networks during large-scale international events. Recent publications reveal a strong trajectory in applying machine learning to real-time traffic forecasting for mega-events like the FIFA World Cup, while simultaneously advancing theoretical foundations of choice-based optimization in high-dimensional spaces. This dual focus bridges practical event logistics challenges with rigorous mathematical modeling. She operates within the research team led by Univ.-Prof. Dr. habil. Knut Haase, collaborating on projects that integrate optimization theory with transportation engineering applications. Her office is located at Moorweidenstraße 18, Room 2027 in Hamburg, with appointments conducted via email contact.

• Algorithmic Game Theory
• Theory of Computation
• Artificial Intelligence
• +۹ مورد دیگر

Simina Branzei is an Associate Professor of Computer Science at Purdue University, where she joined in Spring 2018. Her research spans multiple areas at the intersection of theoretical computer science, game theory, and economics, with a focus on developing rigorous theoretical frameworks for understanding complex systems. Dr. Branzei's research interests include algorithmic game theory, theory of computation, artificial intelligence, algorithms, learning, computational complexity, and their interfaces with dynamical systems and optimization. Specific topics she has worked on include fair division, markets and auctions, games, learning dynamics, and local search processes. Her research is supported by an NSF CAREER Award, a prestigious grant recognizing early-career faculty who demonstrate exceptional potential for leadership in their field. Analysis of her publication record reveals consistent contributions to understanding computational aspects of economic and social systems. Her work shows particular expertise in computational complexity of fixed point problems, fair allocation mechanisms, market dynamics, and game theoretic models. She has developed novel theoretical frameworks for analyzing cake cutting protocols, market equilibria, and strategic behavior in multi-agent systems. NSF CAREER Award IBM Ph.D. Fellowship Google Anita Borg Memorial Scholarship Dr. Branzei actively mentors graduate students in theoretical computer science, with current Ph.D. student Reed C. Phillips. Her past mentees include Nicholas J. Recker (Ph.D.) and Nithish Kumar (Master's). She has been instrumental in organizing academic workshops including the ACM EC 2024 Mentoring Workshop, Mini-Symposium on Games and Learning at CanaDAM 2023, and Workshop on Fair Division at FOCS 2022, demonstrating leadership in building research communities. She teaches graduate courses including Algorithm Design, Theory of Computation, and Algorithmic Game Theory, as well as undergraduate courses in algorithms and theory of computation, shaping the next generation of computer scientists with strong theoretical foundations.

Salvador Navarroمشاهده پروفایل

استاد

• Applied Microeconomics
• Education
• Crime
• +۳ مورد دیگر

Salvador Navarro is a Professor of Economics and W. Glenn Campbell Fellow at Western University . He is affiliated with the Institute for Research on Poverty and the Center for Demography and Ecology at Wisconsin. Education : A.B. in Economics (ITESM Monterrey, 1996), M.A. in Economics (El Colegio de Mexico, 1996), M.A. and Ph.D. in Economics (University of Chicago, 2001 and 2005). Dr. Navarro specializes in identification methods in applied microeconomics, with research spanning education policy, crime, discrimination, dynamic models, and productivity analysis. His recent work examines grade retention timing and capital punishment deterrence. Current research focuses include: Econometric identification in policy evaluation Dynamic treatment effects modeling Aggregate data analysis in discrete choice models Scientific awards include: W. Glenn Campbell Fellowship

• Environmental economics
• Real Estate Economics
• Urban Economics
• +۲ مورد دیگر

Sho Kuroda is an Assistant Professor at Waseda University 's Faculty of Commerce (non-tenure-track) since October 2023, concurrently serving as a researcher at the Research Institute of Business Administration (RIBA). He earned his Ph.D. in Policy and Planning Sciences from University of Tsukuba in September 2023. His research focuses on applied econometrics , environmental economics , real estate , urban economics , and regional sciences . His work includes discrete-continuous choice models for carbon policy analysis, spatial econometric studies on automobile demand, and computational methods for energy-resilient communities. Recent publications (2021-2025) examine carbon dividend frameworks, transportation emission reductions, and hedonic models for urban view valuation. Articles demonstrate expertise in policy simulation , spatial modeling , and renewable energy integration . He teaches Introductory Econometrics , Basic Statistics , and Data Analysis Using Statistical Packages at Waseda University's Graduate Schools of Commerce and Accountancy. Concurrent roles include developer of educational materials for R/Stata-based statistical analysis and causal inference.

