جستجوی استاد راهنمای دانشگاهی

Gabor Oroszمشاهده پروفایل

استاد

• Nonlinear dynamics and control
• Time delay systems
• Connected and automated vehicles
• +۶ مورد دیگر

Gabor Orosz is a Professor at the University of Michigan in both the Department of Mechanical Engineering and Department of Civil and Environmental Engineering . His work bridges nonlinear dynamics and control , time delay systems , and connected automated vehicles , with a focus on traffic flow optimization and vehicle safety . Education: PhD in Engineering Mathematics, University of Bristol, UK (2006) MSc in Engineering Physics, Budapest University of Technology and Economics, Hungary (2002) Research Focus : Orosz's research explores the intersection of vehicle automation , connectivity , and nonlinear dynamics . He investigates time delay effects in teleoperation , intent-sharing protocols for cooperative maneuvering , and control barrier functions for safety-critical systems . His work spans theoretical analysis, numerical validation, and real-world experimentation. Article Trends : Recent publications highlight advancements in latency mitigation for remote driving , nonholonomic vehicle control , intent-sharing frameworks , and energy-efficient strategies for connected vehicle systems . Themes include delayed feedback , stochastic communication , and safety-guaranteed control . Awards & Appointments : NSF CAREER Award (2014) Fulbright Scholar at Budapest University of Technology (2023-2024) Editorial roles in Vehicle System Dynamics (2020) and Time Delay Systems (2017) Student Mentorship : Orosz has advised numerous PhD students, including Anil Alan (2024, TU Delft), Chaozhe He (2018, University at Buffalo), and Tamás Molnár (2020, Wichita State University). His alumni work at institutions like Toyota Research Institute , Ford Motor Co. , and Zoox . Labs & Teams : He leads research at the University of Michigan's Mechanical Engineering Department and collaborates with international institutions such as Caltech and Budapest University of Technology . His team focuses on experimental validation of connected vehicle systems and delay-tolerant control .

Karen Mullenersمشاهده پروفایل

دانشیار

• Fluid Dynamics
• Unsteady Flow Phenomena
• Bio-inspired Robotics
• +۴ مورد دیگر

Karen Mulleners is an Associate Professor at the École polytechnique fédérale de Lausanne (EPFL), affiliated with the School of Engineering (STI), the Institute of Mechanical Engineering (IGM), and the UNFOLD Laboratory (Laboratoire de diagnostic des écoulements instationnaires). She also serves in the SGM-ENS teaching department and is a member of the EDEY-GE doctoral program commission. Her research focuses on experimental fluid dynamics, particularly unsteady flow phenomena and vortex dynamics. Professor Mulleners specializes in the intersection of fluid dynamics and bio-inspired engineering, with research interests including: Unsteady vortex-dominated flow phenomena Fluid-structure interaction in flexible systems Experimental methods for flow visualization and measurement Application of fluid dynamics principles to bio-inspired robotics Aerodynamic performance optimization of wind turbine systems Vortex dynamics in flapping and rotating wing systems Her recent publications (2022-2025) demonstrate a strong experimental focus on understanding complex fluid phenomena, particularly in bio-inspired robotics and renewable energy applications. Mulleners' work consistently addresses fundamental questions about vortex formation, flow control, and fluid-structure interactions, with significant contributions to understanding dynamic stall in wind turbines and undulatory swimming mechanics. Her research group employs advanced diagnostic techniques to study unsteady flows, often bridging engineering and biological principles. Professor Mulleners actively supervises PhD students and has directed multiple EPFL theses. Her teaching responsibilities include courses on Measurement Techniques and Aerodynamics, where she imparts knowledge on experimental methods for observing and measuring physical variables such as force, resistance, temperature, flow velocity, and structural deformation. The UNFOLD Laboratory, which Professor Mulleners leads, focuses on diagnostic techniques for unsteady flow phenomena, employing advanced experimental methods including flow visualization, particle image velocimetry, and force measurement systems to study complex fluid dynamics problems with applications in renewable energy and bio-inspired engineering.

