جست‌وجوی استاد و پژوهشگر

Sarah Deanمشاهده پروفایل

استادیار

• Machine Learning
• Data-Driven Control
• Decision-Making Systems
• +۶ مورد دیگر

Sarah Dean is an Assistant Professor in the Computer Science Department at Cornell University, affiliated with the College of Engineering. Her research focuses on the interplay of machine learning, optimization, and dynamics in real-world systems, particularly in control theory, recommendation systems, and ethical AI. Education: PhD in EECS, University of California, Berkeley (2021) Postdoctoral Research, University of Washington (2021-2022) Research Interests: Data-driven control systems, reinforcement learning, recommendation systems, user dynamics, algorithmic fairness, and the societal impacts of AI. She emphasizes foundational understanding of how learning systems interact with human and social processes. Recent Work Trends: Her articles explore topics like bilinear system identification, user participation dynamics in recommendation platforms, and ethical considerations in AI development. Recent work includes harm mitigation strategies and mathematical modeling of AI-human feedback loops. Awards: AI2050 Early Career Fellow (2024) Best Paper at ICML 2018 (Delayed Impact of Fair Machine Learning) Best Student Paper in Imaging Systems (OSA Congress 2018) Advising & Labs: Advises over 15 graduate and undergraduate students. Leads research on interactive ML systems, with contributions to projects like the 'MSGD' repository for streaming data learning. Active in the GEESE group, promoting socially responsible computing.

Na Duمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Human Factors in Smart City Technologies
• Human-Centered Computing
• +۴ مورد دیگر

Na Du is an Assistant Professor in the Department of Informatics and Networked Systems at the University of Pittsburgh's School of Computing and Information. She holds a PhD in Industrial & Operations Engineering from the University of Michigan (2021) and a Graduate Certificate in Data Science. Her research focuses on human factors in smart cities, human-centered computing, and user experience design. She is affiliated with the Intelligent Systems Program, Pitt Cyber, and the Center for Governance and Markets. Education: PhD in Industrial & Operations Engineering (University of Michigan, 2021); Undergraduate in Psychology (Zhejiang University). Research emphasizes explainable AI, human-AI teaming, and smart technologies. Recent grants include funding from Honda Research Institute and Pitt Cyber Accelerator for projects on emotions in Human-AI interaction and Metaverse privacy awareness. Her work has been recognized with awards like the HFES Best Paper Award and the IOE Outstanding Student Award. Advising includes PhD students and researchers in human factors and UX design. The HAT Lab under her leadership explores interdisciplinary challenges in human-computer interaction and smart systems.

Meng Liمشاهده پروفایل

استاد

• Human-Centered AI
• Managerial Behavior
• Human-Algorithm Interaction
• +۵ مورد دیگر

Meng Li is a Professor and the Endowed C. T. Bauer Chair of AI at the C. T. Bauer College of Business, University of Houston. He also serves as the founding Director of the Bauer Human-Centered AI Institute and the SCM PhD coordinator, highlighting his leadership in AI and operations research. His research centers on human-centered AI , managerial behavior , and human-algorithm interaction , with applications in supply chain management, operations, and strategic decision-making. His work explores cognitive biases, behavioral operations, and the integration of AI in business contexts. The 15 most recent publications demonstrate a consistent focus on behavioral aspects of operations management, particularly overconfidence, regret, and trust in AI systems. The research spans journals such as Management Science , Production and Operations Management , and Strategic Management Journal , indicating interdisciplinary impact across operations, strategy, and information systems. Scientific Awards and Honors: The President’s Circle Awards, University of Houston, 2025 Energy Transition Institute Award ($62.5K), 2024 John Aure Buesseler Distinguished Lectureship, Texas Tech, 2024 Shell Sustainability Research Grant ($100K), 2022 Management Science Distinguished Service Award, 2022 Rutgers Research Council Award, 2020 Finalist for INFORMS Best Service Science Paper Award, 2019 Meng Li advises PhD students as SCM PhD coordinator and leads major research initiatives through the Bauer Human-Centered AI Institute . His work is supported by grants from Shell, HPE, and NIST. He serves as Department Editor for Journal of Operations Management and Decision Sciences Journal , and Senior Editor for Production and Operations Management , and has guest-edited AI-focused special issues in top journals. He is actively building interdisciplinary teams at the intersection of AI, operations, and behavioral science, fostering collaborations across business, engineering, and health domains.

