جستجوی استاد راهنمای دانشگاهی

• Remote Sensing
• GIS
• Ecology
• +۶ مورد دیگر

Dr. Harm Bartholomeus is an Assistant Professor of Remote Sensing at the Department of Geo-information Science and Remote Sensing, Wageningen University & Research. He received his MSc in Physical Geography (2000) and a Geography teaching degree (2001) from Utrecht University. His career includes part-time PhD research (2004–2009) on soil property estimation via spectral measurements and vegetation influence. Since 2009, he has focused on remote sensing techniques like imaging spectroscopy, LiDAR, and UAV-based methods applied to forestry, soil science, and climate studies. In 2012, he co-founded the Wageningen UR Unmanned Aerial Remote Sensing Facility (UARSF), advancing UAV research in environmental studies. His expertise spans Ecology, Geographical Information Systems (GIS), Remote Sensing, Soil Science, Spectroscopy, and 3D analysis. He teaches courses such as Advanced Earth Observation, Remote Sensing and GIS Integration, and MSc thesis supervision in Geo-information Science and Remote Sensing. His work integrates cutting-edge technologies for environmental monitoring, including terrestrial laser scanning and drone-based sensing. Research interests include vegetation-soil interactions, UAV applications in agriculture and ecology, and climate resilience. He collaborates on projects like the UARSF and contributes to global initiatives like the IDEAS-QA4EO network. No scientific awards are explicitly mentioned, but his contributions to UAV and LiDAR methodologies are widely recognized. He advises MSc students on thesis and internship projects in remote sensing and geo-information science.

Yinglong Heمشاهده پروفایل

مدرس

• Intelligent Transportation Systems
• Energy Management & Decarbonisation
• Vehicle & Powertrain Dynamics
• +۷ مورد دیگر

Dr. Yinglong He is a Lecturer in Automated Electrified Transport (AcT) Systems at the School of Mechanical Engineering Sciences, University of Surrey, UK, and an Honorary Assistant Professor at the School of Engineering, University of Birmingham. His research focuses on intelligent transportation systems, energy management, vehicle dynamics, and AI-driven optimisation. He has held positions including Postdoctoral Research Associate at the University of Cambridge and Technology Expert at the European Commission's Joint Research Centre (JRC). Research interests include autonomous vehicle control, traffic simulation, hybrid/electric vehicle dynamics, and sustainable transport solutions. Notable contributions include advancing microscopic traffic models for automated vehicles and optimising energy systems for hybrid powertrains. His work integrates machine learning, multi-agent systems, and data-driven approaches to address challenges in transport decarbonisation and safety. Recent publications highlight advancements in hybrid vehicle dynamics simulation, lithium-air battery modelling, and energy mapping of urban buildings. He has received the Chinese Government Award for Outstanding Self-Financed Students (2022) and is a Fellow of the Institute for Sustainability (IfS). His expertise spans interdisciplinary collaborations in automotive engineering, energy systems, and smart infrastructure.

• Computational Engineering
• Biomedical Engineering
• Digital Twin
• +۴ مورد دیگر

Professor Perumal Nithiarasu is a Professor and Director of Research in the College of Engineering at Swansea University. He also serves as Deputy Head of the College and Dean of Academic Leadership (Research Impact). His expertise spans computational engineering, biomedical engineering, and artificial intelligence, with a focus on blood flow dynamics and finite element methods. He has held leadership roles, including directing the Zienkiewicz Center for Computational Engineering and co-chairing international conferences like the Computational Biomedical Engineering series. Notable awards include the Zienkiewicz ICE Silver Medal (2002) and an EPSRC Senior Fellowship (2006). Research Interests: Computational Fluid Dynamics (CFD) Biomedical Engineering Applications Finite Element Method (FEM) Digital Twin Technology Artificial Intelligence in Biomedical Systems Scientific Contributions: Professor Nithiarasu has pioneered numerical methods like the Locally Conservative Galerkin (LCG) and developed influential tools such as the Zienkiewicz Lecture series. His work bridges computational models with clinical applications, including cardiovascular simulations and patient-specific coronary analysis. He leads a 30+ member research group and edits the International Journal for Numerical Methods in Biomedical Engineering . Grants & Impact: His research has been funded by EPSRC and other bodies, with applications in medical devices (e.g., glaucoma treatment shunts) and thermal systems. Collaborations include RAEng, IACM, and industry partners.