David Parkerمشاهده پروفایل

استاد

• Formal Verification
• Probabilistic Model Checking
• Quantitative Analysis
• +۲ مورد دیگر

David Parker is Professor of Computer Science at the University of Oxford and a Tutorial Fellow at Trinity College. His research focuses on formal verification methods for checking system correctness, particularly quantitative verification techniques for probabilistic systems. As leader of the PRISM and PRISM-games projects, he develops tools for analyzing safety, reliability, and performance properties in complex systems. Current research explores verification of AI systems, robust decision-making under uncertainty, and multi-agent systems using stochastic games. His work bridges theoretical foundations with applications in autonomous systems, robotics, and healthcare technology. Recent publications demonstrate advancements in probabilistic temporal logic, robust policy learning, and bisimulation techniques for Markov models. These works consistently emphasize formal guarantees for safety-critical applications and novel approaches to model checking. ETAPS Test-of-Time Tool Award (2024) HVC Award (2016) Professor Parker mentors PhD students in verification, control synthesis, and AI safety, with research funded by ERC, EPSRC, and industrial partners. He serves on editorial boards for Formal Aspects of Computing and ACM Transactions on Autonomous Systems.

Grace Kimمشاهده پروفایل

دانشیار

• Neurorehabilitation
• Wearable Technology
• Stroke Recovery
• +۳ مورد دیگر

Dr. Grace Kim is an Associate Professor in the Department of Occupational Therapy at New York University's Steinhardt School of Culture, Education, and Human Development. She holds a PhD in Occupational Therapy from NYU (2016), an MA in Occupational Therapy from Columbia University, and a BA in Psychology from UC Davis. She is a clinician-researcher affiliated with the Rehabilitation Medicine department at New York Presbyterian/Weill Cornell Medical Center, specializing in upper extremity robotics, outcome measurement, and stroke rehabilitation. Education: Bachelor's in Psychology: University of California, Davis Master's in Occupational Therapy: Columbia University PhD in Occupational Therapy: New York University (2016) Research Focus: Intersection of technology and neurorehabilitation Client-centered care for stroke survivors Wearable/mobile technology applications Shared decision-making approaches Awards/Grants: Mitchell Leaska Dissertation Grant (2014) Steinhardt Faculty Challenge Grant (2017) NYU Provost Mega-Seed Grant (2018) American Occupational Therapy Foundation Grant (2021) Dr. Kim teaches courses in Evidence-Based Practice, Neurorehabilitation, and Ethics at NYU Steinhardt. She mentors students in Occupational Therapy, Rehabilitation Science, and the R25 Research Education in Cardiovascular Conditions program at NYU's Rory Meyers School of Nursing. Her work emphasizes affordable technology solutions to improve real-world outcomes for stroke patients, including remote self-training programs and home-based interventions.

Vijay K. Shahمشاهده پروفایل

استادیار

• Networking
• Communications
• Controls
• +۷ مورد دیگر

Vijay K. Shah is an Assistant Professor in the Electrical and Computer Engineering Department at North Carolina State University, leading the NextG Wireless Lab. His research focuses on advancing wireless communication and network technologies for beyond 5G/6G systems, including O-RAN architecture, spectrum management, and AI-driven network optimization. Education: Ph.D. in Computer Science, University of Kentucky (2019) Bachelor's in Computer Science and Engineering, National Institute of Technology, Durgapur (2013) Research emphasizes open radio access networks (O-RAN), mmWave testbeds, and cross-layer optimization. Recent work highlights include ORAN-Bench-13K (LLM benchmarking), ZT-RIC (zero-trust security frameworks), and Milli-O-RAN (reconfigurable mmWave networks). His contributions span O-RAN applications (xApps/rApps), satellite-terrestrial coexistence, and AI-driven positioning systems. Experimental validations include 3GPP-compliant 5G positioning and adversarial attack defenses. Publications reflect expertise in O-RAN architecture evolution, spectrum policy tools (ASCENT), and UAV-based network coordination (GLIDE). Current projects explore LEO satellite constellations and resilient disaster response networks. Labs/Teams: Head of the NextG Wireless Lab at NC State, focusing on prototype development in O-RAN, 6G, and secure AI-driven networks.

