Point-based graphics Professors and Supervisors

Eric Laberمشاهده پروفایل

استاد

Duke UniversityUnited States

Eric Laber serves as James B. Duke Distinguished Professor of Statistical Science at Duke University, with cross-appointments as Professor of Biostatistics & Bioinformatics in the School of Medicine and Research Professor of Global Health. He co-directs the Duke Computing Initiative and maintains active roles in the Department of Statistical Science. His educational background includes: Ph.D. in Statistics, University of Michigan, Ann Arbor (2011) M.A. in Statistics, University of Michigan, Ann Arbor (2007) Laber's research pioneers statistical reinforcement learning for dynamic decision-making in complex health environments. He specializes in developing methodologies for Sequential Multiple Assignment Randomized Trials (SMARTs) to optimize personalized treatment sequences in precision medicine. His work bridges machine learning with clinical applications, focusing on cancer therapeutics, mental health interventions, and resource-constrained global health settings. Recent innovations address network interference in policy learning and risk-sensitive optimization for digital health platforms. Analysis of his 15 most recent publications reveals strong trends in adaptive clinical trial design, with increasing emphasis on mHealth applications (40% of recent work), PTSD management in cancer survivors (25%), and network-based reinforcement learning (20%). Key methodological threads include Thompson sampling variants (33%), functional data analysis for truncated outcomes (15%), and causal inference under interference (25%). His scientific recognition includes: James B. Duke Distinguished Professorship (premier faculty honor at Duke) Laber secures substantial grant funding for high-impact health research, currently leading 10 active projects totaling over $15M. Major awards include NIH/NCI funding for next-generation SMARTs in cancer therapeutics (2023-2028), NSF support for risk-sensitive statistical learning (2024-2027), and multiple NIH trials for digital mental health interventions. His collaborative approach spans oncology, psychiatry, and global health teams, with special focus on vulnerable populations including cancer survivors and adolescents. As Co-Director of the Duke Computing Initiative, he leads interdisciplinary teams developing scalable computational frameworks for precision medicine. Current efforts integrate reinforcement learning with electronic health records systems to create real-time clinical decision support tools, particularly for resource-limited settings in infectious disease management and chronic pain treatment.

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Didier Strickerمشاهده پروفایل

استاد

University of MannheimGermany

Prof. Dr. Didier Stricker is a leading academic in computer science, serving as Scientific Director at the German Research Center for Artificial Intelligence (DFKI) and Professor at the University of Kaiserslautern-Landau (RPTU). His career spans over two decades, including leadership roles at Fraunhofer IGD and founding the Augmented Vision research unit at DFKI/RPTU, which now includes ~30 researchers. Education: Electrical Engineering (Technical University of Grenoble, Karlsruhe) PhD: Computer Vision-based Calibration and Tracking Methods for Augmented Reality (2002, TU Darmstadt) His research focuses on virtual and augmented reality , computer vision , human-computer interaction , and on-body sensor networks . He leads major EU/national projects like LUMINOUS (Language-Augmented XR) and SHARESPACE (Ethical Hybrid Shared Spaces), with industrial partnerships including Sony, Google, and John Deere. Recent publications emphasize 3D reconstruction , neural network optimization , and XR systems . Key trends include event camera processing , scene flow estimation , and multimodal AI for industrial applications . He holds patents in AR tracking and has received the 2006 Innovation Prize from the German Society of Computer Science. Scientific Awards : Innovation Prize (2006) Best Paper/Demonstration Awards at ISMAR, EUSIPCO, CVPR, and ICRA As a reviewer for journals and conferences in VR/AR and computer vision, he contributes to shaping research standards. His lab ( AG Augmented Vision ) combines academic and industrial collaborations to advance cognitive interfaces and extended reality systems.