Timothy Cookمشاهده پروفایل

دانشیار

• Astrophysics
• Sounding Rockets
• Ultraviolet Instrumentation
• +۴ مورد دیگر

Timothy Cook is an Associate Professor in the Department of Physics & Applied Physics at the University of Massachusetts Lowell. He holds a PhD in Astrophysics from the University of Colorado (1991) and has extensive experience in developing space-borne and ground-based instrumentation. His research focuses on ultraviolet instrumentation, sounding rockets, and novel data analysis techniques, particularly for studying astrophysical dust, exoplanetary systems, and interstellar/intergalactic material. Key projects include the PICTURE and IMAGER sounding rockets, the SPINR payload for stellar dust studies, and the SCARI interferometer for interstellar medium analysis. He has led and collaborated on multiple NASA-funded missions and grants, including roles as PI and Co-PI on projects like the CLUE experiment and DWEL lidar system. His work integrates astrophysical observations with advanced optical engineering and remote sensing technologies. Education: PhD in Astrophysics, University of Colorado Boulder, 1991 BA in Physics, Johns Hopkins University, 1985 Research Interests: Dr. Cook specializes in designing and deploying instruments for space and terrestrial environments. His expertise spans sounding rocket payloads (e.g., PICTURE-B, LITES), ultraviolet spectroscopy, coronagraphy for exoplanet detection, and lidar systems for forest canopy analysis. He also investigates interstellar dust properties and circumgalactic material using novel tomographic and interferometric techniques. His work bridges astrophysical studies with practical engineering solutions for observational challenges. Grants & Collaborations: Principal Investigator for NASA-funded projects like the Interstellar Medium Absorption Gradient Experiment Rocket (2007) Co-PI for NSF grants developing the Dual-Wavelength Echidna Lidar (DWEL) for forest biomass studies (2009) Contributed to the TERRIERS satellite mission studying Earth’s ionosphere Labs & Teams: Collaborates with interdisciplinary teams at UMass Lowell, NASA, and institutions like Boston University, leveraging expertise in optical instrumentation and data analysis.

Anna Goldenbergمشاهده پروفایل

دانشیار

• Machine Learning
• Artificial Intelligence in Healthcare
• Biomedical Informatics
• +۳ مورد دیگر

Anna Goldenberg is an Associate Professor in Computer Science at the University of Toronto and Senior Scientist in the Genetics & Genome Biology Program at The Hospital for Sick Children (SickKids). She holds the Varma Family Chair in Biomedical Informatics & AI and CIFAR AI Chair. Her roles include Co-chair of SickKids' AI in Medicine Initiative and Associate Research Director, Health at Vector Institute. Goldenberg's research focuses on developing machine learning methods for healthcare applications, including disease mechanisms, risk prediction systems, and responsible AI deployment. Affiliations: SickKids Research Institute, University of Toronto, Vector Institute Education: PhD from Carnegie Mellon University, Postdoc in Computational Biology Research Interests: Machine learning for healthcare, biomedical informatics, precision medicine, and overcoming AI challenges like rare event prediction and model explainability. Her lab emphasizes ethical AI integration into clinical workflows. Publications: Over 119 peer-reviewed articles spanning AI in medicine, genomics, and clinical prediction systems. Recent work includes early cancer detection via cell-free DNA and AI-driven pediatric urology management. Awards: Early Researcher Award, CIFAR AI Chair, Canadian Research Chair in Computational Medicine Grants: Multiple NIH, CIHR, and industry-funded projects totaling ~$20M, including grants for AI in cardiac arrest prediction, pediatric asthma modeling, and cancer detection via federated learning. Labs/Teams: Leads Goldenberg Lab at SickKids focusing on clinical AI systems and responsible innovation in healthcare technology.

Agata Rozgaمشاهده پروفایل

پژوهشگر

• Computational Behavioral Science
• Machine Learning
• Developmental Health
• +۴ مورد دیگر

Agata Rozga is a Principal Research Scientist at the Georgia Institute of Technology's School of Interactive Computing, specializing in computational behavioral science. She leads translational research for the NSF-funded AI-CARING Institute, focusing on AI technologies to support aging in place. Additionally, she directs the Technology Core of Emory’s Cognitive Empowerment Program, integrating sensors and AI into care networks for individuals with Mild Cognitive Impairment (MCI). Her work bridges computing and psychology to develop tools for behavioral monitoring in autism and neurological conditions. Dr. Rozga’s research has been funded by NSF, NIH, and the Simons Foundation. Key areas include longitudinal analysis of communication behaviors in autism, particularly language development, and AI-driven systems for cognitive decline monitoring. She previously served as Head of Product at Diligent Robotics, optimizing service robots for clinical settings, and collaborated with Behavior Imaging to advance telehealth autism diagnostics. Her academic affiliations include the GVU Center at Georgia Tech, emphasizing human-centered computing. Awards and grants include NSF and NIH funding for projects addressing autism and MCI. She actively deploys health technologies in real-world settings, combining quantitative and qualitative methods to assess impacts on patients and caregivers.