• Machine Learning
• Computer Vision
• Biomedical Engineering
• +۶ مورد دیگر

Prof. Huiyu Zhou is a Professor of Machine Learning at the School of Computing and Mathematical Sciences, University of Leicester . He leads the Biomedical Image Processing Lab (BIPL) and serves as Deputy Director of the Research Centre for Artificial Intelligence, Data Analytics and Modelling (AIDAM) , and a member of the Apollo-Leicester Centre for Digital Health and Precision Medicine (CDHPM) . Previously, he held roles as Reader at the University of Leicester and Lecturer at Queen’s University Belfast. Education: BEng in Radio Technology, Huazhong University of Science and Technology, China MSc in Biomedical Engineering, University of Dundee, UK PhD in Computer Vision, Heriot-Watt University, UK His research focuses on AI applications in biomedical imaging , robotics , and health informatics . Current projects include advancing medical image analysis, secure learning systems, and interdisciplinary collaborations in precision medicine. He chairs editorial boards for journals like IEEE Transactions on Human-Machine Systems and Pattern Recognition , and serves as Associate Editor for ICRA and Area Chair for BMVC and IJCAI. Prof. Zhou holds administrative roles such as PGR Director of CMS (2022–2025) , MSc Programme Director (2018–2019) , and coordinates MSc Distance Learning in Informatics . He reviews grants for global bodies including Horizon Europe, UKRI, and the Wellcome Trust, and assesses faculty promotions in the UK, China/HK, Israel, and others. He also acts as an external examiner for institutions across the UK, Hong Kong, Australia, India, and China. Affiliations include the National Academy of Artificial Intelligence (NAAI) and collaborations in interdisciplinary teams for digital health and AI-driven biomedical research.

• Robotics
• Autonomous Systems
• Unmanned Aerial Vehicles (UAVs)
• +۹ مورد دیگر

Nabil Aouf is a Professor of Robotics and Autonomous Systems in the Department of Electrical and Electronic Engineering at City, University of London, a position he has held since January 2019. Previously, from 2006 to 2018, he was Professor of Autonomous Systems at Cranfield University’s Defence and Security campus, where he also served as Head of the System and Autonomy Group and Research Lead of the Centre of Electronic Warfare, Information and Cyber. He earned his PhD in Electrical Engineering from McGill University Faculty of Engineering between 1999 and 2002. His research focuses on Robotics, Autonomous Systems, UAV Navigation, Computer Vision, and Fault-Tolerant Control . Key areas include visual odometry, sensor fusion (vision/IMU, RGBD, thermal-visible), robust control for UAVs, fault diagnosis in inertial systems, 3D perception, and autonomous landing. His work integrates theoretical control methods with real-time implementation in aerospace and defense contexts. His recent publications reflect a strong emphasis on robust optimization, multispectral vision, and real-time autonomous navigation. Trends indicate a focus on enhancing autonomy under uncertainty—through illumination-invariant stereo matching, L∞ optimization, and robust feature matching—particularly for UAVs operating in challenging environments. Nabil Aouf has collaborated extensively with researchers such as M. Richardson, O. Araar, T. Mouats, and M. Boulekchour across numerous projects in UAV control, sensor fusion, and autonomy. While no scientific awards are listed in the provided text, his leadership roles and sustained publication record in high-impact journals and conferences underscore his academic contributions. He has supervised or collaborated with several advisees including S.H. Almutairi, L. Chermak, I. Vitanov, and D. Nam, contributing to both theoretical developments and practical implementations in autonomous systems. His work has applications in aerospace, defense, planetary exploration, and critical infrastructure inspection.

