جست‌وجوی استاد و پژوهشگر

• Sustainability Science
• Human-Environment Modeling
• Complex Systems
• +۷ مورد دیگر

Dr. Wolfram Barfuss is the Argelander Professor of Integrated Systems Modeling for Sustainability Transitions at the University of Bonn, affiliated with the Center for Development Research (ZEF). He is a member of multiple interdisciplinary research areas including TRA Sustainable Futures, TRA Modeling, and TRA Individual and Societies, as well as the Cluster of Excellence PhenoRob and the Center for Earth System Observation and Computational Analysis (CESOC). He also collaborates with the Potsdam Institute for Climate Impact Research and the Earth Resilience Science Unit. His research focuses on understanding whether humanity is 'smart enough for the good life' by developing formal models of collective learning and decision-making in complex social-ecological systems. He integrates methods from complex systems, multi-agent reinforcement learning, and dynamical systems theory to explore sustainability transitions, cooperation, and Earth system resilience. The recent publications demonstrate a strong focus on modeling collective intelligence, cooperation in stochastic games, decision-making under uncertainty, and integrated World-Earth system modeling. His work spans disciplines including computer science, environmental science, game theory, and cognitive science, with frequent contributions to high-impact journals like PNAS , Nature Communications , and Environmental Research Letters . Argelander Professor for Integrated Systems Modeling for Sustainability Transitions Member, TRA Sustainable Futures Member, TRA Modeling Member, TRA Individual and Societies Cluster of Excellence PhenoRob Center for Earth System Observation and Computational Analysis (CESOC) Earth Resilience Science Unit (Potsdam) Earth Resilience and Sustainability Initiative (Princeton-Stockholm-Potsdam) Dr. Barfuss teaches graduate courses at the University of Bonn and Humboldt University Berlin, including Complex System Modeling of Human-Environment Interactions, Economics on Sustainability, Systems Modeling, and Introduction to Agent-Based Modeling. He leads the BarfussLab, where his team develops computational tools such as pyCRLD for modeling collective reinforcement learning dynamics. While specific student advisees are not listed, his lab and publications suggest active supervision and collaboration with early-career researchers. He has not received any explicitly mentioned scientific awards in the provided text. His research is supported through institutional affiliations and collaborative projects rather than individually listed grants.

Nils Wildeمشاهده پروفایل

استادیار

• Robotics
• Autonomous Systems
• Human-Robot Interaction
• +۱۶ مورد دیگر

Nils Wilde is an Assistant Professor in the Faculty of Computer Science at Dalhousie University, Halifax, Canada. He specializes in robotics, AI, and human-computer interaction, with a focus on cognitive robotics, multi-robot systems, and human-robot interaction. His research integrates planning, optimization, control, and machine learning to develop interactive and adaptive robotic systems. His educational background includes: BSc and MSc in Computer Science or related field from Technical University Berlin (2012, 2016) PhD in Electrical and Computer Engineering from the University of Waterloo (2016–2020), co-supervised by Dana Kulić and Stephen L. Smith Postdoctoral Fellow at TU Delft (2021–2024) in the Autonomous Multi-Robots Lab with Javier Alonso-Mora Postdoctoral Fellow at the University of Waterloo’s Autonomous Systems Lab (until August 2021) Nils Wilde's research centers on enabling robots to learn from human feedback and adapt to user preferences in dynamic environments. His work spans preference learning , multi-objective planning , motion planning , task assignment in multi-robot systems , and human-robot interaction . He develops algorithms that allow robotic systems to balance competing objectives such as efficiency, safety, and user comfort, particularly in service robotics applications like hospitals and industrial facilities. His recent publications (2020–2024) demonstrate a strong trajectory in top robotics venues (T-RO, RA-L, ICRA, IROS, CoRL, CDC, WAFR), with a focus on multi-objective optimization, dynamic vehicle routing, sensor scheduling, and learning user preferences. A key theme is improving the quality of service in robotic systems by optimizing metrics like waiting times, statistical distinctness of plans, and user satisfaction, often through novel cost functions and learning frameworks. Nils is actively building a new robotics lab at Dalhousie University, with funded PhD positions and an interdisciplinary research environment. He is involved in organizing academic workshops, such as the upcoming 2025 RSS workshop on Multi-Objective Optimization and Planning in Robotics. He mentors prospective students and encourages applications from diverse backgrounds.

