جست‌وجوی استاد و پژوهشگر

Carlos Andujar Granمشاهده پروفایل

دانشیار

• Computer Graphics
• 3D Modeling and Animation
• Virtual and Augmented Reality
• +۵ مورد دیگر

Carlos Andújar Gran is an Associate Professor in the Computer Science Department at the Polytechnic University of Catalonia . He is a key member of the ViRVIG Research Center for Visualization, Virtual Reality, and Graphics Interaction, as well as the Eurographics Association . His work bridges technical innovation with cultural and educational applications. Primary affiliation: Universitat Politècnica de Catalunya Research center: ViRVIG - Research Center for Visualization, Virtual Reality and Graphics Interaction Professional network: Eurographics Association His research interests focus on advanced topics in computer graphics and virtual reality : 3D Modeling - Digital reconstruction of complex geometries Animation Systems - Real-time motion capture and pose estimation Cultural Heritage - Digital restoration techniques for medieval monuments Human-Computer Interaction - 3D user interface design Sports Analytics - Player position tracking through computer vision Medical Education - VR nursing training platforms His technical publications demonstrate a consistent focus on solving practical problems through innovative algorithms: Developed DragPoser for motion reconstruction with sparse sensors Created PADELVIC dataset for sports analytics Advanced automated color restitution methods for mural paintings Improved normal estimation in point cloud processing Optimized terrain super-resolution techniques using convolutional networks Designed interactive cultural heritage systems for museum exhibitions

• Computer Vision
• Machine Learning
• Medical Image Analysis
• +۴ مورد دیگر

Fredrik Kahl is a Professor at Chalmers University of Technology, leading the Computer Vision Group under the Department of Signal Processing and Medical Technology. His research spans Computer Vision , Machine Learning , and Medical Image Analysis , with a focus on geometric deep learning and 3D reconstruction. University: Chalmers University of Technology Department: Signal Processing and Medical Technology Email: fredrik.kahl@chalmers.se His work addresses rotation equivariance , out-of-distribution detection , and privacy-preserving representations . Recent publications explore Gaussian splatting for 3D edge mapping, semi-supervised learning frameworks, and symmetry encoding in ReLU networks. Projects include collaborations with institutions like Wallenberg AI, Autonomous Systems and Software Program and grants from VINNOVA and Vetenskapsrådet (VR) .

• Virtual Humans
• Intelligent Tutoring Systems
• Immersive Learning Environments
• +۷ مورد دیگر

Randall W. Hill, Jr. serves as Vice Dean of the Viterbi School of Engineering, Executive Director of the USC Institute for Creative Technologies (ICT), and holds the Omar B. Milligan Professorship in Computer Science at the University of Southern California. Education: Bachelor of Science, United States Military Academy at West Point M.S. in Computer Science, University of Southern California Ph.D. in Computer Science, University of Southern California Hill pioneers the integration of storytelling and technology to create immersive learning environments through virtual humans, mixed reality, and social simulations. His research focuses on applying these technologies to leadership training, cultural awareness, negotiation, and mental health assessment, fundamentally transforming educational methodologies by blending dramatic narratives with advanced computing. His publication record includes over 50 technical works, with "Toward Virtual Humans" (2006) representing a cornerstone contribution to virtual human research in AI Magazine. This body of work demonstrates consistent innovation in human-computer interaction and simulation-based learning across military and civilian applications. Awards and honors: Omar B. Milligan Professorship in Computer Science (Games and Interactive Media) Hill leads ICT's diverse team of scientists, artists, and educators in developing cutting-edge training solutions. His oversight of virtual human technologies and mixed reality systems has established new paradigms for experiential learning, directly impacting mental health treatment protocols and cross-cultural communication training through rigorously evaluated simulation platforms.

Kees Vuikمشاهده پروفایل

استاد

• Numerical Analysis
• Scientific Computing
• Computational Fluid Dynamics
• +۴ مورد دیگر

