جست‌وجوی استاد و پژوهشگر

Shenhao Wangمشاهده پروفایل

استادیار

• Urban Science
• Deep Learning
• Choice Modeling
• +۷ مورد دیگر

Shenhao Wang is an Assistant Professor in Artificial Intelligence at the University of Florida and the Director of the Urban Artificial Intelligence (AI) Lab. His work bridges urban science and computer science to address challenges in resilient urban systems, travel behavior modeling, and generative AI-driven urban design. Education Ph.D. in Computer and Urban Science, Massachusetts Institute of Technology (2020) M.S. in Transportation and City Planning, MIT (2017) B.A. in Economics, Peking University (2014) B.A. in Architecture and Law, Tsinghua University (2012) Research Interests Dr. Wang focuses on three core themes: Resilient and Equitable Urban Systems : Using network theory and deep learning to model city dynamics and design equitable infrastructure. Travel Behavior Analysis : Integrating choice models with deep learning to predict mobility patterns and urban activity decisions. Generative AI in Urban Design : Automating urban planning tasks (e.g., land use, mobility patterns) using diffusion models and multimodal AI. His research has been funded by the Department of Energy (DOE), Singapore-MIT Alliance for Research and Technology (SMART), and industry partners. Grants & Labs He leads the Urban AI Lab at the University of Florida, advancing interdisciplinary projects at the intersection of AI and urban systems. Current grants include DOE-funded studies on energy transition impacts and equitable urban prediction models.

Adeel Abbasiمشاهده پروفایل

استادیار

• Machine Learning
• Critical Care Medicine
• Bioinformatics
• +۳ مورد دیگر

Adeel Abbasi, MD, ScM, is an Assistant Professor of Medicine in the Division of Pulmonary, Critical Care, and Sleep Medicine at Brown University's Warren Alpert School of Medicine. He also serves as the Associate Medical Director of the Adult Extracorporeal Life Support Program at Rhode Island Hospital. His research focuses on leveraging machine learning and bioinformatics to improve outcomes in critically ill patients undergoing mechanical circulatory support, particularly extracorporeal membrane oxygenation (ECMO). His work is supported by the Parker B. Francis Fellowship (2022-2025), the CardioPulmonary Vascular Biology Center of Biomedical Research Excellence (CPVB COBRE), and the Advance Clinical and Translational Research (Advance-CTR) Program. Dr. Abbasi holds degrees from the University of Rochester (MD, 2007), Brown University (ScM, 2022), and Northwestern University (BA, 2001). His affiliations include the Health Natural Language Processing Laboratory , Brown Pulmonary, Critical Care & Sleep Medicine , and the Brown-Lifespan Center for Digital Health . His research emphasizes predicting complications such as hemorrhage and thrombosis in ECMO patients, using machine learning to analyze large datasets from registries like ELSO. He has contributed to developing predictive models for neurological injury in ECMO patients and optimizing fluid management protocols in septic shock using ultrasound-based scoring systems. Key awards include the Parker B. Francis Fellowship, recognizing his innovative research in pulmonary and critical care medicine. His grants support translational research aimed at bridging bioinformatics and clinical practice to enhance patient care.

Sven Hoffmannمشاهده پروفایل

پژوهشگر

• Augmented Reality
• Cyber-Physical Production Systems
• Knowledge and Expertise Sharing
• +۴ مورد دیگر

Sven Hoffmann is a Researcher at the Department of Business Information Systems and New Media within the Faculty of Information Systems and New Media at the University of Siegen. He has been working at the university since 2016 and is currently involved in the EU- and NRW-funded project Cyberrüsten 4.0, where he leads the preliminary development of a cyber-physical system to support machine operators during the complex process of machine setup. His work bridges academic research with practical industrial applications. Hoffmann completed his studies in Mechanical Engineering at the University of Siegen. After graduation, he worked at a medium-sized company as a process developer before returning to academia. This industry experience informs his practical approach to research problems in manufacturing contexts. Hoffmann's research focuses on the industrial application of mobile computing, particularly in the areas of augmented reality and cyber-physical production systems. His work examines how digital technologies can support knowledge and expertise sharing in manufacturing contexts, with special attention to industrial setup processes. He investigates how augmented reality can bridge spatial and temporal distances between experts and non-experts, enhance autonomy, and facilitate cooperation in production environments. His research combines ethnographic studies with design case studies to develop practical AR-based solutions for complex manufacturing challenges, with an increasing focus on knowledge management and workplace integration. Hoffmann's publication record demonstrates a consistent progression from technical implementations of AR systems to examining their broader social and organizational impacts in manufacturing settings. His recent work shows a growing emphasis on integrating emerging technologies like large language models with AR systems to address complex knowledge management challenges in industrial contexts, particularly around standards compliance and workplace training. Hoffmann collaborates extensively with researchers across multiple institutions, particularly with Thomas Ludwig, Volker Wulf, and other colleagues at the University of Siegen. He has secured funding for research projects including Cyberrüsten 4.0 and has contributed to numerous collaborative research initiatives in the field of human-computer interaction for industrial applications. He is actively involved in the PRAXLABS research group and contributes to the development of the Fab Lab Siegen, where practical applications of his research can be tested and demonstrated in real-world settings. His work represents an important bridge between theoretical human-computer interaction research and practical industrial applications, with a strong emphasis on user-centered design and real-world validation.