Zhengwu Zhangمشاهده پروفایل

دانشیار

• Machine Learning
• Brain Connectomics
• Deep Learning
• +۴ مورد دیگر

Zhengwu Zhang is an Associate Professor in the Department of Statistics and Operations Research at the University of North Carolina at Chapel Hill. His research focuses on developing statistical and machine learning methods for analyzing high-dimensional neuroimaging data, particularly structural and functional brain connectomics. He leads the UNC Education Program of Intelligence and Connectomics (EPIC), an interdisciplinary initiative training students in brain network analysis. His work addresses challenges in large-scale neuroimaging datasets, including computational efficiency and reproducibility. Zhang completed his Ph.D. in Statistics at Florida State University under Anuj Srivastava. His funding includes NIH grants for CRCNS, structural connectome analysis, and personalized cognitive training. He serves as an Associate Editor for the Journal of the American Statistical Association (Reproducibility). Key contributions include tools like the Surface-Based Connectivity Integration (SBCI) GitHub repository for brain network analysis pipelines. His awards include the 2022 UNC Junior Faculty Development Award and the Oak Ridge Powe Award. Teaching roles include courses on data science, machine learning, and statistical consulting. His research spans brain network dynamics, genetic contributions to connectome structure, and applications of deep learning in neuroscience.

• Magnetic Resonance Imaging (MRI)
• Neuroimaging Biophysics
• Brain Metabolism Analysis
• +۶ مورد دیگر

Harald E. Möller is a Professor and Head of the Nuclear Magnetic Resonance Research and Development Unit at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig. With a career spanning over four decades, he has held academic positions including Honorary Professor at the University of Leipzig and leadership roles in institutions like Duke University Medical Center and the University of Münster. His research focuses on advancing MRI methodologies, biophysical imaging principles, and their applications in neurology and neuroscience. Education: 1979-1985: Chemistry & Physics studies at Universities of Dortmund and Münster 1985: M.Sc. (Diploma) in Chemistry 1988: PhD in Physical Chemistry (summa cum laude) 2000: Habilitation in Physical Chemistry 2002: Habilitation in Biophysical Chemistry Research Interests: Development of novel MRI methods Quantitative tissue characterization Myelin sheath imaging Cerebral blood flow dynamics High-field MRI hardware

James Forbesمشاهده پروفایل

دانشیار

• Dynamics and Control
• Robotics
• Nonlinear Control
• +۵ مورد دیگر

James Forbes is an Associate Professor in the Department of Mechanical Engineering at McGill University. He holds the title of William Dawson Scholar and is affiliated with the Dynamics Estimation & Control of Aerospace & Robotics Systems research group. His primary research focus is on Dynamics and Control, with emphasis on navigation, guidance, and control (GNC) techniques for robotic systems. He teaches courses such as MECH 309 (Numerical Methods), MECH 412 (System Dynamics), and advanced topics in control systems. Forbes earned his Ph.D. in Aerospace Science and Engineering from the University of Toronto, following an M.A.Sc. from the same institution and a B.A.Sc. in Mechanical Engineering from the University of Waterloo. His research interests include nonlinear state estimation (batch methods, filtering), control synthesis via optimization (LQR, LMI approaches), and data-driven modeling using Koopman operator techniques. Applications span unmanned aerial vehicles (UAVs), autonomous underwater vehicles (AUVs), and SLAM systems. He has developed the navlie Python package for state estimation on Lie groups. Notable awards include the William Dawson Scholar distinction. His recent work focuses on multi-UAV localization, robust control algorithms, and sensor fusion techniques. He collaborates on projects involving UWB-based positioning and inertial navigation systems.