Emilio Frazzoliمشاهده پروفایل

استاد

• Autonomous Robotics
• Control Systems
• Game Theory
• +۴ مورد دیگر

Emilio Frazzoli is a Full Professor at ETH Zurich’s Department of Mechanical and Process Engineering. He leads the Institute for Dynamic Systems and Control and the Center for Sustainable Future Mobility, focusing on autonomous systems, robotics, and socio-technical control frameworks. Current affiliations: ETH Zurich (Dynamic Systems and Control, Sustainable Future Mobility) Research: Autonomous mobility-on-demand, game theory for resource allocation, and safety verification in multi-agent systems His work bridges robotics, control theory, and economics, with projects like the open-source AMoDeus simulation framework for autonomous taxis and karma-based resource allocation systems. Recent publications emphasize trustworthy AI, reproducibility in autonomous vehicle control, and human-robot interaction challenges. Notable projects include nuReality (VR-based pedestrian interaction studies) and CARSI II (context-driven vehicle interfaces).

Roy Dongمشاهده پروفایل

استادیار

• Control Theory
• Cyber-Physical Systems
• Machine Learning
• +۷ مورد دیگر

Roy Dong is an Assistant Professor at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory. His research bridges Control Theory Economics Statistics Optimization to address challenges in cyber-physical systems and the Internet of Things, focusing on data manipulation, privacy, and strategic behavior in interconnected systems. His academic journey includes a Ph.D. in Electrical Engineering and Computer Sciences from UC Berkeley (2017) and dual B.S. degrees in Economics and Computer Engineering from Michigan State University (2010). At Illinois, he teaches courses ranging from Control Systems to Convex Optimization , with multiple teaching excellence awards. Roy's research explores Closed-loop effects of machine learning Causality in decision systems Incentive design for strategic agents Privacy-utility tradeoff optimization Human behavior modeling with applications in smart grids, transportation networks, and semi-autonomous vehicles. His work formulates privacy-preserving mechanisms as optimization problems, balancing data utility against user privacy in dynamic systems. Article trends show expertise in Game theory for strategic data sources Energy disaggregation techniques Nonlinear basis pursuit algorithms Privacy-aware control systems with a focus on cyber-physical systems and human-in-the-loop applications. Scientific recognition includes 'Teacher Ranked as Excellent' awards (ECE 120, ECE 486, ECE 515) Contributions to smartSDH building control and CPRL compressive sensing Roy leads the Privacy-aware Control Systems research group, collaborating with institutions like UC Berkeley and Michigan State University , and directs projects funded by grants including the New USDA NIFA grant for agricultural robot autonomy .

Liu Yangمشاهده پروفایل

دانشیار

• Urban Mobility
• Transportation Systems
• Traffic Congestion Management
• +۳ مورد دیگر

Dr. Liu Yang is an Associate Professor jointly appointed in the Department of Civil and Environmental Engineering and the Department of Industrial Systems Engineering and Management at the National University of Singapore (NUS). She holds a B.S. from Tsinghua University, an MPhil from the Hong Kong University of Science and Technology, and a Ph.D. from Northwestern University. Her research focuses on urban mobility and transport systems, including ridesharing, carsharing, traffic congestion management, and data-driven modeling. She leads the Lab for Urban Mobility Systems (LUMOS), comprising 15 members. Dr. Liu serves on editorial boards of journals like Transportation Science and Transportation Research Part C, and holds leadership roles in professional organizations such as the Chinese Overseas Transportation Association (COTA). Her work has been published in top journals including Transportation Research Part A/B/C/E and Transportation Science. Key awards include the CICTP Best Area Editor Award (2022) and the Faculty Teaching Excellence Award (2022). Her research is funded by agencies like the US Federal Highway Administration and Singapore's Ministry of Education. Education: B.S., Civil Engineering, Tsinghua University (2005) M.Phil., Civil Engineering, Hong Kong University of Science and Technology (2007) Ph.D., Transportation System Analysis and Planning, Northwestern University (2013) Professional Activities: Associate Editor, Transportation Science Co-Chair, WTC Shared Logistics and Transportation Systems Committee Member, Transportation Research Board Committee AP020 and AEP40 Her publications emphasize optimization, dynamic systems, and policy analysis in transportation networks. Recent work explores autonomous vehicles, incident-responsive traffic management, and shared mobility systems. She advises students on topics like ridesharing algorithms and congestion pricing strategies.

