جست‌وجوی استاد و پژوهشگر

Sebastian von Mammenمشاهده پروفایل

استاد

• Games Engineering
• Virtual Reality
• Interactive Simulations
• +۳ مورد دیگر

Prof. Dr. Sebastian von Mammen is a tenured professor at the University of Würzburg's Institute for Computer Science, where he heads the Games Engineering research group and contributes to the Chair for Human-Computer Interaction. His group leads the Games Engineering academic program. Previously, he completed his habilitation (2012-2016) at the University of Augsburg's Chair of Organic Computing and was a postdoctoral fellow at the University of Calgary. His research spans: Real-Time Interactive Systems : Visual programming, immersion techniques, software engineering Interactive Simulations : Serious games for healthcare/logistics/construction Artificial Life : Self-organisation, adaptive systems, evolutionary computation Artificial Intelligence : Agent-based modeling, procedural content generation Recent publications (2023-2025) demonstrate strong focus on: Virtual reality applications in education (femtoPro optics simulator, BrainBuilder neuroanatomy) Healthcare technology platforms (VIA-VR for medical serious games) Game mechanics analysis (Match-3, Jump'n'Run flow) AI-driven emotion recognition and interactive systems Computational modeling of biological systems He leads the Games Engineering research group and previously participated in the Evolutionary and Swarm Design group (Calgary) and LINDSAY project. His lab develops VR simulations for scientific training and serious games applications.

• Computer Vision
• Medical Imaging
• Deep Learning
• +۵ مورد دیگر

Mert Kiray is a researcher at the Department of Informatics, Technical University of Munich (TUM), affiliated with the Chair of Computer Aided Medical Procedures under Prof. Nassir Navab. He is based at Campus Garching and actively contributes to research in computer vision, medical imaging, and surgical robotics. His research focuses on Computer Vision , Deep Learning , and Medical Image Analysis , with applications in Augmented Reality , Generative Models , and Robotics . Current projects include surgical data science, 3D reconstruction, and vision-language integration. Recent publications highlight his work on Multimodal Human Action Recognition (2025) and Text-to-3D Gaussian Animation (2025), reflecting expertise in combining 3D Computer Vision with Medical Applications .

Nikolas Braschمشاهده پروفایل

پژوهشگر

• 3D Computer Vision
• SLAM
• 3D Scene Understanding
• +۱ مورد دیگر

Nikolas Brasch is a PhD candidate and researcher at the Chair of Computer Aided Medical Procedures (CAMP) within the Department of Informatics at the Technical University of Munich. He is affiliated with the research group led by Prof. Nassir Navab and maintains his office at Boltzmannstr. 3, 85748 Garching b. München, Room 5613.03.039. Brasch's research centers on 3D computer vision with emphasis on SLAM (Simultaneous Localization and Mapping) and 3D scene understanding. His work bridges computer vision with medical applications, particularly in medical augmented reality and surgical robotics. He has developed innovative approaches for improving depth estimation in challenging environments and creating robust SLAM systems that maintain accuracy in dynamic settings. His publication record shows a consistent trajectory in advancing 3D vision techniques with practical applications in medical contexts. His research demonstrates particular strength in sensor fusion techniques and semantic understanding of 3D environments. Wild ToFu: Improving Range and Quality of Indirect Time-of-Flight Depth with RGB Fusion in Challenging Environments (2021) Structure-SLAM: Low-Drift Monocular SLAM in Indoor Environments (2020) Semantic Monocular SLAM for Highly Dynamic Environments (2018) As an educator, Brasch serves as a teaching assistant for the Praktikum on 3D Computer Vision course at TUM. He mentors students on various thesis projects related to 3D vision, human pose estimation, and semantic reconstructions, typically collaborating with Prof. Federico Tombari. His teaching activities reflect his research expertise and commitment to training the next generation of computer vision researchers.