Kees Vuik is a Professor at the Delft Institute of Applied Mathematics within the Faculty of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. His research focuses on developing advanced numerical methods for complex engineering and scientific simulations across multiple domains including energy systems, environmental engineering, and computational physics. His research interests center on Numerical Analysis and Scientific Computing, with specialized expertise in Computational Fluid Dynamics, Reservoir Simulation, and Wave Propagation. He develops innovative preconditioning techniques for large-scale linear systems and creates high-fidelity models for subsurface flow, flood prediction, and energy conversion systems. Recent work integrates graph-based frameworks for electrolyzer modeling and advanced triangulation methods for point cloud processing. Analysis of his 2025 publications reveals a strong interdisciplinary focus on sustainable energy solutions (hydrogen/CO2 storage), environmental resilience (flood modeling), and computational efficiency (parallel preconditioning). His work consistently bridges theoretical numerical methods with practical engineering applications, particularly in energy transition technologies and climate-related simulations. Professor Vuik's scientific contributions have earned significant recognition: Officer of the Order of Orange-Nassau (2021) Professor of Excellence award (Leermeesterprijs) from TU Delft (2022) He has supervised 49 students across bachelor, master, and PhD levels, with notable successes including undergraduate research published in leading medical journals. His collaborative network spans 21 documented activities including industrial partnerships with Fujitsu Laboratories and leadership in academic societies, indicating substantial research funding and mentorship impact. As chair of the Bataafsch Genootschap der Proefondervindelijke Wijsbegeerte (2023-2024) and active member of the Delft Institute of Applied Mathematics, he contributes to both foundational mathematical research and real-world applications through international collaborations and industrial knowledge exchange.

Benjamin Watsonمشاهده پروفایل

دانشیار

• Computer Graphics
• Human-Computer Interaction
• Visual Perception
• +۶ مورد دیگر

Benjamin Watson serves as an Associate Professor in the Department of Computer Science at North Carolina State University, leading the Visual Experience Lab which investigates how humans derive meaning from visual information through computer graphics, perception, design, and interaction. His work spans applications in entertainment, security, finance, education, and medicine, with leadership roles in major conferences including Graphics Interface, IEEE Virtual Reality, and ACM I3D. He holds senior membership in IEEE and active membership in ACM. Watson's educational background includes: Ph.D. from Georgia Tech (1997) M.S. from Georgia Tech (1997) B.S. from UC Irvine (1987) His research centers on visual perception and interaction, with core expertise in computer graphics, human-computer interaction, and user experience design. Key investigations include perceptual asymmetries in rendering, display technologies for immersive environments, and human performance optimization in high-stakes contexts like esports. The Visual Experience Lab bridges theoretical computer science with practical applications in healthcare, finance, and entertainment through interdisciplinary collaboration. Analysis of Watson's 2019-2025 publications reveals consistent focus on display technology limitations and human performance metrics. His work demonstrates increasing specialization in esports HCI (7 of 10 recent papers), particularly examining display size, rendering techniques, and frame rate impacts on aiming accuracy. Concurrently, he maintains strong contributions to light field displays and multi-view rendering, developing novel point-based methods that exploit perceptual redundancies for real-time performance. Scientific recognition includes: NVIDIA Professor Partnership Award (2007) National Science Foundation Faculty Early Career Award (2006) Watson actively mentors graduate researchers including Ph.D. graduate Adam Marrs (now at NVIDIA) and Holle Christensen, whose work on experience analytics received workshop acceptance at ACM DIS. His $772k+ in active and recent funding spans NSF grants for adaptive rendering systems ($497k), human-machine collaboration projects with the Laboratory for Analytic Sciences ($180k), and Caterpillar-sponsored AR applications ($20k), demonstrating strong industry-academia translation. The Visual Experience Lab operates as a cross-disciplinary hub connecting computer science, design, and cognitive psychology researchers. Current projects focus on real-time GPU acceleration for multi-view rendering, esports performance optimization, and AR applications for industrial equipment, with active partnerships including Caterpillar, NVIDIA, and the Laboratory for Analytic Sciences.

• image analysis
• pattern recognition
• machine learning
• +۵ مورد دیگر

Anders Heyden is a Professor at the Centre for Mathematical Sciences, Lund University, serving as deputy dean at the Faculty of Engineering with responsibility for research and research education. He is affiliated with multiple research initiatives including the Mathematical Imaging Group, eSSENCE: The e-Science Collaboration, and ELLIIT: the Linköping-Lund initiative on IT and mobile communication. His profile areas include Engineering Health, AI and Digitalization, Natural and Artificial Cognition, and Proactive Ageing at Lund University. Heyden received his M.Sc. in Engineering Physics in 1989 and Ph.D. in Mathematics in 1995 from Lund University. His doctoral thesis, "Geometry and algebra of multiple projective transformations," focused on computer vision. He has progressed through academic ranks from post-doctoral research fellow to associate professor and is now a full professor. His research spans image analysis, pattern recognition, machine learning, computer vision for autonomous systems, and medical image analysis. His work contributes to multiple UN Sustainable Development Goals, particularly in medical imaging, geometry, and computer graphics. His research fingerprint shows strong activity in images engineering (100%), mathematics (43%), algorithms (36%), calibration (29%), models (24%), projective geometry (18%), experiments (18%), and homography (18%). IAPR fellow Sparbanken Skåne Innovation Award 2023 Senior member of IEEE Member of the Royal Swedish Physiographic Society Former president of the Swedish Society for Automated Image Analysis (2010-2014) Former member of the International Association for Pattern Recognition governing board (2010-2018) Heyden has co-founded three startup companies: Ludesi, Mometric, and 23°N, with the latter winning the Sparbanken Skåne Innovation Award 2023. His supervised work includes 28 students and researchers across various projects. Current projects include EDAP (Early diagnostics and prognostics of Alzheimer's disease), machine learning-based image analysis of antibody function, Semantic Structure from Motion, DOGS (Digital Pathology for Optimized Gleason Score), and ELLIIT LU P07: Deep Vision: Multiple Object Tracking.

