جستجوی استاد راهنمای دانشگاهی

Robert Laganièreمشاهده پروفایل

استاد

• Computer Vision
• Image and Video Analysis
• Visual Surveillance
• +۸ مورد دیگر

Robert Laganière is a Professor at the School of Electrical Engineering and Computer Science at the University of Ottawa, where he has been actively contributing to the fields of computer vision and image analysis. He is a member of the VIVA research laboratory and holds a Ph.D. and M.Sc. from INRS-Telecommunications in Montreal, as well as a bachelor's degree in Electrical Engineering from École Polytechnique de Montréal. Bachelor's in Electrical Engineering: École Polytechnique de Montréal (1987) Master's Degree: INRS-Telecommunications (1990) Doctorate: INRS-Telecommunications (1996) Professor Laganière's research focuses on computer vision, with particular expertise in image and video analysis, visual surveillance, embedded vision systems, and deep learning applications. His work spans fundamental research in feature detection and matching to practical applications in autonomous driving, human recognition, and real-time object tracking. He has made significant contributions to the development of algorithms for pedestrian detection, age and gender recognition, and 3D object localization. His publication trends reveal a consistent focus on practical computer vision applications with a strong emphasis on real-time performance and embedded implementation. Over the past decade, his research has evolved from foundational work in feature matching and homography estimation toward more complex applications in action recognition, human-computer interaction, and intelligent surveillance systems. His work consistently bridges theoretical computer vision with practical engineering constraints, particularly for mobile and embedded platforms. Best Paper Award, IEEE International Conference on Computer and Robot Vision (CRV 2014) Best Paper Award, CVPR Embedded Vision Workshop, Providence, RI, June 2012 Best Real-time Tracker, IEEE International Conference on Computer Vision (ICCV) Workshop on Visual Object Tracking (VOT2015) Professor Laganière has supervised numerous graduate students through the years, with a particular focus on practical applications of computer vision in surveillance, human recognition, and embedded systems. His research has been supported through industry partnerships with companies including CogniVue Corp, NXP, iWatchLife.com, Solink Corp, CBSA Canada, Ross Video, Thales, Habitat Seven, and YouI Labs. He has successfully translated his research into commercial applications through his founding of Visual Cortek (acquired by iWatchLife in 2009) and Tempo Analytics (founded in 2016). As a member of the VIVA research laboratory, Professor Laganière collaborates with colleagues on advanced computer vision projects, particularly those involving intelligent video analytics for security and commerce applications. His work on NAVIRE (Virtual Navigation in Remote Environments) demonstrates his commitment to developing practical solutions for real-world navigation challenges using image-based representations of real environments.

Mohammad Rostamiمشاهده پروفایل

دانشگاهی

• Machine Learning
• Transfer Learning
• Domain Adaptation
• +۵ مورد دیگر

Mohammad Rostami is a Research Assistant Professor at the University of Southern California (USC) in the Department of Computer Science and Electrical and Computer Engineering, with a joint appointment at the USC Information Sciences Institute (ISI). He holds a PhD in Electrical and Systems Engineering from the University of Pennsylvania and additional degrees in Robotics, Philosophy, Electrical Engineering, and Pure Mathematics from prestigious institutions including the University of Waterloo and Sharif University of Technology. His research focuses on machine learning in data-scarce environments, particularly transfer learning, domain adaptation, low-shot learning, and improving learning efficiency through continual and collective learning. He incorporates symbolic logic and neuro-symbolic approaches to address challenges in catastrophic forgetting and knowledge retention. Applications span medical imaging, computer vision, and explainable AI. Rostami has received several accolades including the UPenn Best PhD Dissertation Award, IJCAI Distinguished Student Paper Award, and University of Waterloo Outstanding Achievement Award. His work bridges theoretical advancements with practical implementations, emphasizing real-world applications in healthcare and autonomous systems. He teaches graduate courses in applied natural language processing and knowledge graph construction. Rostami advises students at all academic levels and collaborates with remote researchers, emphasizing motivated, long-term project commitments.