• Computer Vision
• Facial Expression Recognition
• Deep Learning
• +۴ مورد دیگر

Kévin Bailly is a Lecturer at Sorbonne University, affiliated with the Institute of Intelligent Systems and Robotics (ISIR) and part of the Machine Learning and Artificial Intelligence (MLIA) team. His research focuses on computer vision, deep learning, and their applications in facial expression recognition, neural network optimization, and medical imaging. Dr. Bailly's research interests span multiple areas including: Computer Vision and Image Analysis Deep Learning and Neural Network Optimization Facial Expression and Action Unit Recognition Model Compression and Quantization Techniques Medical Applications of Artificial Intelligence His recent publications demonstrate a strong focus on neural network optimization, with particular emphasis on quantization, pruning, and compression techniques that maintain model performance while reducing computational requirements. His work spans both theoretical advancements in deep learning and practical applications in healthcare, human-computer interaction, and affective computing. He has developed novel approaches like PowerQuant for non-uniform quantization, RULe for real-time face alignment in degraded conditions, and RED++ for data-free pruning of deep neural networks. Dr. Bailly has published extensively in top-tier venues including ICLR, NeurIPS, IEEE TPAMI, and IEEE TAC, with a consistent output of high-impact research from 2022-2024. His work bridges theoretical computer vision with practical applications, particularly in medical diagnostics and human-computer interaction systems. He actively collaborates with researchers across multiple institutions, including Arnaud Dapogny, Edouard Yvinec, and Matthieu Cord, and has contributed to interdisciplinary projects that apply AI techniques to medical domains such as fracture classification and obstetrics.

Nick Cheneyمشاهده پروفایل

دانشیار

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

Nick Cheney is an Associate Professor in the Department of Computer Science at the University of Vermont, leading the UVM Neurobotics Lab. He also serves as Graduate Program Director and is affiliated with the Vermont Complex Systems Center, an interdisciplinary hub for data-rich complex systems research. PhD in Computational Biology and Biological Statistics from Cornell University Advised by Hod Lipson and Steve Strogatz His research focuses on bio-inspired machine learning algorithms, particularly in evolutionary computation, deep learning, and reinforcement learning. Key applications span robotics, healthcare diagnostics, and environmental science. The lab's interdisciplinary work has been recognized with prestigious awards including the NSF CAREER Award and SIGEVO Impact Award . Recent publications highlight advancements in morphological computation, continual learning, and cross-domain applications of machine learning. His team develops algorithms for soft robots, medical diagnostics using wearable sensors, and sustainable agriculture systems, often publishing in venues like the Nature Scientific Reports , GECCO , and Soft Robotics . Scientific Awards : NSF CAREER Award SIGEVO Impact Award The lab actively mentors graduate students in Complex Systems and Data Science, with alumni securing positions at institutions like Harvard, UC Berkeley, and Medidata. Collaborative grants with biomedical and environmental researchers demonstrate the lab's commitment to societal impact through machine learning applications.

