Find Academic Supervisors

• Robotics
• Control Systems
• Human-Machine Interaction
• +4 more

F. Tajdari is a Research Fellow in the Department of Mechanical Engineering at Delft University of Technology, specializing in intelligent vehicles and robotics. Tajdari completed a PhD at TU Delft in 2023 with a dissertation on non-rigid 3D/4D human mesh registration, establishing expertise at the intersection of mechanical engineering and computer vision. Education: PhD in Mechanical Engineering, Delft University of Technology, 2023 Research focuses on advanced control systems for robotics and human-vehicle interaction, with significant contributions to non-rigid mesh registration, adaptive feedback control, and intelligent transportation. Tajdari's work integrates deep learning (Graph Convolutional LSTM networks) with mechanical control theory to solve dynamic shape registration and vehicle navigation challenges, particularly for vulnerable road users. Recent publications (2022-2025) reveal a cohesive research trajectory: evolving from Stewart robot control (2022) to sophisticated non-rigid registration techniques (2024), culminating in vehicle systems for human-robot coexistence (2025). The work consistently bridges mechanical engineering with computer science, emphasizing real-world applications in safety-critical environments. No scientific awards were documented in the available records. No information regarding student advising or research grants was identified in the source materials. Tajdari operates within TU Delft's Intelligent Vehicles research ecosystem, collaborating extensively with the Delft Robotics Institute and contributing to projects involving multi-agent vehicle systems and human-centered robotics, as evidenced by co-authorship with leading researchers in autonomous systems.

Shaodi YouView profile

Associate Professor

• Physics Based Vision
• Computer Vision
• Machine Learning
• +4 more

Dr. Shaodi You is an Associate Professor at the University of Amsterdam (UvA), working in the Computer Vision Research Group within the Institute of Informatics, Faculty of Science. He received his Ph.D. and M.E. degrees from The University of Tokyo, Japan in 2015 and 2012, and his bachelor's degree from Tsinghua University, P. R. China in 2009. His research focuses on three main areas: Physics Based Vision, Perception Based Vision and Learning, and 3D geometry. His work specifically addresses challenging problems such as rain and water drop detection/removal, liquid optics, dehazing, and non-rigid 3D geometry. He is particularly known for his work on adherent raindrop detection and removal in video, which was presented at CVPR 2013 and later published in IEEE TPAMI. His recent publications (2024-2025) demonstrate a strong focus on physics-based vision problems, including rain location prior learning for deraining, generalized segmentation for foggy scenes, and hyperspectral image processing. His research integrates deep learning techniques with physics-based models to solve challenging computer vision problems in adverse conditions. Many of his recent works address weather-related vision challenges, building on his earlier foundational work on raindrop detection. Dr. You serves as an area chair for major computer vision conferences (CVPR, ICCV, ECCV, and NeurIPS) and is an associate editor of the International Journal of Computer Vision (IJCV). He is the founder of the International Workshop on Physics Based Vision meets Deep Learning (since 2017), which has become an important venue for research at the intersection of physics-based modeling and deep learning in computer vision. He has supervised students and supported the CSC scholarship program. His research group has developed several important datasets and projects, including GTAV-NightRain, Atlantis, and BUJT-UvA hyperspectral datasets, which have contributed significantly to the computer vision community.

Yaser Ajmal SheikhView profile

Associate Professor

• Computer Vision
• 3-D Vision and Recognition
• Human Behavior Analysis
• +8 more