Rekha R. Thomasمشاهده پروفایل

استاد

• Optimization
• Applied Algebraic Geometry
• Computer Vision
• +۳ مورد دیگر

Rekha R. Thomas is a Professor of Mathematics and Undergraduate Program Director at the University of Washington. She holds a Ph.D. in Operations Research from Cornell University (1994), with postdoctoral experience at Yale University and the Konrad-Zuse-Zentrum in Berlin. Her research focuses on optimization, applied algebraic geometry, and computer vision, with contributions to semidefinite programming, graphical designs, and geometric algorithms. She has held distinguished positions such as the Robert R. and Elaine F. Phelps Professorship (2008–2012) and the Robert B. Warfield Jr. Faculty Fellowship (2017–2020). Her work bridges theory and application, addressing challenges in computer vision, combinatorial optimization, and algebraic geometry. Notable contributions include advancements in multiview geometry, kernel learning, and the geometric analysis of rank-deficient matrices. She actively collaborates across disciplines, publishing extensively and supervising numerous graduate students and postdocs. Rekha also engages in academic leadership, mentoring students, and participating in international conferences. Her research has been recognized through invited talks at major events like the International Congress of Mathematicians (2018) and SIAM Annual Meetings. She continues to explore the intersections of algebraic geometry, optimization, and computational methods.

• Machine Learning
• Natural Language Processing
• Medical Informatics
• +۳ مورد دیگر

Stefan Riezler is a full professor of Statistical Natural Language Processing at Heidelberg University's Department of Computational Linguistics (since 2010), affiliated with the Faculty of Mathematics and Computer Science. Prior to this, he worked in Silicon Valley at Xerox PARC and Google Research. He holds a PhD in Computational Linguistics from the University of Tübingen (1998) and conducted postdoctoral research at Brown University (1999). His research spans machine learning, NLP, and medical informatics, focusing on interactive statistical learning. He co-leads the Interdisciplinary Center for Scientific Computing (IWR) and serves on the editorial boards of Computational Linguistics and Transactions of the Association for Computational Linguistics . Key research areas include neural machine translation, healthcare AI (e.g., sepsis prediction), data augmentation, and reproducibility in ML. He develops tools like JoeyNMT and explores ethical challenges in clinical machine learning. Notable recent work includes advancements in time series analysis, multimodal interfaces (e.g., NLMaps for OpenStreetMap), and ethical frameworks addressing validity in healthcare ML. His publications emphasize practical applications of NLP in healthcare, speech translation, and cross-lingual systems. Grants and collaborations include interdisciplinary projects on medical data science and training next-gen NLP researchers. He actively contributes to open-source toolkits and reproducible research practices.

Guillaume Balمشاهده پروفایل

استاد

• Inverse Problems
• Medical Imaging
• Geophysical Imaging
• +۴ مورد دیگر

Guillaume Bal is a Professor at the University of Chicago, holding joint appointments in the Departments of Statistics and Mathematics, and affiliated with the Committee on Computational and Applied Mathematics (CCAM). His research focuses on inverse problems in medical and geophysical imaging, partial differential equations with random coefficients, and the mathematical analysis of topological insulators. He explores applications in wave propagation, uncertainty quantification, and hybrid imaging modalities. His recent work delves into theoretical and computational aspects of stochastic partial differential equations and topological edge states. Research Interests: Inverse Problems (Geophysical/Medical Imaging) Topological Insulators and Edge States Wave Propagation in Heterogeneous Media Uncertainty Quantification Publications Trends: Recent articles emphasize topological insulator dynamics, hybrid imaging techniques, and stochastic PDE models. Key themes include bulk-edge correspondence, dual-energy CT optimization, and semiclassical propagation in curved interfaces.