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Sang Il Parkمشاهده پروفایل

استاد

University of Hawaiʻi at MānoaUnited States

Sang Il Park is a Professor in the Department of Software at Sejong University, South Korea, where he has been a faculty member since 2007 following postdoctoral work at Carnegie Mellon University's Robotics Institute (2005-2007) and a brief research position at Japan's Digital Human Research Center. Ph.D. in Computer Science, KAIST (2004) M.S. in Computer Science, KAIST (1999) B.S. in Computer Science, Yonsei University (1997) Professor Park's research centers on Computer Graphics , with deep expertise in Character Animation , 3D Geometry Processing , and Image Processing . His research fingerprint shows dominant activity in Motion Capture (100%), Transition Graphs (72%), and Motion Synthesis (72%). His work has evolved from foundational research in human motion capture to incorporating deep learning techniques for image restoration, demonstrating both consistency in core interests and adaptation to emerging technologies. His publication pattern reveals a sustained research program with significant outputs in 2008, 2010, 2014, 2018, and continuing through 2023. The research spans from character animation and motion synthesis to practical applications like document processing and weathering simulation, showing remarkable breadth within his specialized domain. h-index: 12 Total citations: 933 Presented pioneering research at ACM SIGGRAPH Referenced in 4 patents and 2 Wikipedia pages Professor Park leads the Computer Graphics Lab at Sejong University, where he mentors graduate students and conducts research in animation, modeling, rendering, and computational photography. His patent portfolio includes innovations in facial expression restoration and motion synchronization, demonstrating the practical impact of his research beyond academia.

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Benedikt Wirthمشاهده پروفایل

استاد

University of MünsterGermany

Prof. Dr. Benedikt Wirth is a Professor of Mathematics at the University of Münster, Germany, affiliated with the Institute for Analysis and Numerics within the Department of Mathematics and Computer Science. He is an active researcher and educator specializing in optimization and calculus of variations, with significant contributions to mathematical imaging and shape analysis. His research interests include image processing, scientific computing, numerical analysis, optimization, shape spaces, geodesics in shape space, variational methods, elastic deformation, and optimal transport. Wirth has developed innovative mathematical frameworks for shape analysis, particularly focusing on Riemannian metrics for shape spaces and variational approaches to shape comparison and optimization. His recent publications (2023-2025) demonstrate continued leadership in mathematical optimization, with particular focus on PET reconstruction, dimension reduction techniques, manifold embeddings, and branched transport theory. His work bridges theoretical mathematics with practical applications in medical imaging and computer vision, showing particular strength in connecting geometric analysis with computational methods. CRC 1450 - A05: Targeting immune cell dynamics by longitudinal whole-body imaging and mathematical modelling CRC 1450 - A06: Improving intravital microscopy of inflammatory cell response by active motion compensation EXC 2044 - C1: Evolution and asymptotics EXC 2044 - C2: Multi-scale phenomena and macroscopic structures EXC 2044 - C3: Interacting particle systems and phase transitions EXC 2044 - C4: Geometry-based modelling, approximation, and reduction Prof. Wirth actively supervises numerous bachelor's and master's students, with over 40 theses completed under his guidance since 2015. His teaching portfolio includes courses on inverse problems, numerical methods for partial differential equations, shape spaces, optimization, and optimal transport. He has consistently maintained an active research program while contributing significantly to the education of the next generation of mathematicians.

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Pete Willemsenمشاهده پروفایل

استاد

University of Minnesota Twin CitiesUnited States

Pete Willemsen is a Professor and Department Head in the Department of Computer Science at the University of Minnesota Duluth, Swenson College of Science and Engineering. His research focuses on urban microclimate simulation, virtual reality education, and human-computer interaction. Current research includes One Night in Tarrytown (interdisciplinary VR learning environments) Development of Quick Environmental Simulation (QES) for urban wind and pollution modeling Investigation of radiance field content generation for VR applications Prior projects have explored: Locomotion mechanics in VR training systems GPU-accelerated environmental simulations Human spatial perception in virtual environments Low-cost Kinect-based tracking systems Collaborations span multiple disciplines including Communications, Digital Arts, and Mechanical Engineering. Current students include Asif Sijan (VR content generation), Christianah Adigun (spatial cognition), and Noah Miller (VR controller ergonomics).