Yaser Ajmal Sheikh is an Associate Professor at the Robotics Institute of Carnegie Mellon University (currently on leave) and serves as Director of Facebook Reality Lab in Pittsburgh. His work focuses on "metric telepresence": remote interactions in AR/VR that are indistinguishable from reality. He has made significant contributions to machine perception and rendering of social behavior, spanning computer vision, computer graphics, and machine learning. Dr. Sheikh's research encompasses analyzing dynamism in scenes from moving cameras, with particular focus on dynamic motion reconstruction, human behavior analysis, estimation of nonrigid motion, and modeling moving cameras in spacetime. His work has led to breakthroughs in social robotics, 3D vision and recognition, and multisensor data fusion. He founded and directs the Facebook Reality Lab in Pittsburgh, continuing his work on human-centered robotics and social robots. His publication record demonstrates consistent innovation in computer vision and graphics, with emphasis on human motion capture, facial animation, and social interaction modeling. His most recent work focuses on neural approaches to facial animation, full-body avatars, and capturing the complex dynamics of human interaction. The research shows a clear trajectory from foundational work in motion capture to sophisticated neural approaches for realistic human representation in virtual environments. Popular Science's Best of What's New Award Honda Initiation Award (2010) Best paper awards at WACV (2012), SAP (2012), SCA (2010), ICCV THEMIS (2009) First place in the MSCOCO Keypoint Challenge (2016) Hillman Fellowship for Excellence in Computer Science Research (2004) Dr. Sheikh has advised numerous doctoral and master's students who have gone on to prestigious positions in academia and industry. His research has been generously supported by government agencies including NSF and DARPA, as well as industrial partners such as Intel, Disney, Nissan, Honda, Toyota, and Samsung. He has served on senior committees at major conferences including SIGGRAPH, CVPR, ICRA, and ICCP, and was an Associate Editor of CVIU. He leads the Panoptic Studio project, a massively multiview system for social motion capture that has produced the CMU Panoptic Studio Dataset. His work on OpenPose has become a standard tool in the field for real-time 2D hand, body, and face keypoint detection. His current research at Facebook Reality Lab continues to push the boundaries of what's possible in virtual and augmented reality through advanced computer vision techniques.

Jun LiView profile

Professor

• Structural Health Monitoring
• Artificial Intelligence
• Computer Vision
• +5 more

Professor Jun Li is a tenured Professor and Australian Research Council (ARC) Future Fellow at Curtin University's School of Civil and Mechanical Engineering. He leads the Curtin Research Centre for Infrastructural Monitoring & Protection, focusing on advancing structural health monitoring (SHM) through AI, computer vision, and data analytics. His work integrates emerging technologies to monitor civil infrastructure like the Matagarup Bridge and Stirling Bridge in Western Australia. Affiliations: School of Civil and Mechanical Engineering, Curtin Research Centre for Infrastructural Monitoring & Protection Grants: Chief investigator for 10 projects, including ARC and CRC grants Awards: Nishino Prize (2019), Curtin Mid-Career Research Award (2023), Hong Kong PolyU Young Alumni Award (2024) His research interests span structural dynamics, AI-driven SHM, IoT sensing, and digital twins. Over 230 technical publications, including 170 journal papers, reflect his expertise. He advises 6 PhD students and collaborates on industry projects, emphasizing sustainable infrastructure resilience. Key contributions include developing vision-based displacement measurement systems, physics-guided neural networks, and data-driven damage detection frameworks. His work bridges academia and industry, enhancing infrastructure safety and maintenance efficiency.

Dmitri BuragoView profile

Professor

• Geometry
• Dynamical Systems
• Algorithmic Complexity
• +6 more

Dmitri Burago is a Distinguished Professor in the Department of Mathematics at Pennsylvania State University (Penn State). He holds a Ph.D. from St. Petersburg State University (1992) and has been affiliated with Penn State since 1995, progressing through roles from Assistant Professor to his current rank. His research focuses on geometry, dynamical systems, and algorithmic complexity, with notable contributions to metric geometry, Finsler geometry, and boundary rigidity problems. Education: Ph.D., St. Petersburg State University (1992); Candidate of Physical and Mathematical Sciences (1992); M.S. (Summa Cum Laude), St. Petersburg State University (1986). Research interests include geometric analysis, billiards dynamics, and the interplay between geometry and topology. His work has led to breakthroughs in areas such as the rigidity of metrics, geodesic flows, and the study of hard ball systems. He co-authored the influential book A Course in Metric Geometry (2001), which received the Leroy P. Steel Prize in 2014. Burago has received numerous accolades, including the Sloan Research Fellowship (1997–2001) and the Steel Prize for Exposition (2014). He serves on editorial boards of journals like Geometry and Topology and has organized conferences globally. His teaching spans undergraduate, graduate, and special topics courses, with extensive experience mentoring students and advising on research projects. He has led over a dozen NSF-funded research grants, totaling millions in funding, and advised numerous Ph.D. and master’s students. His professional service includes roles on university committees, panels for NSF, ISF, and other agencies, and participation in international conferences. His work bridges pure mathematics with applications in geometric control theory and inverse problems.