The Anh Hanمشاهده پروفایل

استاد

• Artificial Intelligence
• AI Safety
• Evolutionary Game Theory
• +۶ مورد دیگر

Professor The Anh Han is a leading academic in Computer Science and Head of the Center for Digital Innovation (CDI) at Teesside University's School of Computing, Engineering and Digital Technologies. He holds a PhD from the New University of Lisbon and has conducted postdoctoral research at the Free University of Brussels (VUB). His work spans artificial intelligence safety, evolutionary game theory, and behavioral economics, with over 100 publications in top journals like JAIR and Nature Scientific Reports. He has secured prestigious fellowships from the Future of Life Institute, Leverhulme, and FWO Belgium. Research Interests: AI safety, cognitive modeling, agent-based systems, intention recognition, evolutionary game theory, behavioral economics, and institutional incentives for cooperation. Key Projects: EPSRC grant on evolutionary game theory under uncertainty, Future of Life Institute grants on AI governance, and EU-funded CyberPathway for cybersecurity diversity. Education: PhD (2012) in Intention Recognition from New University of Lisbon; MSc via Erasmus Mundus at Lisbon and Dresden. Awards: Future of Life Fellowship (2023), Leverhulme Fellowship (2020), FWO Belgium Fellowship (2010-2014). Grants: Over £2M secured from EPSRC, EU Horizon, and Future of Life. His research bridges AI and behavioral science, with recent focus on AI development risks, trust in AI systems, and cooperative mechanisms in complex networks. He leads a multidisciplinary team at CDI addressing digital innovation challenges through agent-based modeling and game-theoretic frameworks.

• Civil Engineering
• Materials Science
• Structural Health Monitoring
• +۳ مورد دیگر

Yi Bao is an Associate Professor at Stevens Institute of Technology, affiliated with the Department of Civil, Environmental and Ocean Engineering within the Charles V. Schaefer, Jr. School of Engineering and Science. His research group focuses on developing advanced sensors, materials, and machine intelligence algorithms for intelligent, resilient, and sustainable civil, energy, and environmental systems. Key areas include ultra-high-performance concrete (UHPC), fiber optic sensors, machine learning for infrastructure monitoring, and sustainable material design. Recent work emphasizes AI-driven material optimization, automated structural health monitoring, and environmental impact assessments. His publications span topics such as UHPC crack reduction, microplastic characterization via AI, and high-speed railway safety. He leads studies integrating robotics and computer vision for infrastructure inspection, and his group explores low-carbon concrete solutions and digital twin technologies for bridge decks. Collaborations highlight interdisciplinary approaches to address challenges in smart infrastructure, renewable energy systems, and material sustainability. Notably, his team has developed knowledge graph-guided design frameworks for UHPC and ECC materials, leveraging machine learning to predict properties and discover physicochemical reactions. Current projects include real-time corrosion monitoring of pipelines and fatigue life assessment of orthotropic steel decks using deep learning.

Mario Edenمشاهده پروفایل

استاد

• Process Systems Engineering
• Process Synthesis
• Process Design
• +۴ مورد دیگر