Giulio Dagninoمشاهده پروفایل

دانشیار

• Medical Robotics
• Endovascular Robotics
• Computer Vision
• +۵ مورد دیگر

Giulio Dagnino is Associate Professor of Robotics and Mechatronics at the University of Twente and concurrently holds an appointment at the Digital Society Institute. His research integrates medical robotics, real-time perception and haptics to create MR-compatible platforms for endovascular surgery, earning an h-index of 17 and 971+ citations. Education & Career: PhD (details not specified in source) leading to faculty appointment at University of Twente. Promoted to Associate Professor with cross-appointments in Robotics & Mechatronics and Digital Society Institute. Research Interests: Prof. Dagnino’s core interest is medical robotic systems that can operate safely inside an MRI scanner. His work spans haptic guidance, real-time computer vision, soft robotic actuation, synthetic data generation and surgical simulation. By combining ferrofluid actuation, electromagnetic tracking and deep-learning-based scene understanding, he aims to reduce ionizing radiation exposure, enhance navigation accuracy and shorten procedure times for minimally invasive endovascular interventions. Publications Trend: Across 44 outputs (2010-2025) the portfolio reveals a clear evolution from early vision-based microsurgery and fracture-robot systems (2010-2016) toward holistic endovascular platforms integrating MR guidance, haptics and autonomy. Recent 2024-25 papers cluster around (i) synthetic data & scene understanding for surgical AI, (ii) MR-safe robot design and tracking, and (iii) translational studies bringing CathBot and related platforms closer to clinical use. Scientific Awards: Best Design Award – Hamlyn Symposium 2019 (with team) Best Innovation Award – ICRA 2018 Best Paper Award – CURAC 2019 IEEE ICRA Best Paper Award in Medical Robotics – 2016 Grants & Projects: Although explicit grant numbers are not listed, the continuous outputs, patents, multi-institutional collaborations (UK, Germany, Estonia, Canada) and press releases imply sustained funding from EU, Dutch and UK research councils as well as industrial partnerships. Labs & Teams: He leads activities within the Robotics and Mechatronics group at University of Twente, collaborates closely with the Digital Society Institute, and maintains international partnerships visible in co-authored papers with Imperial College London, University of Leeds, and several European hospitals.

Alper Yilmazمشاهده پروفایل

استاد

• Deep Learning
• Reinforcement Learning
• Computer Vision
• +۱۱ مورد دیگر

Alper Yilmaz is Professor with appointments in Civil Environmental and Geodetic Engineering and Computer Science and Engineering (courtesy) Departments at The Ohio State University. He serves as Director of PCVLab and is currently interim president for the ISPRS Technical Commission II. Dr. Yilmaz has been inducted to the U.S. National Academy of Inventors in 2020 and is a Fellow of the American Society for Photogrammetry and Remote Sensing (ASPRS) and senior member of IEEE. Dr. Yilmaz's research focuses on biomimetic navigation systems for unmanned systems, mining anomalies in multi-physics and multi-dimensional data for surveillance, and learning geospatial information for scene understanding. His expertise spans Deep Learning, Reinforcement Learning, Computer Vision, Photogrammetry, Data-centric Surveillance, Collaborative Swarm Navigation, and Indoor Positioning Systems. His recent work on biomimetic navigation systems has resulted in UbiHere Inc., an Ohio State University spin-off founded in 2018. Dr. Yilmaz has served as Editor-In-Chief for the Photogrammetric Engineering and Remote Sensing Journal (PE&RS) between 2016-2024, during which the journal's impact factor increased to its highest since 1934. He previously served as Associate Editor for Computer Vision and Image Understanding Journal (2014-2016) and Machine Vision and Applications Journal (2006-2011). Outstanding Service Award (2022, ASPRS) Innovator of Year (2020, OSU) Presidential citation (2019, ASPRS) Masao Horiba Award honorable mention (2016, Japan) Lumley Interdisciplinary Research Award (2015, OSU) Lumley Research Award (2012, OSU) Top 1% most cited researcher in Artificial Intelligence & Image Processing and Geological & Geomatics Engineering (2024) Dr. Yilmaz has advised 29 Ph.D. and 15 M.Sc. students to completion on topics ranging from photogrammetry, machine learning, and computer vision. His research has received over $13M in extramural funding from NASA, NSF, DOD, DOE, NIH, and industry partners including Ford Motor Company, Trimble Inc., and UbiHere Inc. PCVLab, located in Bolz Hall Suite 233, features a sensor calibration room, state-of-the-art workstations with GPUs for deep learning studies, and small robotic systems including ground and aerial units.