Demetris P. GerogiannisView profile

Researcher

• Computer Vision
• Image Processing
• Image Segmentation and Registration
• +7 more

Demetris P. Gerogiannis is a researcher in the Department of Computer Science & Engineering at the University of Ioannina, Greece. His academic work focuses on Computer Vision and Image Processing, with specific expertise in image registration, pointset registration, and feature extraction. He has maintained a consistent publication record from 2007 through 2020, primarily collaborating with Christophoros Nikou and A. Likas. Gerogiannis' research interests span multiple areas within Computer Vision including Image Segmentation and Registration, Pointset Registration, Feature Extraction, Object Detection and Recognition, Pattern Recognition, Automatic Image Annotation, and Machine Learning applications. His work demonstrates a consistent focus on developing robust algorithms for geometric problems in vision, with particular attention to handling noise and outliers in visual data. He has made significant contributions to point set registration, shape representation, and vanishing point detection. His publication history reveals a strong trend toward developing mathematically rigorous approaches to Computer Vision problems, particularly utilizing statistical models like Student's t-distributions for robust registration. His work bridges theoretical computer vision with practical applications, including consumer-facing technologies like QR code systems. The 15 most recent publications show progression from foundational work in image registration to more applied research in texture analysis, word spotting, and biomedical applications. Gerogiannis is also an active entrepreneur, currently coordinating QReca!, a startup focused on consumer engagement through QR codes and NFC technology. He has previously worked on a Video On Demand startup and is developing a spin-off to commercialize his Computer Vision research through an API framework. He maintains a commitment to open academic knowledge by providing Matlab code for non-commercial use related to his publications. He has volunteered for the Athens 2004 Olympic Games and maintains a passion for Mathematics, particularly Number Theory and Euclidean Geometry. His academic profile suggests a researcher who bridges theoretical computer vision with practical applications and entrepreneurial ventures.

Hai WangView profile

Professor

• High-Speed Propulsion
• Combustion Chemistry
• Renewable Energy Conversion
• +5 more

Hai Wang is a Professor of Mechanical Engineering at Stanford University with a distinguished career in combustion science, high-speed propulsion, and renewable energy conversion. His research spans combustion chemistry of conventional and renewable fuels, detonation dynamics, quantum-chemistry guided battery materials design, and transport theories in nanoparticle systems. Education : Ph.D. in Fuel Science from Pennsylvania State University (1992), M.S. in Chemical Engineering from Michigan Technological University (1986), B.Eng. in Polymer Materials from East China University of Science and Technology (1984). He has authored seminal works on soot formation in flames, catalytic oxidation of methane, and laminar flame speed modeling. His recent publications address interdisciplinary challenges such as eco-anxiety and advanced data plane verification. Notable administrative roles include co-founding Hestia Tec, LLC (2010-2014) and serving as President of the Combustion Institute (2024-present). Wang’s work has been recognized with the Humboldt Senior Research Award (2019), Mercator Fellow (2019), and multiple fellowships. He mentors doctoral and master’s students and leads research initiatives like the Combustion Energy Frontier Research Center (2010-2014).

Hongkai ZhaoView profile

Professor

• Applied and Computational Mathematics
• Inverse Problems
• Imaging
• +4 more

Professor Hongkai Zhao is a Chancellor’s Professor and Chair of the Mathematics Department at the University of California, Irvine (UCI), with a joint appointment in Computer Science. He holds a B.S. from Peking University, M.S. from USC, and Ph.D. from UCLA, all in Mathematics. His research focuses on computational and applied mathematics, including moving interface problems, inverse problems, imaging, and Hamilton-Jacobi equations. He is renowned for developing the Fast Sweeping Method (FSM) for solving hyperbolic PDEs efficiently. Education: B.S. (Peking University, 1990), M.S. (USC, 1992), Ph.D. (UCLA, 1996). Employment: UCI faculty since 1999, Szego Assistant Professor at Stanford (1996–1999). Research interests span computational fluid dynamics (moving interfaces), inverse problems (tomography, wave propagation), and imaging (medical imaging, computer vision). Key contributions include numerical methods for surfactant-laden drops, fast algorithms for Hamilton-Jacobi equations, and 4D cone-beam CT reconstruction. Awards include the Feng Kang Prize (2007), Sloan Fellowship (2002–2004), and Chancellor’s Professorship (2016–present). He has authored over 100 publications on numerical methods, inverse problems, and PDEs. Lab affiliations: Active in interdisciplinary research at UCI, leading projects in computational mathematics and imaging science. Current work includes high-frequency wave propagation, multi-scale modeling, and geometric data analysis.