Mario Eden serves as Dean of the Samuel Ginn College of Engineering and the Joe T. and Billie Carole McMillan Professor in Chemical Engineering at Auburn University. Appointed Dean in 2023 after leading the Chemical Engineering department, he holds Ph.D. and M.S. degrees from the Technical University of Denmark. Education: Ph.D. in Chemical Engineering, Technical University of Denmark M.S. in Chemical Engineering, Technical University of Denmark Research Focus: Dr. Eden's work centers on Process Systems Engineering , specializing in Process Synthesis, Design, Integration and Optimization and Product/Molecular Design . His research integrates machine learning, optimization algorithms, and sustainability principles to develop efficient chemical processes and green products, with particular emphasis on process intensification and solvent design. Publication Trends: Recent publications (2022-2024) reveal strong focus on sustainable process design through machine learning applications, ethylene oxide process intensification, and green solvent development. Key innovations include hybrid optimization frameworks for heat exchanger networks, interpretable toxicity prediction models, and NLP-based molecular property prediction. Scientific Recognition: Fellow of the American Institute of Chemical Engineers (AIChE) Author of top 1% most cited article in Industrial & Engineering Chemistry Research Leadership Impact: As Department Chair, he elevated Chemical Engineering to its highest U.S. News ranking, added 17 tenure-track faculty, and achieved record enrollment with top-tier student metrics. As Dean, he oversees the college's record $117.7 million FY24 research portfolio and spearheads major initiatives including the Gulf Coast Engineering Research Station. Research Infrastructure: His group contributes to Alabama's bioplastics initiative using agricultural waste and develops computational tools like ProCAFD for sustainable process synthesis, with research directly supporting the college's strategic focus on coastal environment studies through the new Orange Beach facility.

Alioune Ngomمشاهده پروفایل

استاد

• Bioinformatics
• Machine Learning
• Computational Biology
• +۳ مورد دیگر

Dr. Alioune Ngom is a Professor and Graduate Program Chair at the School of Computer Science, University of Windsor. He holds a Ph.D. in Computer Science from the University of Ottawa with additional graduate degrees in the field. Dr. Ngom founded Genesis Genomics Inc. and maintains affiliations with this organization. His research spans multiple interdisciplinary domains with primary focus areas including: Bioinformatics and computational biology (DNA microarray analysis, mitochondrial genomics) Machine learning applications (clustering, classification, neural networks) Network optimization (wireless mobile networks, scheduling) Computational methods in biomedicine (protein structure prediction, drug discovery) Recent publications demonstrate strong focus on AI integration in life sciences, particularly graph neural networks for drug discovery and multi-modal learning frameworks. Publication trends show increasing emphasis on transformer architectures, trustworthy AI systems, and clinical applications of deep learning. Dr. Ngom mentors graduate students as evidenced by his role as Graduate Program Chair.

Richard Yuمشاهده پروفایل

استاد

• Wireless Networks
• Edge Computing
• Federated Learning
• +۷ مورد دیگر

Richard Yu is a Professor at the Carleton School of Information Technology, Carleton University. His research focuses on cross-layer design in wireless systems, security in wireless networks, green information technologies, and multimedia over wireless networks. He holds a Ph.D. from the University of British Columbia and is a Professional Engineer (P.Eng). His work integrates machine learning, edge computing, and reinforcement learning to address challenges in vehicular networks, autonomous systems, and IoT. Education Ph.D., University of British Columbia Research Interests Professor Yu’s research spans wireless communication systems , edge computing , and AI-driven optimization . He explores novel paradigms such as federated learning for industrial IoT, secure UAV-assisted satellite networks, and generative AI for video understanding. His work emphasizes practical applications in autonomous vehicles, smart infrastructure, and industry 4.0 systems. Recent Trends in Publications His recent articles highlight advancements in reinforcement learning frameworks for resource allocation, safety-critical AI in video generation, and cross-modal fusion for embodied agents. He also investigates hybrid NOMA/OMA systems and quantum-inspired algorithms for next-generation networks. Advising & Grants No specific advisees are listed, but his research is supported by grants focusing on edge intelligence, vehicular networks, and secure computing. Labs & Teams Associated with Carleton’s School of Information Technology research groups, focusing on wireless systems and AI for industrial applications.

Xiaopeng Zhaoمشاهده پروفایل

استاد

• Brain-computer Interface
• Wearable Healthcare
• Computational Neuroscience
• +۱۸ مورد دیگر