• Spatial statistics
• Computational statistics
• Statistical genetics
• +۵ مورد دیگر

Charles Geyer is a Professor at the School of Statistics, University of Minnesota . His work spans computational statistics, spatial statistics, and statistical genetics, with applications to endangered species conservation and stochastic process modeling. Research Interests: Spatial statistics, Markov chain Monte Carlo methods, likelihood inference in exponential families, constrained optimization, and statistical software development. Software Contributions: Developed R packages mcmc , aster , rcdd , and trust for Monte Carlo methods, life history analysis, computational geometry, and optimization. Teaching: Instructs graduate and undergraduate courses including Stat 5101, Stat 5102, Stat 5421, Stat 5601, and advanced seminars on computational statistics. Publications: Notable works include research on maximum likelihood estimation in exponential families, fuzzy P-values, geometric ergodicity in MCMC, and computational methods for conservation genetics.

Benedikt Schwabمشاهده پروفایل

پژوهشگر

• Geoinformatics
• 3D City Modeling
• Automated Driving Systems
• +۵ مورد دیگر

Benedikt Schwab is a Researcher at the Chair of Geoinformatics at the Technical University of Munich (TUM), specializing in semantic road space modeling and geospatial technologies for automated driving systems. His work focuses on the intersection of 3D city modeling, sensor data analysis, and autonomous vehicle testing. His primary research interests include semantic road space modeling, HD cards development, spatial-semantic analysis of sensor data, simulation of LiDAR, RADAR and camera sensors, and submicroscopic driving simulation. He has developed multiple Rust-based libraries for geospatial data processing including ecitygml, epoint, and evoxel, demonstrating strong technical expertise in both geospatial concepts and software development. His recent publications reveal a strong focus on 3D city modeling applications for autonomous driving, with particular emphasis on OpenDRIVE to CityGML conversion (r:trån), road space validation, and sensor simulation. His work frequently appears in ISPRS conferences and journals, showing consistent contribution to the geospatial research community. As an educator, Schwab teaches Advanced GIS for Environmental Engineering, Applied Geoinformatics 2, and Geo Sensor Networks courses at TUM, demonstrating his commitment to training the next generation of geospatial professionals. He has led multiple research projects including SAVeNoW (2021-2023) focused on functional and traffic safety for automated and connected mobility, and has contributed to the development of open data resources such as LOD3 Road Space Models and TUM2TWIN.

Md Alimoor Rezaمشاهده پروفایل

استادیار

• Computer Vision
• Robotics
• Machine Learning
• +۴ مورد دیگر

Md Alimoor Reza serves as an Assistant Professor of Computer Science in the Department of Mathematics and Computer Sciences at Drake University's College of Arts & Sciences. He leads the Computer Vision and Robotics Lab and teaches courses including Introduction to Computer Science, Computer Vision, Machine Learning, and Robotics. His academic journey culminated with a Ph.D. from George Mason University under Professor Jana Kosecka. Ph.D. in Computer Science, George Mason University (Advisor: Jana Kosecka) Professor Reza specializes in computer vision, robotics, and machine learning with applications spanning cognitive science, biology, medical diagnosis, and engineering. His research bridges theoretical advances with practical implementations, particularly in 3D reconstruction, human-robot interaction, and assistive technologies. He has developed novel approaches for multi-view stereopsis, point cloud processing, and semi-automatic annotation tools that have advanced computer vision applications. His publication portfolio reveals a strong focus on solving core computer vision challenges while expanding into interdisciplinary applications. Recent work shows increasing emphasis on accessibility technologies, educational AI applications, and real-world deployment of vision systems. The trajectory demonstrates evolution from foundational geometric computer vision toward more applied, human-centered systems with societal impact. As an educator, Professor Reza has mentored numerous undergraduate and graduate students through research projects, many of whom have won awards at regional computing conferences. His teaching spans introductory programming to advanced machine learning and computer vision topics, with a documented course schedule showing consistent offerings of CS 167 (Machine Learning) and CS 195 (Computer Vision). He directs the Computer Vision and Robotics Research Group at Drake, which maintains active collaborations across disciplines and has produced significant student research output. The lab environment emphasizes both theoretical understanding and practical implementation skills, preparing students for advanced research and industry positions in computer vision fields.