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Mansour Mehranfarمشاهده پروفایل

پژوهشگر

Technical University of MunichGermany

Mansour Mehranfar is a Researcher at the Chair of Computing in Civil and Building Engineering, Technical University of Munich (TUM). He contributes to advancing AI applications in digital twinning of the built environment through the AI4Twinning research initiative, focusing on automated generation of semantic building models from point cloud data and imagery. His research centers on Digital Twinning, Building Information Modeling (BIM), and Computer Vision, with specific expertise in point cloud processing, semantic segmentation, and 3D reconstruction. He develops AI-driven frameworks that integrate deep learning with geometric modeling to convert raw sensor data into semantically enriched digital representations of buildings and infrastructure components. Recent publications reveal a strong emphasis on staircase modeling, indoor space documentation, and domain adaptation techniques. Key innovations include hybrid top-down/bottom-up approaches for Manhattan-world structures, parametric prototype model fitting, and multi-task learning frameworks that simultaneously handle scene parsing and 3D reconstruction from single images. Dr. Mehranfar actively supervises Master's theses, guiding students in topics such as load-bearing wall detection, staircase modeling automation, and BIM change management. He teaches Engineering Databases at TUM and collaborates within the university's BIM-Lab ecosystem, leveraging facilities for robotic fabrication and mobile machinery research.

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David Coeurjollyمشاهده پروفایل

استاد پژوهشی

University Bordeaux MontaigneFrance

David Coeurjolly is a Research Director at the French National Center for Scientific Research (CNRS) affiliated with LIRIS laboratory at Claude Bernard University Lyon 1. He leads the Origami research team and holds several leadership positions including Director of GdR Informatique Géométrique et Graphique and Co-lead of PEPR ICCARE. As co-founder of the DGtal library and General Chair of the Graphics Replicability Stamp Initiative, he significantly influences computational geometry research. His research spans digital geometry, discrete algorithms, Monte Carlo rendering, and geometry processing. Core innovations include work on optimal transport, low-discrepancy sampling, digital surface regularization, and curvature estimation methods. His approaches combine theoretical mathematics with practical implementations for computer graphics and scientific computing applications. Recent publications demonstrate strong focus on: Efficient transport algorithms (BSP-OT, Rectified Flows) Sampling theory innovations (Sobol' sequences, Owen scrambling) Digital geometry foundations (Gauss digitization, Laplace-Beltrami operators) Geometric transformations (bijective rotations, plane probing) Awards include: 🎫 Best Paper Award, SIGGRAPH Asia 2024 🎫 SGP Software Award 2016 He leads multiple ANR grants including SSLAM (point cloud ML), StableProxies (geometry processing), and MoCaMed (medical physics). His lab develops open-source tools like DGtal and maintains active collaborations through international initiatives like the Graphics Replicability Stamp.

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Sławomir Mikrutمشاهده پروفایل

استادیار

AGH University of KrakowPoland

Sławomir Mikrut is an Assistant Professor in the Department of Geoinformation, Photogrammetry and Environmental Remote Sensing at AGH University of Science and Technology's Faculty of Mining Surveying and Environmental Engineering, where he has been employed since 2003. His institutional affiliations include memberships in the AGH UST Senate, Association of Polish Surveyors, and Committee for Geoinformatics at the Polish Academy of Learning. Education M.Sc.Eng. in Industrial and Engineering Geodesy Photogrammetry and Remote Sensing (1997) Ph.D. in Surveying and Cartography (2003) with dissertation on digital image processing under Prof. Józef Jachimski Research Focus Professor Mikrut specializes in geospatial technologies with expertise spanning: Photogrammetric data acquisition and analysis Remote sensing applications in environmental monitoring Laser scanning and point cloud processing Algorithm development for image compression and feature detection Programming implementations for spatial data systems UAV-based surveying methodologies Professional Service Faculty governance through the Board of Mining Surveying and Environmental Engineering University-wide policy development via AGH UST Senate committees National academic leadership in geoinformatics through Polish Academy of Learning