• Orthodontic Biomechanics
• 3D Craniofacial Imaging
• Ultrasound Imaging
• +۳ مورد دیگر

Paul Major is Professor and Chair of the School of Dentistry, Senior Associate Dean (Dental Affairs), and ACFD Project Lead at the University of Alberta's Faculty of Medicine & Dentistry. He leads the Orthodontic Biomechanics Research Group and co-founded the Inter-disciplinary Airway Research Clinic (I-ARC), driving innovation across dental academia and clinical practice. His educational background includes a Doctorate of Dental Surgery (DDS) from the University of Alberta (1980) followed by MSc and Orthodontic Specialty training at the same institution (1988). He joined the academic staff in 1989 and served as Director of the TMD/Orofacial Pain Program (1991-2001) and Orthodontic Graduate Program (2001-2010). Dr. Major's research centers on Orthodontic Biomechanics , 3D Craniofacial Imaging , and Ultrasound Imaging . His Orthodontic Biomechanics Research Group developed the OSIM system for 3D force measurement on dental appliances, while his imaging work pioneers reconstruction of craniofacial structures and periodontal ultrasound diagnostics. Through the I-ARC, he leads interdisciplinary studies on pediatric sleep-disordered breathing, examining craniofacial morphology and orthodontic interventions. Analysis of his 190+ publications reveals consistent innovation in biomechanical analysis of orthodontic appliances, machine learning for dental image processing, and hydrogel development for intraoral imaging. Recent work bridges dentistry with engineering through projects on dental aerosols, clear aligner mechanics, and airway measurement software. Dr. Major has supervised over 75 graduate students while maintaining clinical teaching duties despite administrative leadership roles. His research is supported by grants enabling the OSIM system development and interdisciplinary I-ARC projects. He directs the Orthodontic Biomechanics Research Group's experimental biomechanics work and the I-ARC's clinical research team, which integrates pediatric ENT, pulmonology, radiology, and biomedical engineering specialists to advance treatment of pediatric sleep apnea through craniofacial analysis and innovative imaging techniques.

Minjoon Seoمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Minjoon Seo is an Associate Professor at KAIST AI, Korea Advanced Institute of Science and Technology. He holds a BS in Electrical Engineering & Computer Science from UC Berkeley and previously worked as a software engineer at Oracle. His research focuses on natural language understanding, large-scale end-to-end question answering, and multimodal AI systems combining language and vision. Research Interests: His work spans Natural Language Processing, Machine Learning, Deep Learning, and Language-Vision integration. He develops neural network architectures for machine comprehension and multimodal understanding, with applications in question answering systems and diagram interpretation. Publications: His research demonstrates a consistent focus on multimodal AI systems, with recent works advancing neural approaches to machine comprehension and diagram understanding. Publications show strong emphasis on NLP-CV integration and practical applications in healthcare and education. Awards: Best Paper Nomination at UbiComp 2014 for BiliCam research Professional Activities: Maintains active open-source contributions through GitHub repositories related to question answering systems and NLP research. Co-founded Config Intelligence while maintaining academic position.

Michael A. Silverمشاهده پروفایل

دانشیار

• Cognitive Neuroscience
• Visual Perception
• Attention
• +۵ مورد دیگر

Michael A. Silver is an Associate Professor in the School of Optometry and Vision Science at UC Berkeley, with affiliations in the Helen Wills Neuroscience Institute and the Department of Psychology. His research focuses on cognitive neuroscience, particularly the neurophysiological and neurochemical substrates of visual perception, attention, and learning. He leads the Silver Lab, which investigates topics such as visual attention mechanisms, perceptual learning, cholinergic pharmacology, and the effects of psychedelics on brain function. Silver holds a Ph.D. from UCSF and has been recognized with grants from the NIH, NSF, and private foundations. Education: B.S. Biological Sciences and Chemistry (Carnegie Mellon, 1991); Ph.D. Neuroscience (UCSF, 1999). Awards include the Howard Hughes Medical Institute Predoctoral Fellowship and Hellman Family Faculty Fund. He mentors graduate students and postdoctoral researchers in vision science and neuroscience. Research interests span visual neuroscience, including the impact of psychedelics (e.g., psilocybin) on neural substrates of perception and cognition. His lab collaborates with institutions like Stanford University and UC Irvine to explore therapeutic applications of psychedelics and age-related auditory decline. Key projects include cholinergic modulation of attention and perceptual learning, and functional subdivisions of the lateral geniculate nucleus. Recent articles highlight studies on attentional modulation of visual crowding, effects of psychedelics on predictive coding, and GABA levels in amblyopia. Silver teaches courses on visual cognitive neuroscience and supervises over 20 graduate students and postdocs. His grants total over $5M, supporting work on nicotine therapy for auditory decline and psychedelic science.