Xiaopeng Zhao is a Professor in the Department of Mechanical, Aerospace, and Biomedical Engineering at the University of Tennessee, Knoxville, where he has been a faculty member since 2007. His research focuses on computational neuroscience, biomedical informatics, brain-computer interfaces, and AI applications in healthcare, particularly dementia care. Education: PhD in Engineering Science and Mechanics, Virginia Tech (2004) MS in Engineering, Tsinghua University, China (1999) BS in Engineering, Tsinghua University, China (1996) Research Interests: Dr. Zhao's work spans multiple interdisciplinary fields including brain-computer interfaces, wearable healthcare technologies, computational physiology, signal processing, machine learning, and epidemic modeling. His recent focus has been on developing AI-driven solutions for dementia care, including social robotics, explainable AI models, and personalized healthcare interventions. His research also encompasses biomechanics, micro- and nano-systems, impact and vibration analysis, system identification, and nonlinear dynamics. He applies advanced computational techniques to understand complex biological systems and develop innovative healthcare technologies. Scientific Awards: NSF CAREER Award (2009) Winner of Physionet/Computing in Cardiology Challenge (2010, 2011) Southeastern Conference Visiting Faculty Travel Grant (2016) Faculty Understanding Science in Open Networks Award on Human-Technology Frontier (2018) University of Tennessee Engagement and Outreach Grant (2019) University of Tennessee Faculty Fellow (2019) Professional Service: Dr. Zhao is actively involved in professional organizations including IEEE Engineering in Medicine and Biology Society (EMBS), American Society of Mechanical Engineers (ASME) - Dynamic Systems & Control Division (DSCD), Biomedical Engineering Society, and Society for Industrial and Applied Mathematics (SIAM). Contact: xzhao9@utk.edu | 313 Perkins Hall, University of Tennessee, Knoxville

Samy Wu Fungمشاهده پروفایل

استادیار

• Inverse Problems
• Optimization
• Deep Learning
• +۴ مورد دیگر

Samy Wu Fung is an Assistant Professor in the Department of Applied Mathematics and Statistics and affiliated with the Department of Computer Science at Colorado School of Mines. He is part of the Mines Optimization and Deep Learning (MODL) group. Prior to this, he was an Assistant Adjunct Professor at UCLA's Department of Mathematics. He holds a PhD in Applied Mathematics from Emory University (2019) and a BSc in Applied Mathematics from Brown University (2014). His research focuses on the intersection of applied mathematics and data science, particularly in inverse problems, optimization, deep learning, optimal control, and mean field games. His work spans theoretical developments and practical applications, including numerical solutions for Hamilton-Jacobi PDEs and algorithms for swarm control. Recent publications highlight advancements in mean-field games, optimization methods for SAT solving, and generalization bounds for implicit networks. He has received notable awards, including the 2024 Laney Early Career Alumni Award and the 2022 MGB-SIAM Early Career Fellowship. Advising includes PhD students like Soraya Terrab and Alex Vidal. He has secured grants such as the NSF-DMS award (2023) supporting learning-to-optimize research. Teaching roles include courses like Numerical Optimization and Scientific Computing at Colorado School of Mines. His lab, MODL, emphasizes interdisciplinary projects in optimization and deep learning, with collaborations spanning academia and industry. Ongoing work explores explainable AI, hybrid SAT solving, and high-dimensional mean-field control.

Rebekka Wohlrabمشاهده پروفایل

استادیار

• Requirements Engineering
• Software Architecture
• Self-Adaptive Systems
• +۴ مورد دیگر

Rebekka Wohlrab is a tenure-track Assistant Professor in Software Engineering at Chalmers University of Technology (Gothenburg, Sweden) and an adjunct faculty member at Carnegie Mellon University’s Software and Societal Systems Department (S3D). Her research focuses on requirements engineering, self-adaptive systems, and robotics. She leads a research group supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP). Education: PhD in Computer Science and Engineering (Chalmers, 2020), MSc/BSc in Computer Science (Paderborn University, 2016). Postdoc at Carnegie Mellon University (2020-2022). Research interests include requirements conflicts resolution, quality attribute trade-offs, and ethical monitoring in cyber-physical systems. She has published extensively on adaptive systems, architectural tactics, and agile development practices. Notable contributions include frameworks for robot mission planning, security assurance cases, and traceability management in automotive systems. Recent awards include a pedagogical prize from students at Chalmers. She advises PhD students on topics like self-adaptive robotics and WASP-funded projects. Her work spans theoretical research and industry collaboration (e.g., Volvo Cars). Active in conferences like ICSE, RE, and ICSA as a program committee member. Labs/Teams: Leads the Self-Adaptive Systems Research Group at Chalmers, collaborating with WASP and automotive industry partners.