• Maxillofacial Trauma
• Temporomandibular Joint Surgery
• Oral and Maxillofacial Pathology
• +3 more

Dr Nigel Johnson is a Senior Lecturer in Oral and Maxillofacial Surgery at the School of Dentistry, The University of Queensland (UQ). He is actively involved in research and supervision of higher-degree students. His affiliations include the UQ Faculty of Health, Medicine and Behavioural Sciences and the Centre for Orofacial Regeneration, Reconstruction and Rehabilitation (COR3). Dr Johnson’s research focuses on Maxillofacial Trauma , Temporomandibular Joint Surgery , Oral and Maxillofacial Pathology , and Regenerative Medicine . His work often involves systematic reviews and clinical studies on fracture management, surgical techniques, and tumor pathology. Notable projects include investigations into mandibular angle fractures, orbital reconstruction, and the application of bioprinting in regenerative therapies. His publications highlight trends in trauma management, such as the impact of e-scooter-related injuries and advancements in fixation techniques. Dr Johnson has secured grants from the Australia and New Zealand Association of Oral & Maxillofacial Surgeons (ANZAOMS) for studies on maxillofacial trauma and benign jaw lesions. He currently supervises two students: one exploring Evolving trends in the surgical management of maxillofacial trauma (PhD) and another investigating Epidemiology of benign intraosseous jaw pathology (MPhil). No awards are explicitly listed, though his research contributions reflect significant scholarly activity.

Amir VaxmanView profile

Associate Professor

• Geometry Processing
• Discrete Differential Geometry
• Directional-Field Design
• +4 more

Amir Vaxman is a Reader in Graphics, Simulation and Visual Computing at the School of Informatics, The University of Edinburgh, where he leads the Geometry Processing Group within the Institute of Perception, Action and Behaviour. Previously, he was an assistant professor at Utrecht University in the Netherlands and a postdoctoral fellow at TU Wien (Vienna) where he received the Lise-Meitner fellowship. Ph.D. in Computer Science, Technion-IIT, 2011 M.Sc. in Computer Science, Technion-IIT, 2006 B.Sc. in Computer Engineering, Technion-IIT, 2002 Dr. Vaxman's research focuses on geometry processing and discrete differential geometry , with particular interests in directional-field design, unconventional meshes, constrained shape spaces, architectural geometry, and medical applications. His work bridges theoretical mathematics with practical applications in computer graphics, visualization, and fabrication. His recent publications (2023-2025) demonstrate a strong focus on geometric modeling, neural surface reconstruction, and directional field synthesis. Key trends include the development of novel algorithms for mesh processing, integration of deep learning with geometric modeling, and applications to medical imaging and architectural design. Best Paper Award (Honorable Mention) at SIGGRAPH 2024 for "Fabric Tessellation: Realizing Freeform Surfaces by Smocking" Runner-Up Best Paper Award at Symposium on Geometry Processing (SGP) 2023 for "BPM: Blended Piecewise Möbius Maps" Software Award at Symposium on Geometry Processing 2021 for libhedra Dr. Vaxman has supervised numerous graduate students including 5 current PhD candidates and over 20 graduated students. He serves in editorial roles for ACM Transactions on Graphics and has been program co-chair for multiple major conferences including Shape and Physical Modeling (SPM) 2026. He leads the Geometry Processing Group at the University of Edinburgh, which focuses on developing cutting-edge algorithms for geometry processing, with applications ranging from computer graphics to architectural design and medical imaging.

