جست‌وجوی استاد و پژوهشگر

Ayan Mukhopadhyayمشاهده پروفایل

پژوهشگر

• Multi-Agent Systems
• Cyber-Physical Systems
• Smart Cities
• +۵ مورد دیگر

Dr. Ayan Mukhopadhyay serves as a Senior Research Scientist in the Department of Electrical Engineering and Computer Science at Vanderbilt University's School of Engineering. Previously, he was a Post-Doctoral Research Fellow at Stanford Intelligent Systems Lab where he received the 2019 CARS post-doctoral fellowship. His academic journey includes a Ph.D. from Vanderbilt University's Computational Economics Research Lab with a doctoral thesis nominated for the Victor Lesser Distinguished Dissertation Award 2020. His research spans critical domains in smart infrastructure systems with particular focus on: Developing robust decision-making frameworks for cyber-physical systems under uncertainty Creating multi-agent solutions for emergency response optimization Designing machine learning approaches for urban mobility and energy management Building proactive incident detection pipelines using heterogeneous data sources Analysis of his recent publications reveals strong thematic continuity in applying artificial intelligence to real-world infrastructure challenges, particularly in transportation systems, emergency response, and energy management. His work consistently bridges theoretical AI advances with practical implementation in smart city contexts, demonstrating expertise in both algorithmic innovation and systems integration. Award highlights include: CARS Post-Doctoral Fellowship (2019) Best Paper Award at ICLR's AI for Social Good Workshop Victor Lesser Distinguished Dissertation Award Nomination (2020) Dr. Mukhopadhyay leads significant research initiatives through ScopeLab, focusing on creating deployable solutions for public transit, emergency response, and energy systems. His work on vehicle-to-building charging, traffic incident localization, and equitable transit network design demonstrates commitment to solving high-impact urban challenges through rigorous computational methods. Current projects involve developing simulation environments for non-stationary environments (NS-Gym) and explainable planning frameworks integrating formal logic with large language models.

Larry P Heckمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۷ مورد دیگر

Larry P. Heck is a Professor with a joint appointment in the School of Electrical and Computer Engineering and School of Interactive Computing at the Georgia Institute of Technology. He holds the Rhesa S. Farmer Advanced Computing Concepts Chair and is a Georgia Research Alliance Eminent Scholar . Education: BSEE, Texas Tech University (1986) MSEE, Georgia Institute of Technology (1989) PhD EE, Georgia Institute of Technology (1991) His research focuses on conversational AI , dialogue systems , and machine learning applied to natural language processing and speech recognition . He pioneered early industrial applications of deep learning in speech processing and has contributed to advancements in multimodal interaction, knowledge distillation, and real-time question answering systems. Recent publications emphasize moral reasoning in AI , multimodal dialogue , and large-scale dataset creation for conversational systems. His work bridges language modeling , sensor fusion , and ethical AI through innovations in contextual reasoning and interface masking. Scientific Distinctions: IEEE Fellow (2020) IEEE Signal Processing Society Best Paper Award Academy of Distinguished Engineering Alumni, Georgia Tech (2017) Distinguished Engineer Award, Texas Tech University (2017) Fellow, National Academy of Inventors (2025) He has secured significant funding from DARPA and NSA for speaker recognition systems and has led cutting-edge research at institutions including Microsoft, Google, and Samsung. His lab focuses on conversational systems and deep learning for speech and multimodal data.