• Functional Analysis (applied)
• Operator/Spectral Theory
• Compressed Sensing
• +۱۲ مورد دیگر

Professor Anders Christian Hansen is a leading mathematician at the University of Cambridge and University of Oslo , specializing in Functional Analysis , Artificial Intelligence , and Compressed Sensing . As head of the Applied Functional and Harmonic Analysis group and a Royal Society University Research Fellow , his work bridges foundational mathematics with cutting-edge data science. Current affiliations: University of Cambridge (since 2013), University of Oslo (Professor II since 2012) Previous roles: Von Karman Instructor (Caltech), Marie Curie Fellow (Vienna), Junior Research Fellow (Cambridge) Research Interests : Functional Analysis (applied), Operator/Spectral Theory, Compressed Sensing Artificial Intelligence, Computational Barriers, Foundations of Mathematics Medical Imaging, Inverse Problems, Numerical Analysis Article Trends : His recent work explores the limits of AI through the Solvability Complexity Index (SCI), revealing inherent instabilities in deep learning despite theoretical guarantees. Collaborations with B. Adcock and M. Colbrook address structured sampling and numerical algorithms for spectral problems in infinite dimensions. Scientific Awards : PROSE Award Finalist (2022) for Compressive Imaging Whitehead Prize (2019), IMA Prize (2018) Leverhulme Prize (2017) for contributions to applied analysis Advising & Grants : Supervised 17 PhD students and 8 postdocs. Active in grants related to medical imaging , AI safety , and computational mathematics .

Nicolas Moitessierمشاهده پروفایل

استاد

• Medicinal Chemistry
• Computer-Aided Drug Design
• Asymmetric Synthesis
• +۱ مورد دیگر

Nicolas Moitessier is a Professor in the Department of Chemistry at McGill University, specializing in computational and experimental approaches to drug discovery. He earned his Maîtrise (1994) and Ph.D. (1998) from the University of Nancy, France, followed by postdoctoral research at the University of Montreal (1998-2000) and a Research Senior position at CNRS Nancy (2001-2003). His research integrates computer-aided drug design with organic synthesis, focusing on developing predictive software platforms (FITTED, IMPACTS, FORECASTER) for drug binding, metabolism prediction, and asymmetric catalyst design. Current projects include covalent inhibitor development for viral proteases and RNA-targeted therapeutics. Professor Moitessier teaches CHEM 222 (Introductory Organic Chemistry 2) and CHEM 552 (Physical Organic Chemistry). His interdisciplinary work bridges chemical biology, catalysis, and computational chemistry to accelerate molecular discovery.

Marco Hutterمشاهده پروفایل

استاد

• Robotics
• Intelligent Systems
• Autonomous Navigation
• +۴ مورد دیگر

Prof. Marco Hutter is a Full Professor at the Department of Mechanical and Process Engineering at ETH Zürich, and Deputy Head of the Institute for Robotics and Intelligent Systems. His research focuses on advanced robotics, including autonomous legged systems, robotic perception, and control strategies for challenging environments. He leads projects in areas such as forest inventory using legged robots, space exploration robotics, and mobile manipulation systems. His work emphasizes real-world deployment, with contributions to robotic navigation, sensor integration, and policy optimization. Prof. Hutter collaborates on large-scale datasets and frameworks like ROS-LLM for embodied AI, and explores applications in logistics, construction, and planetary exploration. His team develops both hardware (e.g., LEVA logistic vehicles) and software solutions (e.g., risk-guided diffusion models for safety-critical systems). Key projects include ETHcavation (construction environment understanding) and TartanGround (robot perception datasets). He pioneers work in agile locomotion policies, magnetic climbing robots, and multi-agent robotic teams for parcel delivery. His research bridges theoretical advancements with practical implementations, addressing challenges in terrain adaptation, energy efficiency, and robustness in autonomous systems.

Mac Schwagerمشاهده پروفایل

دانشیار

• Multi-Robot Systems
• Distributed Algorithms
• Autonomous Systems
• +۷ مورد دیگر

Mac Schwager is an Associate Professor in the Department of Aeronautics and Astronautics at Stanford University, with a courtesy appointment in Computer Science. He leads the Multi-robot Systems Lab (MSL), focusing on distributed algorithms for coordination, estimation, and learning in autonomous systems. His research spans multi-robot control, swarm intelligence, and perception-driven robotics. Schwager holds a PhD (2009) from MIT in Mechanical Engineering, following an MS (2005) and BS (2000) from MIT and Stanford, respectively. His academic career includes positions at Boston University and postdoctoral work at UPenn/MIT. Research interests emphasize distributed algorithms for autonomous teams, including aerial camera networks, quadrotor swarms, environmental monitoring, and human-swarm interfaces. Key projects include trustworthiness in multi-robot systems and autonomous drone racing. His lab develops tools like Gaussian Splatting for navigation and manipulation, integrating computer vision and reinforcement learning. Teaching includes advanced courses on state estimation, multi-robot control, and feedback systems. His work bridges robotics, control theory, and AI, addressing challenges in scalability, safety, and adaptability in complex robotic systems.