Timo PlathView profile

Researcher

• Multiscale Modeling
• Suspensions Rheology
• Granular Matter
• +5 more

Timo Plath is a Researcher at the Multiscale Mechanics (MSM) group within the Faculty of Thermal and Fluid Engineering at the University of Twente, Netherlands. He is affiliated with the Department of Engineering Technology and the MESA+ Institute for Nanotechnology. As a PhD candidate, his work bridges engineering technology and computational modeling for industrial applications. Plath specializes in multidisciplinary engineering with research interests spanning contact mechanics, granular matter, non-Newtonian flow rheology, concrete simulation, and industrial process modeling. His expertise particularly focuses on developing multiscale models where both micro and macro-scale effects are critical, with significant contributions to wet granulation processes and particle dynamics simulation. His publication trend shows consistent advancement in particle-based modeling techniques, particularly through the open-source MercuryDPM software platform. Recent work emphasizes quadrature method of moments applications, non-dimensionalization approaches, and virtual prototyping for industrial granulation processes, demonstrating a clear trajectory from fundamental particle dynamics to practical industrial implementation. Plath actively collaborates within the University of Twente research ecosystem, working closely with supervisors including Thomas Weinhart, Stefan Luding, and Anthony Thornton. His activities include multiple oral presentations at international conferences between 2022-2024, focusing on model order reduction, quadrature methods, and granulation process characterization. His research infrastructure includes five significant datasets published through 4TU.Centre for Research Data between 2021-2024, supporting reproducibility in twin-screw wet granulation modeling, quadrature method applications, and rapid prototyping of granulation equipment. These datasets demonstrate his commitment to open science and computational reproducibility in particle technology research.

Felix AmbellanView profile

Researcher

• Statistical Shape Analysis
• Medical Imaging
• Knee Osteoarthritis
• +5 more

Felix Ambellan is a researcher at the Zuse Institute Berlin (ZIB), working in the Visual and Data-Centric Computing department within the Mathematics of Complex Systems division. His research focuses on applying advanced mathematical and computational techniques to medical imaging problems, particularly in the context of knee osteoarthritis and neurological disorders. Dr. Ambellan completed his doctoral studies at Freie Universität Berlin, where he earned his PhD in 2022 with a thesis titled "Efficient Riemannian Statistical Shape Analysis with Applications in Disease Assessment," supervised by Christof Schütte and Christoph von Tycowicz. His primary research interests lie at the intersection of medical imaging, computational geometry, and machine learning. Ambellan specializes in statistical shape analysis using Riemannian geometry, developing novel approaches for disease assessment through anatomical shape variations. His work has significant applications in knee osteoarthritis diagnosis and Alzheimer's disease grading, where he applies graph neural networks and manifold-valued statistics to extract clinically relevant information from medical images. He has made substantial contributions to the field of statistical shape modeling, particularly through the development of the open-source Python library Morphomatics. Analysis of Ambellan's recent publications reveals a strong focus on advancing statistical shape modeling techniques within non-Euclidean spaces. His work bridges theoretical mathematics with practical medical applications, particularly in developing methods that can handle the complex geometry of anatomical structures. Many of his papers demonstrate how incorporating geometric awareness into machine learning models improves diagnostic accuracy for conditions like knee osteoarthritis. His research often involves large-scale medical image datasets, including thousands of knee MRI scans from the Osteoarthritis Initiative. Dr. Ambellan is actively involved in several research projects including "Manifold-Valued Graph Neural Networks," "Morphological Scoring of Disease States," and "Treating Osteoarthritis in Knee with Mimicked Interpositional Spacer," which reflect his commitment to translating theoretical advances into clinical applications. His work has been published in high-impact journals including Medical Image Analysis, Physics in Medicine and Biology, and BMC Medical Imaging, demonstrating both the theoretical rigor and practical relevance of his research.