Trym Vegard Haavardsholmمشاهده پروفایل

مدرس

• Computer Vision
• Machine Learning
• Robotics
• +۳ مورد دیگر

Trym Vegard Haavardsholm is a 20% part-time Lecturer at the University of Oslo (UiO) within the Section for Autonomous Systems and Sensor Technologies. He also serves as Principal Scientist at the Norwegian Defence Research Establishment (FFI) and is a PhD candidate at the Department of Engineering Cybernetics, NTNU. His research focuses on computer vision, machine learning, robotics, and image analysis, with a particular emphasis on multispectral imaging systems and unmanned aerial vehicles (UAVs). Haavardsholm has contributed to advancements in sensor technologies for tactical reconnaissance, autonomous navigation, and real-time data processing. His work spans applications in defense, environmental monitoring, and emergency response systems. Research Interests Haavardsholm’s research integrates interdisciplinary approaches to develop innovative sensor systems and algorithms. Key areas include compact multispectral imaging for small UAVs, in-operation camera calibration, and anomaly detection in hyperspectral data. His contributions emphasize practical applications such as urban feature classification, collaborative indoor navigation, and bioaerosol detection. His work bridges theoretical computer science with applied engineering, addressing challenges in autonomous systems and sensor fusion. Publications His publications highlight trends in multispectral sensor design, UAV imaging, and real-time georeferencing. Recent work includes compact sensor systems for tactical use and advancements in pushbroom image rectification. Earlier research explored band selection algorithms for target detection and GPU-accelerated anomaly detection. Professional Roles As a lecturer, Haavardsholm contributes to academic supervision and teaching in autonomous systems. His dual role at FFI and UiO reflects his commitment to translating academic research into practical defense and civilian applications. His PhD candidacy at NTNU underscores his ongoing academic engagement in engineering cybernetics.

• Industrial Automation
• Control Systems
• Machine Learning Applications
• +۵ مورد دیگر

Valeriy Vyatkin is a Professor at the Department of Electrical Engineering and Automation, Aalto University. His research focuses on advancing industrial automation, control systems, and their integration with emerging technologies like machine learning and digital twins. He specializes in standards such as IEC 61499, addressing interoperability, formal verification, and performance optimization in distributed automation systems. Key research interests include: Physics-informed machine learning for industrial processes (e.g., steel rolling, reservoir engineering) Formal methods for control system validation and safety-critical applications Development of adaptive automation frameworks for Industry 5.0 challenges, including human-robot collaboration and energy systems Interoperability between legacy and modern industrial standards (OPAS, OPC UA) Recent work emphasizes real-time simulation, FPGA-based control prototyping, and AI-driven solutions for energy efficiency and sustainability in manufacturing, horticulture, and process industries. Publications span topics like robotic walker design, probabilistic model checking, and decentralized learning management systems. He collaborates on EU and industry-funded projects, focusing on digital twin implementation, edge computing, and virtual commissioning. His team develops tools for automated code generation, system migration, and anomaly detection in complex industrial settings.

• Autonomous Vehicles
• Intelligent Transportation Systems
• Robotics
• +۷ مورد دیگر

Miguel Ángel Sotelo Vázquez is a full Professor at the University of Alcalá, leading the INVETT Research Group (Intelligent Vehicles and Traffic Technologies). He holds the Department of Automatic Control and specializes in autonomous systems, particularly in path planning, sensor fusion, and human-vehicle interaction. His research integrates machine learning, robotics, and control theory to address challenges in intelligent transportation systems. He earned his Ph.D. in 2001 with a thesis on autonomous vehicle navigation in partially known environments. His work emphasizes real-world deployment, explainable AI, and safety-critical systems. Recent projects focus on lane change prediction, pedestrian behavior modeling, and cybersecurity for autonomous systems. Key contributions include neuro-symbolic frameworks for decision-making, real-time multi-physics field reconstruction, and cross-cultural studies of pedestrian interactions. He collaborates internationally on urban mobility resilience and hydrogen refueling infrastructure. Research Highlights : Development of knowledge graph-based prediction architectures Experimental validation of human-vehicle interaction in VR environments Creation of the SCOUT trajectory prediction framework