Amal Dev Parakkatمشاهده پروفایل

استادیار

• Sketch-based Interfaces
• Geometry Processing
• Digital Content Creation
• +۳ مورد دیگر

Amal Dev Parakkat is a tenured Assistant Professor at Telecom Paris (Institut Polytechnique de Paris) since September 2021, affiliated with the Computer Graphics Group and the IMAGES research team. His work focuses on sketch-based interfaces and geometry processing , particularly for digital content creation tasks like 3D shape inference and mesh generation. PhD in Computer Science from IIT Madras (2019), under Ramanathan Muthuganapathy Postdoctoral experience at Ecole Polytechnique (2019) and TU Delft (2020-2021) Research highlights include Surface Reconstruction , Interactive Modeling , and Geometric Algorithms . He leads the ANR SketchMAD project (2024-2028) and serves as International Mobility Chair at Telecom Paris. His publications span top conferences like SIGGRAPH Asia , Eurographics , and ACM CHI . Scientific Awards: SMI Young Investigator Award (2024) BEST PAPER AWARD (SIGGRAPH Asia Technical Communication 2023) Administrative roles include Head of Interaction, Graphics & Design at Telecom Paris. He mentors PhD students Tara Butler , Anandhu Sureshkumar , and Leiheng Qin , and collaborates with international researchers.

• Human-Computer Interaction
• Eye Movement and Gaze Interaction
• Augmented and Virtual Reality (AR/VR)
• +۶ مورد دیگر

Professor Hans Gellersen is currently affiliated with Lancaster University in the School of Computing and Communications . His academic rank is Professor in Human-Computer Interaction (HCI), with a focus on eye movement and gaze interaction techniques. Main research areas: HCI , eye movement tracking , AR/VR interaction , ubiquitous computing , and cross-device interaction Current projects include H2020 GEMINI (2021-2026) investigating gaze and eye movement foundations in interaction Recent work explores 3D interaction, eye-head coordination, and implicit calibration techniques in extended reality environments His research team's work can be explored at http://gemini-erc.org . Notable scientific achievement includes receiving an ERC Advanced Grant for gaze and gestural interaction research. PhD students: Baosheng James Hou, Hock Siang Lee, Guanlin Li, Franziska Prummer, Haopeng Wang, Thomas Wells Active research groups: SCC (Interactive Systems)

Yusuf Sahillioğluمشاهده پروفایل

استاد

• Computer Graphics
• 3D Shape Processing
• Geometric Algorithms
• +۳ مورد دیگر

Professor Yusuf Sahillioğlu is a faculty member in the Computer Engineering Department at Middle East Technical University (METU), Faculty of Engineering, specializing in computer graphics and geometric processing. His academic journey includes a BS from Bilkent University, MS from University of Florida, and PhD from Koç University, followed by postdoctoral work at Koç University and University of Pennsylvania. His research focuses on 3D shape correspondence , deformation algorithms , deep learning for 3D data , and geometry processing . Key contributions include novel methods for isometric shape matching, topologically-stable correspondence, and real-time secondary animation. His recent work integrates deep learning with traditional geometric approaches for mesh segmentation, point cloud classification, and 3D scene extrapolation. His 15 most recent publications (2021-2025) reveal a strong trend toward real-time geometric algorithms (e.g., spring-decomposed skinning), kernel computation for mesh processing, and deep generative models for 2D-to-3D conversion. The research consistently bridges theoretical geometry with practical applications in animation, reconstruction, and medical imaging. Award highlights include: METU Academic Performance Award (2018) METU Young Scientist Awards (2015-2017) TÜBİTAK 1001 Grants (2016, 2020, 2024) for 3D content analysis, shape matching, and reconstruction TÜBİTAK 3501 Career Grant (2015) for virtual medicine applications He teaches core courses including Data Structures, Operating Systems, Computer Graphics, and Advanced Digital Geometry Processing. His lab focuses on geometric algorithms with applications in medical imaging, animation, and 3D printing, maintaining strong industry and international collaborations through projects like TÜBİTAK-funded initiatives.