Bruno ValletView profile

Researcher

• Geographic Information Science
• Computer Vision
• Remote Sensing
• +7 more

Bruno Vallet is a Senior Researcher at IGN (French National Institute of Geographic and Forest Information) within the LASTIG lab and leads the ACTE research team since 2019. His work focuses on geospatial data processing, including LiDAR and image registration, 3D urban modeling, and computer vision applications. He contributes to projects like AI4GEO and Time Machine , specializing in large-scale point cloud analysis and structured city reconstruction. Education : Habilitation (HDR) in Geographic Information Science (Univ Paris-Est, 2016), PhD in Computer Science (Institut National Polytechnique de Lorraine, 2008), and Master's in Computer Vision (Telecom ParisTech, 2005). His research integrates surface reconstruction , semantic labelings , and uncertainty propagation , with methodologies applied to autonomous navigation and urban change detection. Recent publications emphasize data fusion, visibility computation, and deep learning for 3D scene analysis. He co-supervises PhD students and leads teaching activities at ENSG (National School of Geographic Sciences), covering image processing and 3D data structures. Bruno Vallet also chairs the ISPRS Working Group II/4 on 3D Scene Reconstruction, demonstrating leadership in photogrammetry and remote sensing communities.

Julina OngkasuwanView profile

Professor

• Pediatric Otolaryngology
• Laryngology
• Voice Disorders
• +6 more

Julina Ongkasuwan, MD, is a Professor in the Department of Otolaryngology - Head & Neck Surgery at Baylor College of Medicine , Houston, Texas. She serves as Chief of Laryngology and directs clinical programs at Texas Children's Hospital and the Jamail Specialty Care Center. Education : MD, University of Maryland School of Medicine (2004); BS in Molecular Biophysics and Biochemistry, Yale University (2000); Residency and fellowships at Baylor College of Medicine (Pediatric Otolaryngology, 2009; Laryngology, 2010) and UCSF (Laryngology, 2011). Her research focuses on pediatric laryngeal disorders , including voice disorders , vocal fold movement impairment , and recurrent respiratory papillomatosis , with expertise in laryngeal ultrasound and airway protection mechanisms . Recent work examines subglottic stenosis recurrence modeling , tracheotomy dressing outcomes , and long-term effects of laryngeal reinnervation . Her publications span diagnostic innovations in pediatric voice and swallowing, surgical outcome predictors , and interdisciplinary approaches to aerodigestive disorders. She has received grants from PCORI, CDC, and Texas Children's Hospital for projects on JORRP, airway transitions, and laryngeal ultrasound applications. Dr. Ongkasuwan leads clinical programs at Texas Children's Hospital and collaborates with the Institute for Voice & Swallowing , contributing to pediatric otolaryngology advancements through her roles in clinical care, research, and education.

Maire BraniffView profile

Senior Lecturer

• Peace and Conflict Studies
• Truth Recovery
• Commemoration Studies
• +3 more

Maire Braniff is a Senior Lecturer in Politics at Ulster University's School of Applied Social and Policy Sciences, where she has taught since 2013 and served as Director of INCORE (International Conflict Research Institute) from 2016-2018. Based at Belfast campus (Room BC-07-131), she is affiliated with the Institute for Research in Social Sciences and holds an ORCID (0000-0003-2016-9877). Her research centers on peace, justice and truth recovery at the intersection of competing narratives, power dynamics, and victimhood across post-conflict societies. Specializing in comparative conflict studies , she examines: Conflict resolution mechanisms in the Balkans, Northern Ireland, South Caucasus and Southeast Asia Legacies of violent conflict and memorialization practices Memory politics and commemoration in divided societies Unionist and nationalist political evolution in Northern Ireland EU enlargement policy impacts on post-conflict regions Analysis of her recent publications reveals a consistent focus on memory politics and narrative contestation in post-agreement societies, with increasing emphasis on gender dimensions (e.g., WE CAN project) and tourism's peacebuilding potential. Her 2023-2024 work examines Alliance Party's transcendence of traditional unionism/nationalism binaries while analyzing how historical legacies shape contemporary political behavior. Key awards include: British Academy Rising Star Award for Engagement (2016/2017) Paddy Power Political Book Award (2015) for 'Inside the Democratic Unionist Party' Political Studies Brian Farrell Book Prize (2015) Sir Bernard Crick PSA Teaching Prize (2012) As principal investigator for major projects including Women's ReconciliNation Project (2025-2029) and WE CAN (2023-2024), she secures significant research funding while serving on European Commission review panels and as Academic Friend of the European Peacebuilding Liaison Office. Her current work emphasizes women's empowerment in post-conflict reconstruction through capacity-building initiatives across contested spaces.