Jiayun Wangمشاهده پروفایل

پژوهشگر ارشد

• Computer Vision
• Machine Learning
• Computational Imaging
• +۳ مورد دیگر

Jiayun (Peter) Wang is a Postdoctoral Scholar Research Associate in the Department of Computing and Mathematical Sciences at the California Institute of Technology (Caltech). His research focuses on advancing AI-driven solutions in medical imaging, computational imaging, and computer vision. Current projects emphasize applying deep learning to diagnose ocular conditions like dry eye syndrome and improving 3D reconstruction techniques. Collaborations with institutions such as UC Berkeley, Microsoft, and NVIDIA highlight his interdisciplinary approach to solving real-world medical and imaging challenges. Research Interests: Medical AI and Healthcare Analytics Deep Learning Applications in Ophthalmology 3D Reconstruction and Scene Understanding Physics-Informed Neural Networks Compressed Sensing MRI Key Contributions: Developed machine learning models predicting dry eye-related outcomes using meibography images Pioneered physics-aware neural operators for ultrasound lung aeration mapping Advanced open-vocabulary 3D object detection systems Labs/Teams: Collaborates with Caltech's AI4Health initiative and NVIDIA's research group, contributing to medical imaging advancements through interdisciplinary teams.

• Artificial Intelligence
• Data Systems
• Large Language Models
• +۹ مورد دیگر

Joseph E. Gonzalez is an Associate Professor in the Department of Electrical Engineering and Computer Science at the University of California, Berkeley, co-director of the Sky Computing Lab and RISE Lab, and member of the Berkeley AI Research (BAIR) group. His work bridges artificial intelligence and data systems with significant impact on large language model infrastructure and deployment. His research focuses on large language models (LLMs) including tool use, RAG, and agent systems; LLM deployment infrastructure; edge-based machine learning; cloud computing innovations; and computer vision applications. He addresses the full machine learning lifecycle from training to serving, emphasizing real-time decision systems and secure execution environments. Scientific Awards: Okawa Research Grant NSF Expedition Award NSF CAREER Award Professor Gonzalez mentors 17 current graduate students and numerous former students/post-docs across AI and systems research. His work is funded by the NSF Expedition grant for the RISE Lab, an NSF CAREER Award, and industrial sponsors including major technology companies. He co-directs the RISE Lab advancing real-time intelligent secure execution systems, and the Sky Computing Lab pioneering cloud computing abstractions. These initiatives tackle low-latency systems, online learning algorithms, and security frameworks for intelligent decision-making in physical environments.

• Sound and Music Computing
• Human-Computer Interaction
• Human-Centered Computing
• +۶ مورد دیگر

Dr. Sam Ferguson is a Senior Lecturer at the School of Computer Science, University of Technology Sydney (UTS), with a multidisciplinary background in music performance, cognitive science, and psycho-acoustics. His research explores the intersection of sound, music, and human experience through creative coding, machine learning, and interactive systems. Key Research Areas: Sound and Music Computing, Human-Computer Interaction, Creative Coding, Cognitive Science, Installation Art, and Acoustics. Current Projects: ARC Linkage project on creative coding and multiplicitous media; industry collaborations on IoT-based audiovisual systems. Recent Publications: Focus on spatial audio complexity, gestural interaction with networked sound, music emotion recognition frameworks, and robotic performance through genre-based cultural platforms. Leadership Roles: Director of Teaching & Learning Engagement; former Deputy Head of School (Teaching and Learning); active in ACM Creativity and Cognition Steering Committee. Teaching: Courses like Digital Media Studio , Prototyping Physical Interaction , and Data Processing using R within UTS's interdisciplinary Software Development Studio.