• Cyber-Physical Systems
• Machine Learning
• Autonomous Systems
• +۵ مورد دیگر

Radu Grosu is a Professor at Technische Universität Wien (TU Wien), leading the Forschungsbereich Cyber-Physical Systems . His research focuses on Cyber-Physical Systems (CPS), Machine Learning, and autonomous robotics, with notable contributions to neural network architectures like Liquid Time-Constant Networks (LTC) and their applications in robotics and medical imaging. He is affiliated with the Network Lab and has supervised numerous PhD and Master's students, including Sebastian Michael Bittner, Daniel Scheuchenstuhl, and Sophie Neubauer. His work spans topics such as reinforcement learning, autonomous driving, and IoT ecosystems. Recent projects include developing robust AI systems for healthcare and robotics, such as tumor delineation using PET imaging and neuromorphic IoT architectures for smart villages. Grosu has published extensively on CPS, with over 146 contributions across peer-reviewed journals and conferences. His research emphasizes bridging theory and practice, addressing challenges in safety, scalability, and real-time control in autonomous systems. Key research interests include robotic perception, neural network robustness, and CPS/IoT integration. He has pioneered methods like DeepSTL for translating temporal logic requirements into neural network training objectives and developed frameworks like NimbleAI for neuromorphic sensing-processing systems. His team also explores distributed control algorithms for multi-agent systems, such as flocking drones and formation control using relative distance measurements. Recent work examines the generalization properties of deep filters in CNNs and quantum-classical reinforcement learning models for game AI. Grosu has advised over 20 students on topics ranging from deep learning in wafer defect analysis to bio-inspired neural circuits for auditable autonomy. His lab collaborates on interdisciplinary projects, such as applying AI to battery health estimation and prostate cancer diagnostics. He actively contributes to academic communities, editing special issues on AI in healthcare and CPS resilience, and has organized summer schools on CPS and IoT systems.

Evangelos Boukasمشاهده پروفایل

استادیار

• Robotics Vision
• Autonomous Systems
• Machine Learning
• +۳ مورد دیگر

Evangelos Boukas is an Assistant Professor at Aalborg University Copenhagen and a former PhD Candidate in Robotics Vision at the Democritus University of Thrace. He is affiliated with the Group of Robotics & Cognitive Systems (Gryphon Lab) and ESA's Automation and Robotics Laboratory (ESTEC). His research focuses on robotic vision, autonomous systems, and space exploration technologies. He has contributed to projects like SPARTAN and ESA-funded localization initiatives. Boukas has co-taught courses in Robotics, Mechantronics, and Robotics Vision, and co-supervised diploma theses in areas like 3D object recognition and robot navigation. His work employs open-source frameworks (ROS/ROCK) and tools like MATLAB. Education: PhD Candidate (2012-current): Robotics Vision, Democritus University of Thrace MSc (2010-2012): Electrical and Computer Engineering, Democritus University of Thrace Diploma (2005-2010): Production & Management Engineering, Democritus University of Thrace Research Highlights: Designed robots like the Heavy Duty Planetary Rover (HDRP) and MAGGIE, developed algorithms for planetary rover localization, and worked on ESA-funded projects. His interests include modular robotics systems and probabilistic approaches in autonomous systems. Labs/Teams: Gryphon Lab, ESA's Automation and Robotics Laboratory (ARL).

Ben Hodgesمشاهده پروفایل

استاد

• Environmental and Water Resources Engineering
• Urban Stormwater Drainage Modeling
• Computational Fluid Dynamics (CFD)
• +۴ مورد دیگر

Ben Hodges is a Professor in the Civil, Architectural and Environmental Engineering (CAEE) Department at the University of Texas at Austin, holding the Marion E. Forsman Centennial Professorship in Engineering. He specializes in environmental and water resources engineering, with a focus on computational fluid dynamics (CFD), urban stormwater drainage modeling, and river dynamics. His research bridges hydraulics, geospatial analysis, and environmental fluid mechanics, addressing challenges like flood modeling, water distribution systems, and supersaturated dissolved gas management. Education: Ph.D., Civil Engineering, Stanford University (1997) M.S., Mechanical Engineering, George Washington University (1991) B.S., Marine Engineering/Nautical Science, U.S. Merchant Marine Academy (1984) Research Interests: Development of computational models (e.g., SPRNT, Frehd, SUNTANS) Oil spill transport modeling, saltwater intrusion, and continental river networks High-performance parallel algorithms and hydraulic simulation tools His work emphasizes practical applications, such as designing stormwater systems and predicting environmental impacts like oil spill trajectories. He collaborates on projects with organizations like IBM Research Austin and the U.S. EPA, contributing to tools like the SWMM5+ and PTSNet simulators. Hodges advises a dynamic graduate research group (JETlab) and maintains active involvement in academic conferences and international collaborations.