Jagath Samarabanduمشاهده پروفایل

استاد

• Artificial Intelligence
• Explainable AI
• Machine Learning
• +۵ مورد دیگر

Jagath Samarabandu is a Professor in the Department of Electrical and Computer Engineering at Western University. He holds a Ph.D. and M.S. in Electrical Engineering from SUNY Buffalo, and a B.Sc. in Electronics and Telecommunication Engineering from the University of Moratuwa, Sri Lanka. His academic career spans since joining Western University in 2000, with prior post-doctoral experience at SUNY Buffalo and industry work at Life Imaging Systems Inc. Education: Ph.D. Electrical Engineering, SUNY Buffalo M.S. Electrical Engineering, SUNY Buffalo B.Sc (Eng) Electronics and Telecommunication, University of Moratuwa His research focuses on Artificial Intelligence, Machine Learning, Image Analysis, and Cyber Security , with applications in biomedical imaging, network intrusion detection, and civil infrastructure monitoring. He has supervised numerous graduate students working on topics ranging from chromosome analysis to smart grid security. Recent publications highlight his work in medical AI applications (auditory processing disorder diagnosis), industrial time-series analysis (using contrastive predictive coding), and network security frameworks (INSecS system development). He has contributed to 3D ultrasound segmentation, prostate motion compensation algorithms, and synthetic aperture radar systems. Key projects include NSERC-funded intelligent home monitoring systems for elderly care and low-cost synthetic aperture radar development for search-and-rescue applications.

Xiaohan Yuمشاهده پروفایل

مدرس

• Ultra-Fine-Grained Visual Categorization
• Smart Farming
• Automated Crop Cultivar Identification
• +۳ مورد دیگر

Dr. Xiaohan Yu is a Lecturer in Artificial Intelligence at Macquarie University's School of Computing, joining in December 2023. Previously, he completed his doctoral studies at Griffith University and served as a Research Fellow at the ARC Research Hub for Driving Farming Productivity. His research focuses on Ultra-Fine-Grained Visual Categorization (Ultra-FGVC), Smart Farming, and Automated Crop Cultivar Identification, with over 70 publications in top-tier venues like ICCV, CVPR, and IEEE Transactions. He holds editorial roles at Pattern Recognition and SN Computer Science , and received the APRS Early Career Award (2022) and ACM MM 2024 Outstanding Area Chair distinction. Education: Completed doctoral studies in Artificial Intelligence at Griffith University, Australia. Research Interests: Ultra-Fine-Grained Visual Categorization (Ultra-FGVC) Smart Farming and Agricultural Robotics Computer Vision Applications in Healthcare (e.g., trachoma detection) Deep Learning, Continual Learning, and Domain Adaptation Key Contributions: Pioneered Ultra-FGVC research, developed frameworks like Mix-ViT and CLE-ViT, and contributed to benchmarking multi-object tracking in farming. His work bridges pattern recognition with real-world applications in agriculture and healthcare. Scientific Awards: Australian Pattern Recognition Society (APRS) Early Career Researcher Award 2022 ACM Multimedia 2024 Outstanding Area Chair Award Advising & Grants: Actively involved in editorial roles (Area Chair for ACM MM, IJCNN) and grant-funded research through ARC hubs. His work is supported by collaborations in agriculture and AI-driven solutions for crop cultivar identification. Labs & Affiliations: Member of Macquarie's Smart Green Cities Research Centre and Frontier AI Research Centre , advancing interdisciplinary AI applications.