Francesco Fabianoمشاهده پروفایل

پژوهشگر ارشد

• Multi-agent systems
• Epistemology
• Autonomous planning
• +۷ مورد دیگر

Francesco Fabiano is a Research Fellow at the Department of Computer Science, University of Oxford, and an Affiliated Faculty Member at New Mexico State University. He previously served as Assistant Professor at New Mexico State University (2023–2024) and held Adjunct Professor roles at Saint Joseph’s University and University of Parma. His research spans neuro-symbolic AI, multi-agent systems, epistemology, and autonomous planning. Postdoctoral Research Associate at University of Oxford (2025–present) Ph.D. in Computer Science from University of Udine (2018–2021) Master’s Degree in Computer Science from New Mexico State University (2017–2018) Bachelor’s Degree in Computer Science from University of Parma (2013–2016) His work focuses on neuro-symbolic architectures that integrate formal logic with machine learning, particularly for multi-agent epistemic planning and ethical decision-making frameworks . He explores hybrid systems combining System-1/System-2 cognitive paradigms to enhance AI trustworthiness. Recent publications analyze knowledge representation in neuro-symbolic systems, the role of large language models in planning, and heuristic optimization in multi-agent epistemic solvers like EFP. His work also addresses ethical AI and explainable data-to-text frameworks . Best Ph.D. Thesis Award by GULP (2022) He collaborates with IBM Watson Research Lab on cognitive theory-inspired AI paradigms and contributes to open-source planning tools like EFP. His teaching experience includes courses in Applied Machine Learning , Automated Planning , and LaTeX programming .

Andres Kwasinskiمشاهده پروفایل

استاد

• Cognitive Radios and Networks
• Machine Learning for Wireless Communications
• 5G/6G/NextG Networks
• +۵ مورد دیگر

Andres Kwasinski is a Professor in the Department of Computer Engineering at Rochester Institute of Technology (RIT), part of the Kate Gleason College of Engineering. He serves as Graduate Program Director for the Ph.D. in Electrical and Computer Engineering and M.Sc. in Computer Engineering. He co-directs the Networking and Information Processing (NetIP) Lab and holds editorial roles with IEEE publications, including Chief Editor of the IEEE Signal Processing Repository and Associate Editor of IEEE Signal Processing Magazine. Education: Ph.D. and M.Sc. in Electrical and Computer Engineering from the University of Maryland, College Park (2004 and 2000), and B.Sc. in Electrical Engineering from the Buenos Aires Institute of Technology (1992). Prior to RIT, he worked at Texas Instruments, Lucent Technologies, and the University of Maryland. Research Interests: Cognitive radios, machine learning for dynamic spectrum access, 5G/6G networks, VR communications, cross-layer resource allocation, smart infrastructures, and signal processing. His work emphasizes sustainable and resilient communication systems, integrating renewable energy and AI-driven solutions. Notable Contributions: Authored/co-authored books on cooperative communications and 3D visual communications. Over 70 peer-reviewed publications, including works on energy-efficient wireless networks, microgrid integration for base stations, and deep reinforcement learning in cognitive radio. His research is funded by the NSF, Harris Corporation, and the Air Force Research Laboratory. Grants & Awards: Supported by grants from NSF and industry partners. Recognized for contributions to IEEE standards and technical leadership in signal processing and communications. Labs & Teams: Co-director of the NetIP Lab, focusing on networking, signal processing, and smart infrastructure. Collaborates on interdisciplinary projects in robotics, warehouse automation, and 5G/B5G systems.

Bing Yanمشاهده پروفایل

استادیار

• Power System Optimization
• Grid Integration of Renewables
• Microgrid Operation
• +۳ مورد دیگر

Bing Yan is an Assistant Professor in the Department of Electrical and Microelectronic Engineering at Rochester Institute of Technology (RIT), affiliated with the Kate Gleason College of Engineering. She holds a B.S. in Information Management from Renmin University of China (2010), and M.S. and Ph.D. degrees in Electrical Engineering and Statistics from the University of Connecticut (2012–2017). Prior to RIT, she was an Assistant Research Professor at the University of Connecticut. Dr. Yan’s research focuses on power system optimization , including grid integration of renewables (wind/solar), microgrid operations, distributed energy systems, and manufacturing scheduling. She has published over 30 peer-reviewed articles and secured grants from the National Science Foundation (including a CAREER Award), Department of Energy, and industry partners like Brookhaven National Laboratory and ABB. Her work emphasizes mixed-integer linear programming and machine learning applications in energy systems. Notable contributions include stochastic unit commitment models for wind farms, voltage control via deep reinforcement learning, and multi-layer weather models for PV prediction. She advises on projects involving grid resilience, smart manufacturing, and data-driven optimization. Awards: National Science Foundation Faculty Early Career Development (CAREER) Award Multiple NSF grants, DOE grants, and industry contracts Teaching: Courses include Circuits I , Electric Power Transmission & Distribution , and Advanced Power Systems . She also mentors students through co-op programs and independent studies. Labs/Teams: Leads the Intelligent Lab of Power and Manufacturing (ILPM), focusing on multidisciplinary solutions for energy and manufacturing systems. The lab emphasizes hands-on training and innovation in smart grid technologies and sustainable energy systems.