Teemu Malmiمشاهده پروفایل

استاد

• Management Control
• Performance Measurement
• Management Accounting
• +۷ مورد دیگر

Teemu Malmi is a Professor in the Department of Accounting at the School of Business, Aalto University, Finland. He has been an influential figure in management accounting research, particularly in management control systems and performance measurement. His academic qualifications include a Doctoral degree (1997), Licentiate degree (1994), and Master's degree (1990), all in Business and Economics from the Helsinki School of Economics. His research interests span management control, performance measurement, digitalization in finance, public sector accounting, and organizational behavior. His work often integrates empirical analysis with case studies, including a notable investigation into Nokia’s management control challenges. He has published extensively in top-tier journals and contributed to major handbooks in accounting and information systems. The recent trend in his publications (2020–2025) reflects a growing emphasis on digital transformation, blockchain, data analytics, and the evolving role of finance functions. His research increasingly bridges traditional accounting with technology and public policy, especially in healthcare financing and sustainability. Scientific Awards: “Thirst for knowledge” (“Tiedon Jano”) award by JOKO Executive Education Oy (2001) Teemu Malmi has supervised at least five theses and led externally funded research projects, including the SOTE/Kaks project (2015–2016) on social and healthcare services. He has been actively involved in academic service, such as serving on editorial boards, hosting international scholars, presenting keynote lectures, and participating in funding organization committees. His media appearances demonstrate his engagement in public discourse on welfare policy and regional financing in Finland. There is no indication of part-time status, retirement, or former staff designation; he remains an active academic.

Ellen Kuhlمشاهده پروفایل

استاد

• Living Matter Physics
• Computational Biomechanics
• Soft Matter Modeling
• +۵ مورد دیگر

Ellen Kuhl serves as the Catherine Holman Johnson Director of Stanford Bio-X and the Walter B. Reinhold Professor in the School of Engineering at Stanford University. She holds dual appointments as Professor of Mechanical Engineering and, by courtesy, Bioengineering, leading interdisciplinary research at the convergence of physics, computation, and biology. Her academic credentials include: Habil., TU Kaiserslautern (2004) Ph.D., University of Stuttgart (2000) M.S., Leibniz University of Hanover (1995) B.S., Leibniz University of Hanover (1993) Kuhl pioneers Living Matter Physics , developing computational frameworks that integrate physics-based modeling with machine learning to simulate biological systems across scales. Her work spans cardiovascular dynamics (including the 400-member global Living Heart Project), neurodegenerative disease progression (Alzheimer's tau pathology), and sustainable food systems (mechanics of plant/fungi-based meats). Recent innovations focus on automated model discovery using constitutive neural networks to democratize simulation tools for soft matter systems, with applications in precision medicine and climate-resilient food innovation. Her lab actively bridges engineering fundamentals with urgent societal challenges in healthcare and planetary health. Her publication trajectory reveals accelerating integration of AI with biomechanics, particularly in automated constitutive modeling for diverse tissues and food materials. Key trends include uncertainty quantification in neural networks, physics-informed machine learning for digital twins, and democratization of simulation tools for non-experts – reflecting her commitment to accessible computational science. Major recognitions include: National Science Foundation Career Award (2010) Humboldt Research Award (2016) ASME Ted Belytschko Applied Mechanics Award (2021) ERC Advanced Grant (2024) Fellowships in ASME and AIMBE As Bio-X Director, Kuhl orchestrates major interdisciplinary initiatives connecting engineering with life sciences, securing substantial funding including the 2024 ERC Advanced Grant. Her leadership extends to the US National Committee on Biomechanics and World Council of Biomechanics, while her Living Heart Project demonstrates exceptional translational impact through industry/medical partnerships across 24 countries. The Living Matter Lab operates as a nexus for high-impact research, developing computational tools that transform cardiovascular medicine, decode neurodegenerative mechanisms, and engineer sustainable food alternatives. Current projects leverage AI to accelerate plant-based meat development, model elephant-trunk-inspired soft robotics, and personalize cardiac simulations – all unified by her vision of physics-driven machine learning for global challenges.