Cathy Wuمشاهده پروفایل

دانشیار

• Reinforcement Learning
• Machine Learning
• Large-scale Optimization
• +۶ مورد دیگر

Cathy Wu is the Class of 1954 Career Development Associate Professor in Civil and Environmental Engineering at MIT, affiliated with the Institute for Data, Systems, and Society (IDSS). Her research bridges machine learning, optimization, and urban systems, with a focus on mixed autonomy systems in mobility. She holds degrees from MIT (B.S., M.Eng in EECS) and a Ph.D. from UC Berkeley (EECS). Education: B.S. and M.Eng in Electrical Engineering and Computer Science, MIT (2012-2013) Ph.D. in Electrical Engineering and Computer Science, UC Berkeley (2018) Research Interests: Reinforcement Learning and Machine Learning Large-scale Optimization and Control Theory Mobility Systems and Urban Infrastructure Implications of AI and Automation Her work emphasizes interdisciplinary collaboration, involving transportation, computer science, and public policy. She founded the Interdisciplinary Research Initiative within the ACM Future of Computing Academy to advance cross-disciplinary computing research. Key Projects: Includes Flow (open-source RL framework for traffic control), eco-driving incentive mechanisms, and mixed autonomy traffic optimization. Her articles address congestion mitigation, autonomous vehicle integration, and scalable supervision strategies. Awards: Recipient of fellowships, best paper awards, and teaching honors (specific names unlisted). Engagement: Collaborations with institutions like Microsoft Research, OpenAI, and Caltrans. Active in policy-oriented initiatives and education through IDSS programs.

Chuchu Fanمشاهده پروفایل

دانشیار

• Formal Methods
• Control Theory
• Machine Learning
• +۳ مورد دیگر

Chuchu Fan is the Leonardo Career Development Professor and Director of the REALM Lab (REliable Autonomous system Lab) at MIT's School of Engineering , with a primary appointment in the Department of Aeronautics and Astronautics and Laboratory for Information and Decision Systems . Her work bridges formal methods , control theory , and machine learning to ensure safety in autonomous systems. Ph.D., University of Illinois at Urbana-Champaign (2019) B.E., Tsinghua University (2013) Her research focuses on rigorous safety verification of autonomous systems through neural Lyapunov-barrier functions , control contraction metrics , and formal logic specifications . Recent work emphasizes LLM integration for symbolic planning, multi-robot collaboration , and robustness against model uncertainties . The 15 most recent articles highlight trends in safety-critical control using graph neural networks , reinforcement learning , and temporal logic . Key themes include collision avoidance , multi-agent coordination , and runtime safety filters applied to drones, self-driving cars, and microgrids. Scientific Awards & Honors : 2025 ONR YIP Award 2023 NSF CAREER & AFOSR YIP Awards 2020 ACM Doctoral Dissertation Award 2016 Rising Stars in EECS As head of the REALM Lab, she leads projects on autonomous air taxis , safety verification , and neural certificates for robotic systems. Her teaching includes courses on feedback control and formal methods for autonomous systems.

Hesham ElSawyمشاهده پروفایل

استادیار

• Wireless Networks
• Internet of Things (IoT)
• Federated Learning
• +۳ مورد دیگر

Hesham ElSawy is an Assistant Professor in the Department of Systems and Networks at the School of Computing, Faculty of Arts and Science, Queen's University. His work focuses on advancing next-generation wireless systems, with particular emphasis on federated learning, IoT networks, and edge computing. He is affiliated with Ingenuity Labs Research Institute, Queen's University, and contributes to interdisciplinary research at the intersection of communication theory and network optimization. His research interests span stochastic geometry modeling, energy-efficient protocols for massive IoT deployments, and resilient federated learning frameworks. ElSawy explores novel paradigms in aerial wireless networks, UAV-assisted communication, and network security through percolation theory applications. Recent publications highlight his contributions to system-level analysis of parallel computing at extreme edges, UAV-enabled federated learning architectures, and energy-as-a-service models for RF-powered networks. His work emphasizes practical implementations and large-scale network validation. ElSawy holds no listed awards or grants in the provided text but maintains active collaborations through Ingenuity Labs. His research addresses critical challenges in 5G/6G networks, including latency optimization, resource allocation, and heterogeneous network integration.