• Mechatronics
• Signal Processing
• Control Systems
• +۷ مورد دیگر

Jonas Sjöberg is a Full Professor of Mechatronics at Chalmers University of Technology, where he leads the Mechatronic research group in the College of Engineering. His research spans multiple aspects of mechatronic systems with a strong focus on automotive applications. Sjöberg holds leadership roles in numerous research projects related to autonomous vehicles, vehicle control systems, and transportation safety. His research interests encompass a broad spectrum of mechatronics applications, with particular emphasis on model-based methods, signal processing, control systems, system identification, and optimization for design and product development of mechatronic systems. Sjöberg's work bridges theoretical control engineering with practical automotive applications, especially in the domains of Automotive Active Safety and Hybrid Electric Vehicles. Analysis of Sjöberg's recent publications reveals a strong research trajectory focused on autonomous vehicle technologies, with particular attention to vehicle dynamics control, intersection safety, road surface condition estimation, and optimization of vehicle maneuvers. His work demonstrates a consistent approach of applying advanced control theory to solve real-world transportation challenges, with increasing emphasis on machine learning techniques integrated with traditional control systems. Sjöberg actively supervises research and education at both undergraduate and graduate levels while leading multiple research projects funded by VINNOVA, the European Commission, and other organizations. His research group collaborates extensively with both academic institutions and industry partners in the automotive sector. His laboratory work focuses on mechatronic systems development, particularly for automotive applications including autonomous bicycles, bus docking systems, and vehicle control algorithms. The research group maintains strong connections with the automotive industry, particularly in Sweden's robust vehicle technology ecosystem.

Jitendra Malikمشاهده پروفایل

استاد

• Computer Vision
• Robotics
• Machine Learning
• +۷ مورد دیگر

Jitendra Malik is the Arthur J. Chick Professor of Electrical Engineering and Computer Sciences (EECS) at the University of California, Berkeley, with affiliations in Bioengineering, Cognitive Science, and Vision Science groups. He previously served as Chair of the Computer Science Division (2002-2004) and Department Chair of EECS (2004-2006 and 2016-2017). He also worked part-time as Research Director and Site Lead at Facebook AI Research (Meta Inc.) during 2018-2019. Education : B.Tech in Electrical Engineering (IIT Kanpur, 1980), PhD in Computer Science (Stanford, 1985) Current Research : Computer Vision, Robotics, Machine Learning, Computational Modeling of Human Vision, and Biological Image Analysis Making significant contributions to computer vision , robotics , and machine learning , Malik’s work includes foundational algorithms like anisotropic diffusion , normalized cuts , and R-CNN . His recent research focuses on embodied AI agents , dexterous manipulation , 3D scene reconstruction , and vision-language-action models . His publications have received 11 best paper awards , including test-of-time awards like the Longuet-Higgins Prize (3x) and Helmholtz Prize (3x). Malik has mentored over 80 PhD students and postdoctoral fellows , many of whom hold prominent positions at institutions like MIT, Caltech, Google, and Meta. His lab’s work spans computer vision , robot learning , and 3D modeling , with applications in autonomous systems, medical imaging, and computational biology. Scientific Awards Presidential Young Investigator Award (1989) Distinguished Alumnus Award, IIT Kanpur (2008) IEEE PAMI-TC Distinguished Researcher (2013) K.S. Fu Prize, IAPR (2014) ACM-AAAI Allen Newell Award (2016) IJCAI Award for Research Excellence (2018) IEEE Computer Society Pioneer Award (2019) Fellow, IEEE, ACM, AAAS Member, National Academy of Engineering and National Academy of Sciences As a leader in sensorimotor learning and humanoid robotics , Malik has pioneered approaches for vision-based quadcopter control , humanoid locomotion , and visuo-tactile perception , further advancing the field of artificial intelligence and computational vision .

• Human Reinforcement Learning
• Neuroeconomics
• Decision-Making Under Uncertainty
• +۳ مورد دیگر

Stefano Palminteri is a researcher at the École Normale Supérieure in Paris, leading the Human Reinforcement Learning team at the Cognitive Neuroscience Laboratory (LNC2) since 2017. He holds a PhD in Cognitive Neuroscience from Université Pierre et Marie Curie (2012) and completed postdoctoral work at LNC2, University of Trento, and University College London. Education: Master in Pharmaceutical Biotechnology (University of Bologna, 2006); Master in Cognitive and Behavioral Neuroscience (Université Pierre et Marie Curie, 2007); PhD in Cognitive Neuroscience (UPMC, 2012). His research focuses on computational and neurobiological mechanisms of reinforcement learning, decision-making under uncertainty, and behavioral biases. He investigates how contextual factors (e.g., social influence, neurochemical states) shape learning processes in healthy and clinical populations (e.g., addiction, depression, Tourette syndrome). Recent work explores value normalization, metacognitive biases, and cross-cultural comparisons of reinforcement learning. Key trends in his publications include (1) computational modeling of reward/punishment sensitivity in psychiatric conditions, (2) neuroeconomic frameworks for decision-making under risk and ambiguity, (3) multi-agent reinforcement learning in financial market simulations, and (4) cross-domain comparisons of description vs. experience-based learning. He employs functional neuroimaging, pharmacological interventions, and behavioral experiments to uncover how brain circuits (e.g., striatum, prefrontal cortex) encode value signals. His lab offers internships in epistemic biases and reinforcement learning.