• Game Theory
• Algorithmic Game Theory
• Congestion Games
• +۹ مورد دیگر

Alexander Skopalik is an Assistant Professor in the Mathematics of Operations Research department, specializing in Game Theory, Congestion Games, and Algorithmic Game Theory. His work explores Strategic Resource Allocation, Equilibrium Analysis, and Network Games, with significant contributions to multi-agent systems and facility location optimization. Key research areas: Congestion Games Facility Location Nash Equilibrium Strategic Resource Allocation Algorithmic Game Theory His recent publications focus on equilibrium dynamics in facility location, battery charging games, and strategic resource allocation, emphasizing the interplay between theoretical guarantees and practical applications in AI and mobility systems. He actively participates as a committee member in leading conferences such as IJCAI and AAMAS.

Zhengtian Xuمشاهده پروفایل

استادیار

• Urban Transportation Systems
• Mathematical Modeling
• Operations Research
• +۳ مورد دیگر

Zhengtian Xu is an Assistant Professor in the Department of Civil & Environmental Engineering at George Washington University , affiliated with the School of Engineering & Applied Science . His research focuses on optimizing urban transportation systems through mathematical modeling and data-driven analysis, emphasizing emerging mobility services and vehicle technologies. Education: B.S., Tsinghua University (2014) M.S., University of Florida (2016) Ph.D., University of Michigan (2020) Research interests include urban transportation systems, mathematical modeling, operations research, and sustainable mobility solutions. His work addresses challenges such as electrification of ride-hailing services, driver labor dynamics, and last-mile delivery optimization. Recent publications explore topics like drone delivery networks and platform competition in autonomous vehicle markets. No academic awards or student advisees are listed. His articles highlight trends in electrification strategies, behavioral driver dynamics, and urban logistics optimization. He has not disclosed grants or lab affiliations in the provided information.

Nima Monshizadeh Nainiمشاهده پروفایل

استاد

• Cyber-Physical Human Systems (CPHS)
• Control Theory
• Optimization
• +۵ مورد دیگر

Nima Monshizadeh Naini is a Professor in the Faculty of Science and Engineering at the University of Groningen. He holds the position of Chair of the IEM Program Committee and serves on the boards of ENTEG and YSEN. His academic journey includes a PhD in Control Systems from the University of Groningen (2013, Cum Laude), followed by postdoctoral research at the University of Cambridge (2016-2017) and the University of Groningen (2014). He has been an Assistant Professor since 2018 and was awarded the NWO Open Competition Grant in 2021. His research focuses on Cyber-Physical Human Systems (CPHS) , addressing two core areas: (1) Coordination of self-interested users in power systems and traffic networks using dynamic information design and game-theoretic mechanisms; and (2) Privacy-aware control and optimization with tailored encryption methods for dynamic systems. Applications include energy markets, microgrids, and smart grids. He has published extensively in top venues like IEEE CDC and Automatica, with key topics including privacy-preserving algorithms, distributed control, and optimal intervention design. His work contributes to UN Sustainable Development Goals related to affordable energy and responsible consumption. Education: PhD in Control Systems, University of Groningen (2013) Research Associate, University of Cambridge (2016-2017) Postdoctoral Researcher, University of Groningen (2014) Awards: Cum Laude distinction for PhD thesis (2013) NWO Open Competition Grant (2021) Advising & Grants: Supervised 5 PhD students Editorial roles in IEEE journals and conference proceedings Labs/Teams: Leading the Cyber-physical systems group within the Smart Manufacturing Systems institute.

Xiaolei Guoمشاهده پروفایل

استاد

• Transportation
• Supply Chain Management
• Congestion Pricing
• +۳ مورد دیگر

Xiaolei Guo is a Professor of Management Science at the Odette School of Business, University of Windsor. His expertise spans transportation systems, supply chain optimization, and infrastructure pricing strategies. He holds a Ph.D. in Transportation from the Hong Kong University of Science and Technology (2008) and a B.A. in Hydraulic Engineering from Tsinghua University (2004). His research focuses on congestion pricing mechanisms, transportation network modeling, and toll road economics. Notable areas include optimizing toll designs under uncertainty, analyzing autonomous vehicle impacts on ridesharing, and evaluating strategic distribution channel decisions under environmental regulations. Dr. Guo has contributed to over 30 peer-reviewed articles since 2005, exploring topics such as Braess paradox under bounded rationality, Pareto-improving congestion schemes, and profit maximization for private toll roads. His work bridges theoretical optimization with practical policy applications. He is an active member of the Canadian Transportation Research Forum (CTRF) since 2011 and the Institute for Operations Research and the Management Sciences (INFORMS) since 2009.