Hamed Rahimianمشاهده پروفایل

استادیار

• Stochastic Optimization
• Data-Driven Optimization
• Risk-Averse Optimization
• +۳ مورد دیگر

Dr. Hamed Rahimian is an Assistant Professor in the Department of Industrial Engineering at Clemson University. He holds a Ph.D. in Industrial and Systems Engineering from The Ohio State University (2018), an M.Sc. from The University of Arizona (2012), and B.Sc./M.Sc. degrees from Sharif University of Technology (2008/2011). Prior to Clemson, he was a Postdoctoral Research Fellow at Northwestern University under Prof. Sanjay Mehrotra. His research focuses on data-driven decision-making under uncertainty, including stochastic optimization, distributionally robust optimization, and risk-averse methodologies. He has published in top journals such as Mathematical Programming , SIAM Journal on Optimization , and Operations Research . Education: Ph.D. Industrial and Systems Engineering, The Ohio State University (2018) M.Sc. Industrial Engineering, The University of Arizona (2012) B.Sc./M.Sc. Industrial Engineering, Sharif University of Technology (2008/2011) His research has been recognized with awards including the Harold W. Kuhn Award (2022), Runner-Up INFORMS Computing Society Student Paper Award (2017), and 2nd Place IISE Pristker Dissertation Award (2019). He serves as an Associate Editor for INFORMS Journal on Computing and Sharif Journal of Industrial Engineering & Management . Grants & Advising: He secured an Air Force grant (2024) on multistage stochastic programming. His research group focuses on advancing optimization under uncertainty with applications in healthcare, energy, and supply chains. Labs/Teams: Leads a research group in Clemson’s Industrial Engineering department, collaborating on projects in distributionally robust optimization and data-driven decision-making.

Wooram Parkمشاهده پروفایل

دانشیار

• Robotics
• Biomedical Robotics
• Computational Structural Biology
• +۳ مورد دیگر

Wooram Park is an Associate Professor in the Department of Mechanical Engineering at the University of Texas at Dallas (UT Dallas), affiliated with the Erik Jonsson School of Engineering and Computer Science. He leads the Robotics and Intelligent Systems Laboratory (ROBINS Lab) and holds a PhD from Johns Hopkins University (2008), along with MS and BS degrees from Seoul National University (2003 and 1999). His research focuses on robotics, biomedical robotics, computational structural biology, and image processing. Key projects include flexible needle steering for medical applications, haptic feedback systems, and advanced algorithms for motion planning and image reconstruction. He has received notable awards such as the Creel Fellowship (2007) and Critics’ Choice Award in ArtBot Design (2004). His work spans theoretical contributions in stochastic systems and practical innovations like vibratory magnetic robots (Vimbot) and wearable haptic devices. The ROBINS Lab emphasizes interdisciplinary research at the intersection of mechanical engineering, computer science, and biomedical applications.

Suresh Khatorمشاهده پروفایل

استاد

• Modeling of Energy Systems
• Health Care Systems
• Manufacturing Systems
• +۳ مورد دیگر

Suresh Khator is a Professor and Senior Associate Dean for Graduate Programs and Computing Facilities at the University of Houston's Cullen College of Engineering, Department of Industrial and Systems Engineering. He holds a Ph.D. in Industrial Engineering from Purdue University and has extensive experience in academia, industry, and administrative roles. His research focuses on modeling energy, healthcare, and manufacturing systems, facilities design, production planning, and project management. He has supervised 10 PhD dissertations and 37 Master's theses, and his work has been published in top-tier journals and conferences. Khator has received numerous awards, including the Fellow of IISE and multiple teaching excellence awards. His administrative roles include Director of Engineering Computing and Interim Associate Dean at the University of South Florida. He has also contributed to community initiatives, such as science fairs and outreach programs. His articles span power systems resilience, renewable energy integration, and manufacturing automation. He has led over 30 grants and contracts, including projects with the Florida Department of Transportation and utility companies.