Joshua I. Goldمشاهده پروفایل

استاد

• Neuroscience
• Computational Neuroscience
• Cognitive Neuroscience
• +۶ مورد دیگر

Joshua I. Gold is a Professor of Neuroscience at the Perelman School of Medicine, University of Pennsylvania , where he co-directs the Computational Neuroscience Initiative . His research focuses on the neural mechanisms underlying inference and learning in decision-making, integrating computational modeling with human and non-human primate studies . Education: Sc.B. in Neural Sciences from Brown University (1991), Ph.D. in Neurosciences from Stanford University (1997) Grants: NIH R01 EY015260, NIH F31 MH093099, NIH F21 MH093099 Gold's work explores how uncertainty, contextual information , and neural computations shape adaptive behavior. His lab employs psychophysics, electrophysiology , and pupillometry to study decision-making in dynamic environments. Recent publications highlight Bayesian inference, dual anticipatory processes , and neural correlates of reward prediction . Scientific Contributions: Developed computational models for learning rate regulation Investigated subthalamic nucleus and caudate nucleus roles in decision-making Linked noradrenergic systems to adaptive learning

• Image Registration
• Image Segmentation
• Image Classification
• +۶ مورد دیگر

Sune Darkner is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen, specializing in the Image Analysis, Computational Modelling, and Geometry research section. His work focuses on medical image processing with particular emphasis on neuro-imaging data including MRI and PET scans. His primary research interests include Image Registration, Segmentation and Classification of Medical Image Data , with a specific focus on estimation of image similarity as his main research interest. Darkner strongly believes that the implementation of image processing algorithms should be thoroughly tested and reflect the theoretical properties as accurately as possible. His work primarily centers on neuro-imaging data such as MRI and PET. His recent publications (2024-2025) reveal a strong focus on medical image analysis, with particular emphasis on tumor volume delineation, deformable image registration with physics constraints, and applications of deep learning in medical imaging. His work spans both theoretical foundations of image processing and practical clinical applications. Darkner previously held a Post Doc position at the Technical University of Denmark from February 2009 to January 2010, demonstrating his longstanding engagement with image analysis research in the Danish academic community.

Ondrej Vaculinمشاهده پروفایل

استاد

• Passive Vehicle Safety
• Automated Driving
• Vehicle Dynamics
• +۳ مورد دیگر

Prof. Ondrej Vaculin, Ph.D., is a Professor at Technische Hochschule Ingolstadt (THI), specializing in passive vehicle safety, mechatronic systems, and automated driving. He joined THI in 2018 and previously held roles at TÜV SÜD Prague (2008–2018) as Vice President for Safety and Security in Automotive, and as a researcher at Czech Technical University (2005–2008) and DLR Oberpfaffenhofen (2000–2005). His research focuses on enhancing vehicle safety through advanced simulation, sensor integration, and machine learning applications. Education: Ph.D. in Mechanics of Solids, Deformable Bodies and Continua from Czech Technical University in Prague (2001), and a degree in Technical Cybernetics from the Faculty of Electrical Engineering at the same university (1991). His work bridges theoretical research and practical implementation, addressing challenges in automated driving systems, crash simulation, and human body diversity in safety design. Research interests include passive safety systems, autonomous vehicle dynamics, and infrastructure integration for safety enhancements. Recent publications emphasize collaborative smart infrastructure, sensor fusion for free space detection, and machine learning in crash detection. He actively contributes to FISITA, serving as a Council Delegate and member of technical committees. Awards/memberships: Active in FISITA, holding leadership roles in technical committees. No explicit awards listed, though his contributions to automotive safety standards are notable. Grants and advising: While no specific grants or students are detailed, his extensive industry and academic collaborations (e.g., IN2Lab testing field projects) highlight his role in applied research and development.