• Machine Learning
• Revenue Management
• Forecasting
• +۳ مورد دیگر

Misuk Lee serves as Associate Professor in the Department of Management at Seattle University's Albers School of Business & Economics. Her expertise spans Machine Learning, Revenue Management, and Forecasting with specialized applications in hospitality and transportation industries. Education: PhD in Industrial Engineering, Georgia Institute of Technology BS and MS in Industrial Engineering, Seoul National University Dr. Lee's research integrates advanced machine learning techniques with business strategy to solve complex revenue management problems. Her work focuses on developing predictive models for hotel demand forecasting, airline booking conversion, and dynamic pricing optimization. Recent publications demonstrate innovative applications of k-Nearest Neighbor algorithms and neural networks to move beyond traditional accuracy metrics in hospitality revenue systems. Scientific Awards: No awards listed in available information Dr. Lee maintains strong industry connections from her tenure as business consultant and data scientist at IBM and Intercontinental Hotels Group. Her teaching in Programming for Business Analytics, Machine Learning, and Capstone Projects bridges academic theory with practical implementation. While specific grant details aren't provided, her publication record indicates active research in revenue management systems with industry collaboration. Though no formal lab is mentioned, Dr. Lee leads research initiatives in business analytics with emphasis on hospitality applications, collaborating with industry partners to develop practical forecasting solutions for real-world revenue management challenges.

• Industrial Organisation
• Competition Economics
• Econometrics of Dynamic Games
• +۳ مورد دیگر

Philipp Schmidt-Dengler is University Professor of Economics at the University of Vienna and Vice-Dean of its Faculty of Business, Economics and Statistics. He is also Research Affiliate of the Centre for Economic Policy Research (CEPR) and Research Fellow of the Mannheim Centre for Competition and Innovation (MaCCI), among other international affiliations. Education Ph.D. in Economics, Yale University, December 2005 M.A. in Economics, Queen’s University, October 2000 B.A. in Economics, University of Vienna, March 1998 Research Interests Professor Schmidt-Dengler’s research lies at the intersection of industrial organisation , applied econometrics and competition policy . A major strand develops structural methods for estimating dynamic oligopoly games and applies them to study entry, investment and technology adoption. A second strand analyses consumer search, price dispersion and the effects of indirect taxation in retail markets. Recent work also evaluates health-care market reforms, auction design and the impact of state aid on firm survival. Scientific Awards & Fellowships Zellner Thesis Award, American Statistical Association (2006) AER Excellence in Refereeing Award (2010) Young Economist Award, Austrian Economic Association (2008) Young Economist Essay Award, E.A.R.I.E. (2005) Carl Anderson Prize Fellowship, Cowles Foundation (2004) Yale and Queen’s University Graduate Fellowships Research Funding “Business Use of Digital Platforms”, OeNB Jubiläumsfonds (2020-23) “Understanding Consumer Search”, Austrian Science Fund (FWF, 2018-21) “Individual Behaviour and Aggregate Economic Outcomes”, FWF Doctoral School (2017-20, Speaker) DFG SFB-TR15 Grant on “Stability, Competition and Liquidity in Financial Markets” (2012-15) ESRC Award on “General Pre-emption Games” (2006-07) Editorial & Professional Service He served as Co-Editor of the International Journal of Industrial Organization (2014-21), Associate Editor of the Review of Economic Studies , Journal of Applied Econometrics , Journal of the European Economic Association and is Editor of the Journal of Industry, Competition and Trade . He has advised the Bank of Canada, the UK Competition Commission and the Austrian Competition Authority.