Tingkai Wangمشاهده پروفایل

مدرس ارشد

• Mobile Robots
• Intelligent Systems
• Artificial Intelligence (AI)
• +۵ مورد دیگر

Dr. Tingkai Wang is a Senior Lecturer in the School of Computing and Digital Media at London Metropolitan University. His research focuses on mobile robots, intelligent systems, artificial intelligence, control systems, image/signal processing, and virtual reality. He teaches the Programming for Computer Science module and has led projects like the Virtual Environment and Simulation System (2000-2002) and Navigation and Control of Mobile Robots (1995-1998). His work emphasizes interdisciplinary approaches, combining expert systems, neural networks, and fuzzy logic to address challenges in autonomous systems. Notable contributions include AGV navigation algorithms, hybrid control systems, and predictive modeling. Over 30 publications span robotics, control engineering, and AI applications. He collaborates internationally and has presented at venues like the International Conference on Intelligent Systems Engineering and the IEEE Conference on Engineering in Medicine and Biology. Dr. Wang’s expertise bridges theoretical modeling and practical implementation, with applications in manufacturing automation, environmental monitoring, and industrial management systems. His current research continues exploring adaptive control mechanisms and AI-driven robotics solutions.

• Additive Manufacturing
• Metamaterials
• Energy Absorption
• +۵ مورد دیگر

Dr. Ahmad Baroutaji is a prominent researcher at Aston University's School of Engineering and Technology, specializing in Additive Manufacturing, Metamaterials, and Energy Systems. His work focuses on advancing materials science for biomedical, acoustic, and energy applications. He holds a strong academic affiliation within the College of Engineering and Physical Sciences. Research Interests: Optimizing 3D-printed metamaterials for energy absorption and crashworthiness Development of advanced materials for fuel cells and hydrogen technologies Acoustic metamaterials for noise reduction in buildings Biomaterials for orthopedic and tissue engineering applications Notable contributions include pioneering studies on cobalt-chromium-molybdenum meta-scaffolds for bone reconstruction and acoustic panels using titanium perforated structures. His 2024 review on PEM hydrogen technologies has been widely cited. Collaborations span global institutions, emphasizing practical material innovations. Grants and Advising: While specific grants are not detailed, his extensive publication record indicates sustained research funding. No formal advisee list is provided, but co-authorships suggest collaborative mentorship. Labs/Teams: Engaged in multidisciplinary teams focusing on additive manufacturing applications, though specific lab names are not mentioned in the text.

• Macroeconomics
• Public Finance
• Contract Theory
• +۳ مورد دیگر

Max Troshkin is an Associate Professor of Economics at the University of Exeter Business School. He holds a Ph.D. in Economics from the University of Minnesota, advised by V.V. Chari and Larry E. Affiliations: University of Exeter Business School, Policy @Exeter Research Network Education: Ph.D. in Economics, University of Minnesota Research Interests: Focuses on macroeconomic and contracting aspects of public policies, including optimal taxation, social insurance, and pension systems. His work bridges micro and macroeconomic frameworks to address policy challenges in fiscal theory, public finance, and economic inequality reduction. Awards: Presidential Merrill Scholar Mentor Award (Cornell University) Edmund S. Muskie Fellowship (U.S. Department of State) Professional Contributions: Organizes research conferences, referees for leading journals (MIT Press, NSF), and contributes to sustainable development goals (SDG 10: Reduced Inequalities; SDG 16: Peace, Justice & Institutions). Engages in policy-related advising and committee memberships. Labs/Teams: Active in Policy @Exeter, a research network addressing public policy challenges through interdisciplinary collaboration.

Asu Ozdaglarمشاهده پروفایل

استاد

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

Asu Ozdaglar is the EECS Department Head and MathWorks Professor at MIT, serving as Deputy Dean of Academics in the MIT Schwarzman College of Computing. Her research bridges optimization theory, machine learning, and network science with societal implications, focusing on AI ethics, data-driven decision systems, and strategic interactions in networked environments. Her technical contributions include foundational work on large-scale optimization algorithms (e.g., distributed methods, first-order methods), game-theoretic models for network systems, and federated learning frameworks. Recent work addresses critical societal challenges like misinformation dynamics, data market inefficiencies, and algorithmic fairness in AI systems. Publications from 2023-2025 highlight advancements in graphon-based network game analysis, privacy-preserving data mechanisms, and multi-agent learning dynamics. She co-leads initiatives in MIT's AI+D program, emphasizing interdisciplinary education and ethical AI development. Notable institutional roles include oversight of MIT's computing education strategy and contributions to pandemic-related research on infection control through testing optimization. Her work integrates technical rigor with policy-relevant insights, influencing both academic and real-world systems.