Syed Ali Razaمشاهده پروفایل

پژوهشگر

• Reinforcement Learning
• Robotics
• Artificial Intelligence
• +۳ مورد دیگر

Syed Ali Raza is a researcher affiliated with the University of Technology, Sydney (PhD 2018), with a former affiliation at the Institute of Business Administration, Karachi. His work focuses on reinforcement learning, robotics, and human-robot interaction. Key contributions include studies on social robots' question-answering services, human feedback integration in AI systems, and optimization of robotic movements in multi-agent scenarios. He has collaborated extensively with researchers like Mary-Anne Williams and Sajjad Haider. His research spans theoretical advancements in machine learning algorithms and practical applications in robotics competitions (e.g., RoboCup). Research interests emphasize computational reinforcement learning, reward shaping techniques, and ethical design of autonomous systems. Notable projects include privacy-first approaches for social robots and hybrid methods for humanoid robot locomotion. His work bridges theoretical AI advancements with real-world robotic implementations.

Mohammad T. Khasawnehمشاهده پروفایل

استاد

• Healthcare Systems Engineering
• Operations Management
• Data Science
• +۳ مورد دیگر

Mohammad T. Khasawneh is a SUNY Distinguished Professor and Director of the School of Systems Science and Industrial Engineering at Binghamton University. He leads the Watson Institute for Systems Excellence (WISE) and the Healthcare Systems Engineering Center. His roles include Director of the Manhattan Graduate Program in Health Systems. Education: BS and MS in Mechanical Engineering from Jordan University of Science and Technology (1998, 2000), PhD in Industrial Engineering from Clemson University (2003). Research focuses on healthcare systems engineering, operations management, and data science. His work optimizes healthcare systems for improved patient outcomes and cost efficiency. Key areas include predictive analytics, hospital resource utilization, and clinical performance improvement. He has generated over $15M in external funding and led projects with U.S. hospital systems. Notable achievements include developing the Executive Master of Science in Health Systems and an MS in Healthcare Systems Engineering. His research has produced 60+ journal articles and 120+ conference papers. Awards include SUNY Chancellor’s Awards for Teaching (2011) and Scholarship (2021), University Awards for Graduate Director (2015) and International Education (2016). He is an IISE Fellow and holds honorary visiting professorships at Hebei University of Technology (China) and Vellore Institute of Technology (India). Grants and funding: $2.5-3M annually via WISE, $39M in software/equipment grants. Lab/Initiatives: Healthcare Systems Engineering Center, WISE, and multiple hospital partnerships.

Marinko Sarunicمشاهده پروفایل

استاد مدعو

• Biomedical Imaging
• Optical Coherence Tomography
• Microscopy
• +۲ مورد دیگر

Marinko Sarunic is an Adjunct Professor at the School of Engineering Science , Simon Fraser University . He holds a PhD in Biomedical Engineering from Duke University and has been recognized as a Michael Smith Foundation for Health Research Scholar . His research focuses on biomedical imaging , particularly optical coherence tomography (OCT) , microscopy , and low-coherence interferometry , with applications in diabetic retinopathy , Alzheimer’s disease , and age-related macular degeneration . Dr. Sarunic's work spans adaptive optics , deep learning , and sensorless OCT systems , emphasizing clinical translation and open-source software development (e.g., OCTAVA ). His Google Scholar publications highlight multimodal imaging , vascular heterogeneity analysis , and AI-driven diagnostics for retinal diseases. His contributions include the Michael Smith Foundation for Health Research Scholar award. Though not currently teaching courses, his collaborations and leadership in retinal imaging and medical device innovation are pivotal for advancing non-invasive diagnostics in neurodegenerative and diabetic conditions .

Melvin Wongمشاهده پروفایل

استادیار

• Transportation Engineering
• Machine Learning
• Urban Mobility
• +۲ مورد دیگر

Melvin Wong is an Assistant Professor in the Department of Urban Planning and Transportation within the Built Environment school at Eindhoven University of Technology. His research focuses on transportation engineering, machine learning applications in urban mobility, reinforcement learning for traffic systems, and sustainable transportation solutions. He utilizes advanced computational methods including graph neural networks, generative AI, and physics-informed models to address challenges in traffic prediction, electric vehicle infrastructure, and urban design. His research interests encompass transportation optimization, spatiotemporal modeling, generative design methods, and behavioral analysis in urban systems. Recent publications demonstrate a strong focus on AI-driven solutions for traffic management, battery-swapping systems, and multimodal design optimization. Dr. Wong has received recognition including the Best Research Paper Award (2024) and Swiss Government Excellence Scholarship (2020). He contributes to academic activities through conference presentations, peer reviews, and course development in urban mobility and big data analytics.