Manxi Wuمشاهده پروفایل

استادیار

• Societal Networks
• Game Theory
• Network Optimization
• +۴ مورد دیگر

Manxi Wu is an Assistant Professor in Cornell University's School of Operations Research and Information Engineering, specializing in societal networks and game-theoretic approaches to system design. Her research develops computational models for strategic learning and incentive mechanisms in socio-technical systems, with applications to transportation networks and digital platforms. Education: B.S. Applied Mathematics, Peking University (2015) M.S. Transportation, Massachusetts Institute of Technology (2017) Ph.D. Social and Engineering Systems, Massachusetts Institute of Technology (2021) Her research integrates game theory, optimization, and machine learning to address challenges in autonomous services, traffic management, and decentralized decision-making. Current investigations focus on adaptive incentive structures, spatial resource allocation, and equilibrium analysis in complex networked environments. Publication analysis reveals consistent emphasis on game-theoretic frameworks applied to urban mobility systems, with recent work exploring multi-agent reinforcement learning, congestion pricing equity, and electric fleet management. Methodological innovations include novel convergence proofs for decentralized algorithms and computational approaches to fairness constraints. Awards and Honors: Hammer Fellowship UTC Milton Pikarsky Memorial Award Siebel Scholarship EECS Rising Star recognition No information is currently available regarding student advising, research grants, or laboratory affiliations.

Zhenyu Yangمشاهده پروفایل

مدرس

• Urban Transportation Network Modeling
• Travel Demand Management
• Transport Economics
• +۵ مورد دیگر

Zhenyu Yang is a Lecturer and Postdoctoral Researcher at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Engineering through the Department of Civil Engineering and the Urban Transport Systems Laboratory (LUTS) . He holds a PhD in Industrial System Engineering from the National University of Singapore (2022), an M.Eng from Beijing Jiaotong University, and a Diploma in Transportation Engineering from Huazhong University of Science and Technology. PhD, Industrial System Engineering, National University of Singapore (2022) M.Eng, Beijing Jiaotong University Diploma, Transportation Engineering, Huazhong University of Science and Technology His research focuses on urban transportation network modeling , travel demand management , and traffic information provision , with a strong emphasis on handling uncertainty and optimizing shared mobility systems. Recent work explores reinforcement learning applications, vehicle-drone cooperative delivery , and dynamic incident-responsive traffic systems . His publications highlight advancements in ridesourcing algorithms , congestion pricing , and multi-modal transport regulation . As a lecturer, he teaches Transportation Economics , covering demand-supply dynamics, welfare analysis, and environmental policy in transport systems. He is affiliated with EPFL's Urban Transport Systems Laboratory (LUTS) and contributes to the SGC-ENS teaching unit.

Zhibin Chenمشاهده پروفایل

استادیار

• Transportation Network Modeling and Optimization
• Intelligent Transportation Systems
• Discrete Optimization
• +۴ مورد دیگر