• Virtual Humans
• Mixed Reality
• Intelligent Tutoring Systems
• +۷ مورد دیگر

Randall Hill is a Research Professor of Computer Science at the University of Southern California's Viterbi School of Engineering and serves as Vice Dean and Executive Director of the USC Institute for Creative Technologies (ICT). He leads interdisciplinary teams in developing immersive learning environments using virtual humans, mixed reality, and educational games to train in leadership, cultural awareness, and mental health treatment. Education: B.S. from West Point, M.S. and Ph.D. in Computer Science from USC Research Focus: Virtual humans, intelligent tutoring systems, and simulation technologies for behavioral training Leadership: Executive Director of ICT since 2006, Vice Dean of Viterbi School of Engineering Hill's work integrates storytelling and AI to create dynamic educational tools. His research spans virtual reality applications for PTSD treatment, automated leadership training, and perceptual attention modeling in synthetic agents. He has authored over 50 publications and pioneered systems like BiLAT and UrbanSim for real-world skill development. Key trends in his publications include advancing 3D point cloud analysis (2021), ethical considerations in immersive tech (2019), and foundational work in virtual human behavior modeling (2012-2006). His research consistently bridges AI, psychology, and military training applications. Scientific Awards: Omar B. Milligan Professor in Computer Science, AAAI Membership Hill oversees a multidisciplinary team at ICT combining computer scientists, storytellers, artists, and educators. His career began at NASA's JPL, transitioned through USC's Information Sciences Institute (1995), and ICT (2000), focusing on scalable simulation technologies.

Alexander Ihlerمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Graphical Models
• +۵ مورد دیگر

Alexander Ihler is a Professor in the Department of Information and Computer Science at the University of California, Irvine. His work bridges artificial intelligence and machine learning with a focus on statistical methods for data analysis and approximate inference in graphical models. Applications span sensor networks, computer vision, and computational biology. His research emphasizes graphical models for structuring probability distributions and enabling efficient reasoning. Key contributions include analyzing convergence in belief propagation, extending variational techniques to continuous systems, and developing bounds for marginal MAP and decision-making problems. He also explores adaptive inference algorithms for dynamic problems, such as tracking changes in sensor data or biological systems. Recent publications highlight advancements in AI for protein design and astrophysics UAV mobility modeling deep learning integration with classical algorithms dynamic mode decomposition for forecasting Scientific accolades include the NSF CAREER award and first-place UAI 2014 Approximate Inference Challenge in five categories. He has advised numerous PhD and MS students, with graduates in fields like robotics, computational biology, and statistical learning. His group collaborates with institutions like Microsoft Research, DARPA, and the NIH, focusing on scalable methods for real-world systems. Additional personal details include a PhD from MIT under Alan Willsky and a prior role as co-program chair of the 2016 Uncertainty in Artificial Intelligence (UAI) conference.

Steve Petruzzaمشاهده پروفایل

استادیار

• High-Performance Computing
• Data Analytics
• Scientific Visualization
• +۳ مورد دیگر

Dr. Steve Petruzza is an Assistant Professor in the Department of Computer Science at Utah State University (USU) and a Staff Scientist at the Scientific Computing and Imaging (SCI) Institute's Center for Extreme Data Management, Analysis, and Visualization (CEDMAV). He holds a PhD in Computer Science from the University of Rome 'Tor Vergata' and has conducted research at the University of Utah and KAUST. His work focuses on High-Performance Computing (HPC), scientific visualization, and scalable data analytics with applications in edge computing, cloud optimization, and large-scale scientific data management. Education: PhD in Computer Science, University of Rome Tor Vergata (2018) M.S. in Computer Engineering, University of Rome Tor Vergata (2013) B.E. in Computer Engineering, University of Rome Tor Vergata (2010) Research Interests: Dr. Petruzza’s research spans high-performance computing systems, in situ analytics, parallel I/O optimization, and scalable visualization frameworks. His work addresses challenges in managing and analyzing massive scientific datasets across domains like materials science, climate modeling, and geospatial imaging. Grants & Projects: National Science Foundation (NSF) grant for scalable I/O runtimes and adaptive data layouts. USGS collaboration for real-time multispectral aerial mapping. NSF ASPIRE ERC project for equitable EV charging infrastructure visualization. Leadership in the ALPINE project for exascale visualization infrastructure (DOE ECP). Labs & Teams: He leads the Computing Elevated Lab, which develops cutting-edge techniques for large-scale data analysis and visualization across edge devices to supercomputers. Collaborations include the OpenVisus framework for scientific data management and the ALPINE Ascent in situ visualization infrastructure.