• Generative Programming
• Model-Based Development
• Software System Families
• +۷ مورد دیگر

Krzysztof Czarnecki is a Professor at the University of Waterloo's Department of Electrical and Computer Engineering, with a cross-appointment to the School of Computer Science. He serves as leader of the Waterloo Intelligent Systems Engineering Lab and holds the title of University Research Chair. His research focuses on generative software development, model-driven engineering, and autonomous systems, particularly in automotive cybersecurity and perception safety. Education: Doctorate in Computer Science, Technical University of Ilmenau (1999) Master of Science in Computer Science, Technical University of Ilmenau (1995) Bachelor of Science in Computer Science, California State University (1994) Research Interests: Dr. Czarnecki's work spans generative programming, software product lines, and safety-critical AI for autonomous vehicles. Recent projects address robust perception systems, uncertainty quantification in neural networks, and strategic driving behavior modeling. He co-authored Generative Programming (Addison-Wesley, 2000), a foundational text in the field. Publications Trends: Recent work emphasizes multimodal AI integration (e.g., LEO-MINI), 3D object detection improvements (OV-SCAN), and safety assurance frameworks for autonomous systems. His research bridges theoretical software engineering with applied robotics challenges. Awards: Premier’s Research Excellence Award (2004) British Computing Society’s Upper Canada Award (2008) University Research Chair, University of Waterloo (2023) Teaching & Leadership: Teaches courses like ECE 495 (Autonomous Vehicles) and ECE 651 (Software Engineering Foundations). Oversees WatCAR initiatives and collaborates on industry projects through the NSERC Bank of Nova Scotia Industrial Research Chair (previous). Labs & Teams: Directs the Waterloo Intelligent Systems Engineering Lab, focusing on AI-driven solutions for autonomous systems and safety-critical software. Active in cross-disciplinary collaborations with automotive and robotics partners.

• Architectural Design
• Evidence-Based Design
• Reinforcement Learning
• +۳ مورد دیگر

Raphaël Baur is a Lecturer at the Department of Humanities, Social and Political Sciences at ETH Zürich, with affiliations to the ETH AI Center and OAT X. His research focuses on integrating cognitive science and reinforcement learning into architectural design, emphasizing evidence-based methodologies and educational tools. He develops frameworks for human-centered design and investigates how feedback mechanisms can enhance AI-driven decision-making in architecture. His work spans interdisciplinary areas, including the application of machine learning to architectural learnability, the design of educational tools like MazeMastery for high school curricula, and the creation of toolkits such as DesignMind to bridge cognitive science and spatial design. Key publications highlight trends in reinforcement learning with human feedback, evidence-based architectural pedagogy, and the cognitive foundations of architectural practice. Baur’s contributions aim to advance both theoretical and applied aspects of design through computational and behavioral insights. Labs/Teams: Active member of the ETH AI Center and OAT X, collaborating on projects at the intersection of AI, education, and spatial design.

• Autonomous Industrial Systems
• Model Predictive Control
• Optimization
• +۷ مورد دیگر

Mehmet Mercangoz is an Associate Professor in Autonomous Industrial Systems at the Department of Chemical Engineering, Faculty of Engineering, Imperial College London. His research focuses on developing intelligent systems for industrial processes and energy systems, emphasizing safety, reliability, and sustainability through process modeling, model predictive control, optimization, and machine learning. His recent publications highlight applications of large language models , reinforcement learning , and hybrid mechanistic-machine learning models to address challenges in industrial automation, fault handling, energy storage, and gas compression systems. Key themes include decarbonization, electrification of process industries, and adversarially robust control frameworks. Current affiliations include Imperial College London and ABB, with prior roles at ABB Corporate Research Switzerland and ABB Future Labs. His work bridges chemical engineering , control theory , and artificial intelligence , targeting zero-emission industrial operations.