Zhibin Chen is an Assistant Professor of Engineering at NYU Shanghai and concurrently a Global Network Assistant Professor within the broader New York University system. Since January 2019 he has led research and teaching activities at the Division of Engineering and Computer Science in Shanghai, while maintaining university-wide collaborations through his Global Network appointment. Education Ph.D. in Transportation Engineering, University of Florida (2017) Research Interests Dr. Chen’s scholarship centres on Transportation Network Modeling and Optimization , Intelligent Transportation Systems , and Discrete Optimization . He integrates operations research, data science, and engineering to address emerging challenges in electric mobility, autonomous vehicles, and large-scale urban networks. Recent thrusts include: Data-driven analytics of electric-vehicle charging behaviour under usage heterogeneity. Optimization of charging and swapping infrastructure for electric buses and trucks. Network-level deployment and control strategies for connected and automated vehicles. Day-to-day traffic dynamics and equilibrium models with elastic demand. Pricing, policy, and incentive design for sustainable transportation systems. Scientific Awards Stella Dafermos Best Paper Award – awarded at the 95th Transportation Research Board Annual Meeting. Ryuichi Kitamura Paper Award – also conferred at the 95th TRB Annual Meeting. Editorial & Professional Service Dr. Chen currently serves on the Editorial Advisory Board of Transportation Research Part C: Emerging Technologies , shaping the editorial direction of the leading journal in his field. Grants & Collaborations While specific grant identifiers are not disclosed in the provided text, Dr. Chen’s extensive publication record in top-tier journals ( Transportation Science , Transportation Research Parts B, C, D , IEEE ITS , Applied Energy ) and his editorial role indicate sustained research funding and active collaboration with international partners across North America and China. Laboratories & Teams Operating within the Division of Engineering and Computer Science at NYU Shanghai , Dr. Chen leads a research group focused on next-generation mobility analytics, leveraging the university’s interdisciplinary ecosystem and NYU’s Global Network resources to advance smart and sustainable transportation.

Lillian Ratliffمشاهده پروفایل

دانشیار

• Game Theory
• Optimization
• Machine Learning
• +۳ مورد دیگر

Lillian Ratliff is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Washington, holding additional Adjunct Associate Professor positions in the Allen School of Computer Science and Engineering and the Department of Aeronautics and Astronautics. She specializes in research at the intersection of game theory, optimization, machine learning, and control theory, with a focus on decision-making in intelligent systems with learning-enabled components and strategic agents. Her research interests span game theory and economics, optimization, machine learning, and control theory. She develops theoretical frameworks for understanding how intelligent systems make decisions when interacting with strategic agents. Her work bridges theoretical foundations with practical applications in multi-agent learning systems, where she examines how learning algorithms converge to equilibria in strategic environments. She has made significant contributions to understanding the dynamics of gradient-based learning in games, Stackelberg games, and decision-dependent distributions. Her recent publications demonstrate a strong focus on theoretical foundations of game-theoretic learning, with particular attention to convergence properties, equilibrium analysis, and strategic behavior in multi-agent systems. Her work spans zero-sum games, matrix games, Stackelberg games, and decision-dependent learning scenarios, with applications across multiple domains including human-machine interaction and networked systems. NSF Graduate Research Fellowship (2009) NSF CISE Research Initiation Initiative award (2017) NSF CAREER award (2019) ONR Young Investigator award (2020) UW CoE Junior Faculty Award (2021) Invited speaker at NAE China-America Frontiers of Engineering Symposium (2019) Dhanani Endowed Faculty Fellowship (2020) Professor Ratliff's research is supported by multiple NSF grants (current: CNS-1736582, CNS-1836819, CNS-1931718, CNS-1907907, CNS-1844729, CNS-1952011; previous: CNS-1634136, CNS-1646912, CNS-1656873) and an Office of Naval Research Young Investigator award. She has mentored numerous students and collaborators across her extensive publication record, with work appearing in top venues including NeurIPS, ICML, AISTATS, and IEEE conferences.

• Smart Grids
• Renewable Energy Integration
• Optimization of Power Systems
• +۴ مورد دیگر

Cuo Zhang is a Lecturer of Power Engineering and ARC DECRA Fellow at the University of Sydney's School of Electrical & Computer Engineering. He holds a B.E. (Hons.) from the University of Sydney (2014) and a Ph.D. in Electrical Engineering from UNSW (2018). His research focuses on smart grids, renewable energy integration, voltage control, and optimization of power systems. Key interests include distributed generation planning, energy storage systems, and demand response mechanisms. Dr. Zhang leads research on enhancing distribution network resilience through advanced control strategies and participates in the Net Zero Institute. He has secured grants such as the 2024 ARC DECRA project on renewables hosting capacity. Supervised students include Yunqing Zhang, working on robust renewables integration. His recent publications emphasize data-driven approaches, decentralized energy trading, and adaptive control for unbalanced networks. He explores machine learning applications in energy management and stochastic optimization under uncertainty. Awards include the University Medal for academic excellence. Awards: ARC DECRA Fellow Grants: 2024 Robust Renewables Hosting Capacity Enhancement Labs/Teams: Member of the Net Zero Institute