Ping Wangمشاهده پروفایل

استاد

• Chinese Intellectual History
• Literature
• Medieval Philology
• +۳ مورد دیگر

Ping Wang is a Professor in the Department of Asian Languages & Literature at the University of Washington, serving as Graduate Program Coordinator and Graduate Admissions & Education Committee Chair. They hold a Ph.D. in Chinese Language and Literature from the University of Washington (2006). Their research focuses on Chinese Intellectual History, Medieval Philology, Poetry and Poetics, and Translation Studies. Recent research trends include interdisciplinary approaches to classical texts and modern translation methodologies. The listed articles highlight contributions to computational linguistics and AI, though primary disciplinary focus remains humanities. No specific awards are listed, though the department's 'Awards & Honors' page may contain institutional recognitions. Advising emphasizes graduate education coordination rather than individual student mentorship. Active in departmental administration and curriculum development. Research affiliations include the Bilingual and Biliteracy Research Lab and the Early Buddhist Manuscript Project. Office located in GWN M246 with regular Thursday office hours.

Daniel W. Apleyمشاهده پروفایل

استاد

• Industrial and engineering statistics
• Statistical learning and data mining
• Predictive analytics
• +۷ مورد دیگر

Daniel W. Apley is Professor of Industrial Engineering and Management Sciences at the McCormick School of Engineering and Applied Science, Northwestern University, where he has served since 2003. He is Editor-in-Chief-Elect of Technometrics and previously Editor-in-Chief of the Journal of Quality Technology . He is also affiliated with Northwestern’s Master of Science in Machine Learning and Data Science Program. Education PhD Mechanical Engineering, University of Michigan, Ann Arbor MS Electrical Engineering, University of Michigan, Ann Arbor MS Mechanical Engineering, University of Michigan, Ann Arbor BS Mechanical Engineering, University of Michigan, Ann Arbor Research Interests Professor Apley is an industrial statistician whose work sits at the intersection of engineering modeling, statistical analysis, and predictive analytics. His major thrusts include statistical modeling of complex engineering and enterprise systems, machine learning for manufacturing data, quality engineering and Six Sigma methodologies, and computer-experiment–based design optimization under uncertainty. Recent applications span healthcare risk modeling, credit-risk analytics, materials microstructure prediction, and autonomous process control. Scientific Awards NSF CAREER Award IIE Transactions Best Paper Award (Quality & Reliability) Wilcoxon Prize for best practical application paper in Technometrics Teaching & Advising At Northwestern he teaches undergraduate courses in Statistical Methods for Quality Improvement, Introductory Statistics, and Statistical Tools for Data Mining, as well as graduate courses in Predictive Analytics, Engineering Applications of Data Mining, and Intermediate Statistics. His research has been supported by numerous industrial partners and federal agencies, underscoring a strong record of funded graduate and post-doctoral advising. Leadership & Service Beyond editorial roles, Professor Apley has chaired the Quality, Statistics & Reliability Section of INFORMS and served as Director of the Manufacturing and Design Engineering Program at Northwestern, shaping interdisciplinary curriculum and research initiatives.

• Human-Robot Interaction
• Assistive Robotics
• Machine Learning in Robotics
• +۵ مورد دیگر

Prof. Dongheui Lee is a Full Professor at TU Wien's Institute of Computer Technology, Faculty of Electrical Engineering and Information Technology, and leads the Human-centered Assistive Robotics Group at the German Aerospace Center (DLR). She holds a PhD from the University of Tokyo (2007) and has held academic roles at Technical University of Munich (TUM), the University of Tokyo, and KIST. Her research focuses on human-robot interaction, assistive robotics, and machine learning applications in robotics. Education: PhD, Information Science and Technology, University of Tokyo, 2007 MS, Kyung Hee University, 2003 Research Interests: Her work spans human motion understanding, assistive robotics, human-robot collaboration, and control systems. Key areas include robotic balance assistance, motion imitation, and safety-aware robotics. She has pioneered methods for light touch support in human-robot interaction and developed frameworks for dynamic task execution. Recent Trends in Publications: Recent work emphasizes variable stiffness control, motion retargeting, and multimodal anomaly detection. Publications highlight advancements in assistive robotics, human motion prediction, and reinforcement learning for locomotion. Articles often integrate robotics with machine learning to improve safety and adaptability in human-robot systems. Awards: Carl von Linde Fellowship (TUM Institute for Advanced Study, 2011) Helmholtz professorship prize (2015) Best Intelligence Paper Award (2024) Projects & Grants: Leads projects like LunarAssembly (robotic assembly on the Moon) and INVERSE (interactive robots through reasoning). Funded initiatives include EU Horizon, BMBF, and industry collaborations. Coordinates teams for projects like PERSEO (service-oriented robotics) and SOLAR (body representation studies). Labs/Teams: Directs the Human-centered Assistive Robotics Group at DLR and collaborates with TU Wien's Autonomous Systems unit. Her teams focus on real-world applications in healthcare, manufacturing, and human-centered robotics.