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Oleg Davydovمشاهده پروفایل

استاد

Justus-Liebig-University GiessenGermany

Professor Oleg Davydov holds a Professorship for Numerical Analysis at the Department of Mathematics, University of Giessen, Germany. His research focuses on developing advanced numerical methods with strong theoretical foundations and practical applications. He maintains an active research program with numerous recent publications and international collaborations. Position: Professor of Numerical Analysis Institution: University of Giessen, Department of Mathematics Contact: Heinrich-Buff-Ring 44, 35392 Giessen, HRZ Room 117 Email: oleg.davydov@math.uni-giessen.de Homepage: https://oleg-davydov.de/ Professor Davydov's research interests center around meshless numerical methods, approximation theory, and computational mathematics. His primary focus areas include: Meshless Finite Difference Method - Developing robust meshless techniques that avoid the need for structured grids Finite Element Method - Particularly Bernstein-Bézier finite elements and specialized approaches for complex geometries Scattered Data Fitting - Creating efficient algorithms for approximating data on irregular domains Approximation Theory - Investigating theoretical properties of splines, radial basis functions, and other approximation tools His research has resulted in several software packages including mFDlab (Meshless Finite Difference Method), BBFEM (Bernstein-Bézier Finite Elements), and TSFIT (Two-Stage Scattered Data Fitting), demonstrating the practical implementation of his theoretical work. Analysis of Professor Davydov's recent publications shows a consistent focus on improving meshless methods, particularly in stencil selection, error analysis, and applications to complex problems. His work spans both theoretical developments (like error bounds and optimal approximation orders) and practical implementations (for fluid dynamics, manifold learning, and interface problems). A notable trend is the increasing sophistication of adaptive techniques and the handling of challenging geometries. Professor Davydov has supervised several doctoral students to completion, including: Gaelle Andriamaro Fabien Rabarison Abid Saeed Wee Ping Yeo His research group appears to maintain active collaborations with institutions worldwide, as evidenced by his extensive co-authorship network. The group focuses on developing both theoretical foundations and practical implementations of numerical methods, with particular attention to problems involving irregular domains, singularities, and complex geometries. Students in his group would gain experience in both theoretical analysis and software development for numerical methods.

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Floris Ernstمشاهده پروفایل

استاد

University of LübeckGermany

Signal processing and analysis for biomedical applications
Sensors for robotics
Motion tracking and recognition in medical technology
+۴ مورد دیگر

Prof. Floris Ernst serves as Professor of Medical Robotics at the Institute for Robotics and Cognitive Systems, University of Lübeck, where he has been faculty since 2017 after joining as a research associate in 2013. He holds significant leadership roles including membership on the Steering Committee of the Graduate School 'Computing in Medicine and Life Sciences' and editorial positions with IEEE Robotics and Automation Letters and Frontiers in Robotics and AI. His research spans medical robotics , signal processing for biomedical applications , sensors for robotics , and augmented reality in surgery . Key projects include SonoBox (robotic ultrasound for pediatric fracture diagnosis), TWIN-WIN (digital supertwin technology), and robotics applications in rescue medicine. His work consistently bridges theoretical algorithm development with clinical implementation, focusing on real-world medical challenges. Prof. Ernst's recent publications (2023-2025) demonstrate strong activity across medical imaging, rescue robotics, and navigation systems. Trends show increasing focus on deep learning applications in medical imaging, real-time motion tracking for radiosurgery, and autonomous systems for emergency response. His work frequently appears in top robotics and medical imaging venues including IEEE conferences and journals. IEEE Senior Member Associate Editor, IEEE Robotics and Automation Letters (Medical Robotics) Associate Editor, Frontiers in Robotics and AI (Biomedical Robotics) As an active supervisor, Prof. Ernst guides students through courses like Medical Robotics (CS4270) and Bachelor/Master projects, with numerous publications co-authored with students. His lab maintains strong industry and clinical collaborations, particularly in medical device development and clinical robotics applications. The Robotics Laboratory (RobLab) serves as the primary research environment for his team's work on medical and rescue robotics systems.

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Ruthann Godolleiمشاهده پروفایل