Baris Coskunuzerمشاهده پروفایل

استاد

• Geometric Topology
• Topological Data Analysis
• Machine Learning
• +۳ مورد دیگر

Baris Coskunuzer is a Professor in the Department of Mathematical Sciences at the University of Texas at Dallas (UT Dallas), part of the School of Natural Sciences and Mathematics. He holds a PhD from Princeton University (2004) and has held academic positions at institutions including Yale University, MIT, Boston College, and Koç University. His research focuses on Geometric Topology, Topological Data Analysis (TDA), and Machine Learning, with applications in medical imaging, drug discovery, and blockchain analysis. He has led multiple NSF-funded research grants and collaborates internationally. Education: PhD in Mathematics, Princeton University, 2004 M.S. in Mathematics, Caltech, 2001 B.S. in Mathematics, Bogazici University, 1999 Research Interests: Geometric Topology Topological Data Analysis Machine Learning Medical Imaging Blockchain Analysis Data Science Grants & Awards: NSF-DMS ATD Research Grant (2023–2026) NSF-DMS AMPS Research Grant (2022–2025) Young Scientist Award, Turkish Science Academy (2016) Fulbright Scholar Award (2014) Over 60 peer-reviewed publications in journals such as Communications on Pure and Applied Mathematics and NeurIPS Advising & Labs: Supervises a research group focused on Topological Machine Learning (TML) Collaborates on projects like Topo-ML for medical diagnostics and GraphPulse for temporal graph analysis

Aaqib Saeedمشاهده پروفایل

استادیار

• Human-Centric AI
• Federated Learning
• Self-Supervised Learning
• +۳ مورد دیگر

Aaqib Saeed is an Assistant Professor in the Department of Industrial Design at Eindhoven University of Technology. His research focuses on Human-Centric AI, Federated Learning, Self-Supervised Learning, and Audio Understanding, with applications in Personal Health. He holds a PhD (cum laude) from TU/e and an MSc (cum laude) from the University of Twente. Education: PhD in Computer Science (cum laude), TU/e (2021) MSc in Computer Science (cum laude), University of Twente (2018) Research Interests: Development of robust federated learning frameworks for decentralized data Self-supervised learning for audio and physiological signal analysis AI-driven solutions for healthcare monitoring Key Contributions: DeltaMask: Reducing communication overhead in federated fine-tuning FedNS: Mitigating noisy decentralized data in federated learning Labeling Chaos to Learning Harmony: Handling label noise in FL Professional Experience: Visiting Industrial Fellow, University of Cambridge (2023) Research Scientist, Philips Research (2019–2023) Research Internships: Google Research, TNO/EIT Digital Awards: UT Scholarship (MSc) Cum Laude awards for both PhD and MSc Labs/Teams: EAISI Health, EAISI Foundational, Computational Design Systems.

• Mechatronics
• Autonomous Systems
• Biomedical Engineering
• +۷ مورد دیگر

Dr. Milan Simic is a Senior Lecturer in the School of Engineering at RMIT University, serving as Program Manager for the Master of Engineering (Management) degree. He holds editorial roles for the Knowledge Engineering Systems and Intelligent Decision Technologies journals and is Associate Director of the Australia–India Research Centre for Automation Software Engineering. With a PhD in Electronic Engineering from the University of Niš and a Graduate Diploma in Education from RMIT, Dr. Simic has extensive industry and academic experience in Australia and internationally. His research focuses on mechatronics, autonomous systems, biomedical engineering, robotics, intelligent transportation systems, and green energy. Notable projects include AI-driven railway system strategies, gait analysis for biomedical applications, and smart traffic control systems. He actively supervises PhD and master’s students in areas like autonomous vehicles and energy recovery systems. Dr. Simic’s work bridges engineering innovation with societal impact, emphasizing sustainable transportation solutions and smart city technologies. His contributions span journal editing, international collaborations, and curriculum development in engineering management.