Steven V. Leyمشاهده پروفایل

استاد

• Organic Chemistry
• Flow Chemistry
• Green Chemistry
• +۴ مورد دیگر

Professor Steven V. Ley leads the Yusuf Hamied Department of Chemistry at the University of Cambridge, focusing on transformative research in flow chemistry, organic synthesis, and green chemistry. His work emphasizes sustainable methodologies and the integration of advanced technologies like microcontrollers and automation to revolutionize chemical processes. In 2018, he received the prestigious Arthur C. Cope Award—the first UK-based recipient—recognizing groundbreaking contributions to organic chemistry. Research interests include developing continuous flow systems for hazardous reaction management, immobilized reagents, and machine-assisted synthesis. Collaborations span academia and industry, notably through spin-off company New Path Molecular , which applies cutting-edge synthesis techniques to pharmaceuticals and agrochemicals. Key publications highlight innovations in flow chemistry applications, sustainable process design, and automation. His work bridges chemistry with engineering, aiming to address global challenges in resource efficiency and environmental impact. Awards: Arthur C. Cope Award (2018) Lab/Teams: Active research group at the University of Cambridge; collaborates with New Path Molecular on commercial applications.

Bart J. Hengeveldمشاهده پروفایل

استادیار

• Internet of Things (IoT) Interaction Design
• Sound Design
• Tangible Interaction
• +۳ مورد دیگر

Bart J. Hengeveld is an Assistant Professor at Eindhoven University of Technology (TU/e), affiliated with the Industrial Design department within the Faculty of Industrial Design. He leads the Future Everyday research cluster, focusing on the intersection of sound design and IoT systems. His work explores how embedded technologies can communicate through sound and tangible interaction in ways that enhance human experience. Education: BSc/MSc in Industrial Design Engineering, Delft University of Technology (2003) PhD in Industrial Design, TU/e (2011), with concurrent training in double bass at Codarts Conservatory Research Interests: Acoustic communication in IoT systems Tangible interaction design Social implications of ubiquitous computing Sound as a medium for human-technology interaction Recent Research Trends: Focus on multimodal interfaces combining sound/light/haptics Studies on sound design for healthcare and smart living environments Investigations into ethical and inclusive IoT soundscapes Awards: Two Teacher of the Year awards at TU/e, reflecting his commitment to pedagogical excellence. Advising & Projects: Supervised 113 student projects including interactive installations like 'Naktis' and 'Sonostapp' Leading the €1.5M 'Role of Sound in Hybrid Work Environments' project (2023–2026) Organized TEI 2016 conference and serves on its steering committee Teams/Labs: Core member of TU/e's Future Everyday cluster, collaborating with multidisciplinary teams on embodied interaction research.

Heikki Handroosمشاهده پروفایل

استاد

• Mechatronics
• Robotics
• Machine Automation
• +۵ مورد دیگر

Heikki Handroos is a Full Professor of Mechanical Engineering at LUT University, leading the Laboratory of Intelligent Machines since 1993. He holds a DSc (Technology) from Tampere University of Technology and has served as Vice-Dean of the Faculty of Technology (2007-2009) and currently chairs the Collegiate Body of LUT University. His research focuses on mechatronics, robotics, control systems, and fluid power, with over 300 publications and 2,400+ citations. He has supervised 34 doctoral theses and 150+ MSc projects, managed R&D projects exceeding €20M, and co-founded four tech startups. His work spans industrial collaborations, digital twin applications, and innovative robotics for nuclear energy (e.g., DEMO reactor maintenance systems). He has held visiting professorships in the U.S., Japan, and Russia, and actively contributes to academic editorial roles and professional societies like ASME and IEEE.

Tho Leمشاهده پروفایل

استادیار

• Transportation Modeling
• Logistics and Supply Chain Optimization
• AI/Machine Learning Applications
• +۶ مورد دیگر

Tho Le is a tenure-track Assistant Professor of Industrial Engineering Technology at Purdue University’s School of Engineering Technology. He leads the Smart Logistics (SmartLogs) Systems Research Lab and is affiliated with the Institute for Control, Optimization, and Network (ICON), Realizing the Digital Enterprise (RDE) initiative, Integrative Data Science Initiative (IDSI), and Semiconductors @ Purdue. His research focuses on system-scale transportation modeling using optimization, AI/ML, and econometrics to guide industry and policy decisions. Education: Ph.D. in Transportation Systems, Purdue University MSc in Industrial Engineering (Operations Research), Purdue University M.Eng. in Transportation Engineering (Best Thesis Award), Hiroshima University Research Interests: Emerging technologies: sidewalk delivery robots, indoor service robots, drones Semiconductor supply chain resilience and Lean Six Sigma applications Digital twins for logistics systems and fleet management Freight transport, connected/autonomous vehicles, travel behavior modeling Awards & Recognition: Recipient of National Science Foundation grant for energy efficiency modeling (2024) Polytechnic Research Awards (July 2024) Lab & Collaboration: SmartLogs Lab: develops data-driven solutions for urban logistics and asset management Leadership in Purdue’s semiconductor initiatives aligned with the CHIPS Act