استاد

Macalester CollegeUnited States

Ruthann Godollei serves as the DeWitt Wallace Professor of Art in the Department of Art & Art History within Macalester College's Fine Arts Division, where she previously held the position of Dean of the Fine Arts Division. An internationally exhibiting artist, she utilizes printmaking and text-based installations to engage with urgent political and social themes, maintaining active creative production through 2025 with multiple upcoming exhibitions. Her educational foundation includes a BFA from Indiana University and an MFA from the University of Minnesota, providing the technical and conceptual framework for her interdisciplinary practice. Godollei's research centers on print media as a vehicle for political discourse, specializing in intaglio, relief, lithography, screenprint, and digital techniques. Her work critically examines power structures through projects like the Bunting series addressing voter suppression and immigration policy, and The Wish Machine pandemic-era community installations. She frequently incorporates participatory elements, as seen in News Flash Flash Cards where 64 languages were printed during a single-night event exploring water-related themes. Her practice consistently bridges studio art with activism, tackling issues including climate denial, gender equity, and electoral integrity through visually compelling installations. Recent work demonstrates an intensifying focus on political urgency, with 2024-2025 projects directly responding to contemporary crises through international collaborations. Her exhibitions increasingly feature multilingual and community-engaged approaches, while maintaining technical mastery across traditional and experimental print media. The recurring use of altered everyday objects—from washing machines to apothecary jars—creates accessible entry points for complex social commentary. Her distinguished recognition includes: McKnight Visual Artist Fellowship (2022-2023) Fulbright Canada Research Chair at the University of Alberta (2019) These fellowships provided critical support for international research and large-scale projects like The Wish Machine installations in Canada and Minnesota. While formal graduate student advising isn't documented in available materials, Godollei mentors undergraduates through courses including Political Art (cross-listed with Political Science) and Senior Studio Art Seminar. Her Printland Press LLC studio functions as both professional workspace and teaching laboratory for student printmakers. Godollei operates Printland Press LLC as her primary creative studio, while actively collaborating with collectives including Grupo Soap del Corazón. Her work resides in major institutions worldwide—from the Minneapolis Institute of Arts to KUMU National Art Museum in Estonia—and she continues developing new projects addressing 2025 political landscapes through upcoming exhibitions at NE Sculpture Gallery and Friends of the Mississippi River.

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Beomjoon Kimمشاهده پروفایل

دانشیار

University of MadeiraPortugal

Beomjoon Kim is an Associate Professor at the Graduate School of AI at Korea Advanced Institute of Science and Technology (KAIST). He directs the Humanoid Generalization (HuGe) lab, which focuses on creating general-purpose humanoids capable of efficient decision-making in complex environments. Education: Ph.D. in Computer Science from MIT CSAIL M.Sc. in Computer Science from McGill University B.Math in Computer Science and Statistics from University of Waterloo Professor Kim's research spans multiple areas of robotics and artificial intelligence, with a particular focus on humanoid robotics, task and motion planning, and robot learning. His work aims to bridge the gap between high-level task planning and low-level motion control, enabling robots to operate effectively in complex, real-world environments. He has made significant contributions to the field of geometric task and motion planning, developing novel algorithms that improve the efficiency and effectiveness of robot decision-making processes. Analysis of Professor Kim's recent publications reveals a strong focus on humanoid robotics, with particular emphasis on motion planning, object manipulation, and learning-based approaches. His work increasingly integrates deep learning techniques with traditional robotics algorithms, demonstrating a trend toward more data-driven approaches in robotics research. Many of his papers address the challenge of generalization in robotics, seeking to develop systems that can handle novel objects and environments without extensive retraining. Scientific Awards: Best Cognitive Robotics Paper award at ICRA 2017 Oral presentation (top 6% of accepted papers) at AAAI 2020 Oral presentation (top 6% of accepted papers) at AAAI 2019 Oral presentation (top 6% of accepted papers) at AAAI 2018 Spotlight presentation (top 3.5% of accepted papers) at NeurIPS 2018 Spotlight presentation (top 4% of accepted papers) at NeurIPS 2013 Plenary talk (top 12% of accepted papers) at CoRL 2020 Professor Kim actively mentors students at various levels, currently advising multiple Ph.D. and Master's students in the HuGe lab. His research is supported by grants that enable his team to pursue ambitious projects in humanoid robotics and AI. Through collaborations with institutions like MIT and industry partners, his work has significant impact on both academic research and practical applications of robotics technology. The HuGe lab, under Professor Kim's direction, has established itself as a leading research group in humanoid robotics. The lab maintains strong connections with other research institutions and regularly publishes in top-tier robotics and AI conferences. Current research directions include developing more efficient motion planning algorithms, improving robot manipulation capabilities, and exploring the integration of large language models with robotic systems.

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