• Intelligent Transportation Systems
• Automated Vehicles
• Control Systems
• +۶ مورد دیگر

Lingxi Li is a Professor at the Elmore Family School of Electrical and Computer Engineering at Purdue University's Indianapolis campus. His research focuses on modeling complex systems, connected and automated vehicles, intelligent transportation systems, and parallel intelligence. He holds a Ph.D. from the University of Illinois at Urbana-Champaign (2008), and master's and bachelor's degrees from the Chinese Academy of Sciences (2003) and Tsinghua University (2000). Research Interests: Dr. Li's work bridges control systems, transportation engineering, and AI, with emphasis on human-machine interaction, autonomous vehicle systems, and scenario-based traffic modeling. His projects include developing frameworks for Industry 5.0 collaboration, enhancing traffic flow prediction through parallel learning, and advancing safety in micro-mobility systems like e-scooters. Recent Publications: Over 15+ articles (2023-2025) explore topics such as game-theoretic vehicle interaction modeling, vision-language systems for autonomous driving, and acoustic SLAM technologies. These studies reflect a focus on real-world validation and system integration in smart transportation. Labs & Initiatives: Leads research in autonomous mining systems and scenario engineering for intelligent vehicles, leveraging parallel intelligence concepts. Collaborates on projects like ParallelWorkforce (Industry 5.0 frameworks) and SceNDD++ (naturalistic driving datasets).

• 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.

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.

Waldemar Karwowskiمشاهده پروفایل

استاد

• Human Factors and Ergonomics
• Neuroergonomics
• Human Systems Integration
• +۷ مورد دیگر

Waldemar Karwowski is a Pegasus Professor and Chairman of the Department of Industrial Engineering and Management Systems at the University of Central Florida, USA. He also serves as Executive Director of the Institute for Advanced Systems Engineering. His affiliations include the College of Engineering and Computer Science and multiple international academic roles. He holds a Doctor of Science (dr hab.) in Management Science from Poland, a Ph.D. in Industrial Engineering from Texas Tech University, and three honorary doctorates from universities in Ukraine, Slovakia, and Russia. His research focuses on neuroergonomics, human systems integration, safety engineering, nonlinear dynamics in human-machine systems, and applications of soft computing. He co-edits leading journals including Human Factors and Ergonomics in Manufacturing and Theoretical Issues in Ergonomics Science . Key achievements include Fellowships from HFES, IEA, and the UK's Institute of Ergonomics, and past presidencies of HFES (2007) and the International Ergonomics Association (2000-2003). His work addresses grand challenges in human factors, AI ethics, and healthcare safety culture. Recent research explores EEG-based neuroergonomic metrics, graph neural networks for brain connectivity analysis, and pandemic modeling with nonlinear dynamics. Awards include the Handbook of Human Factors and Ergonomics (5th ed., 2022) co-edited with Gavriel Salvendy. His advisory roles span industry and government, emphasizing systems engineering and human-centered AI integration.

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.

• Fluid Power (Hydraulics)
• Acoustic Sensing
• Dynamic Modelling
• +۴ مورد دیگر

Travis Wiens is an Associate Professor in the Department of Mechanical Engineering at the University of Saskatchewan. His research focuses on fluid power systems, acoustic sensing, dynamic modeling, mining mechatronics, and artificial intelligence applications in engineering. He holds a B.Sc., M.Sc., and Ph.D. in Mechanical Engineering from the same institution, completed in 2002, 2004, and 2008 respectively. His research interests emphasize innovative solutions for hydraulic control systems, pipeline resonance reduction, and mining safety through acoustic technologies. He has developed low-cost electrohydrostatic actuators and explored additive manufacturing in valve design. Notable contributions include vibrational data communication tools for extractive industries and machine learning models for mine roof stability assessment. Dr. Wiens teaches ENGC 01A - Introduction to Fluid Power Components . His work bridges theoretical fluid dynamics with practical applications in energy efficiency, structural health monitoring, and biomimetic systems. Ongoing research trends include integrating AI with hydraulic systems and advancing non-destructive testing methods for infrastructure integrity. His publications highlight interdisciplinary approaches, combining mechanical engineering principles with computational modeling, neural networks, and mechatronics. Collaborative efforts focus on mining safety, oil and gas infrastructure, and sustainable hydraulic technologies.