Find Academic Supervisors

Tzu-Mao LiView profile

Assistant Professor

• Computer Graphics
• Rendering Algorithms
• Differentiable Programming
• +5 more

Tzu-Mao Li is an Assistant Professor in the Department of Computer Science and Engineering (CSE) at the University of California, San Diego (UCSD), affiliated with the Center for Visual Computing. His research focuses on differentiable graphics algorithms, combining classical visual computing with modern machine learning techniques. He holds a Ph.D. from MIT CSAIL under Frédo Durand and a postdoc at MIT and UC Berkeley with Jonathan Ragan-Kelley. His work spans rendering, programming languages for graphics, Monte Carlo methods, and inverse problems. Education: B.S. and M.S. from National Taiwan University (2011-2013), advised by Yung-Yu Chuang. Ph.D. from MIT CSAIL (Computer Graphics Group), advised by Frédo Durand. Postdoctoral research at MIT and UC Berkeley with Jonathan Ragan-Kelley. Research Interests: Differentiable rendering, Monte Carlo integration, programming language design for visual computing, physical simulation, adversarial machine learning, and applications in computer vision and robotics. Key areas include rendering algorithms (path tracing, bidirectional methods), optimization techniques (MCMC, gradient-based), and neural representations (SDFs, neural fields). Publications focus on advancing rendering algorithms, differentiable systems, and applications in inverse problems. Notable contributions include edge sampling for differentiable rendering, warped-area sampling, and diffusion models for BSDF sampling. Awards: ACM SIGGRAPH 2020 Outstanding Doctoral Dissertation Award, multiple Best Paper Awards at SIGGRAPH, and oral presentations at ICCV. Teaching: Courses include CSE 167 (Computer Graphics), CSE 168 (Rendering), and CSE 272 (Advanced Image Synthesis), emphasizing physically-based methods and programming.

Pavel PanchekhaView profile

Assistant Professor

• Programming Languages
• Program Synthesis
• Verification
• +4 more

Pavel Panchekha is an Assistant Professor in the School of Computing at the University of Utah, where he holds the Warnock Chair for Junior Faculty. His research spans programming languages, web browsers, and numerical analysis, with a focus on developing programming language techniques to address challenges across computer science. Dr. Panchekha received his educational training at prestigious institutions: PhD in Computer Science from the Paul G. Allen School for Computer Science and Engineering at the University of Washington, advised by Michael D. Ernst and Zachary Tatlock BS in Mathematics from MIT Panchekha's research program has two major thrusts. First, he works on web browser internals , with projects including fuzzing layout invalidation, multi-tenant garbage collection, and optimizing 2D graphics. He is also authoring a textbook on web browsers that informs much of this research. Second, he focuses on automatic numerical analysis , with projects such as automatic accuracy improvement, synthesis via term rewriting, scalable static accuracy analysis, and math library implementation. He leads the FPBench and Herbie projects, which are major deployments of his research. His scholarly output demonstrates consistent contributions across programming languages, verification, and numerical methods. Recent work shows a growing emphasis on bidirectional typing systems, layout invalidation in browsers, and robust floating-point error analysis. His publications reveal a trajectory from foundational work on floating-point accuracy (notably the Herbie tool that won a Distinguished Paper Award at PLDI 2015) toward more comprehensive systems for program synthesis, verification, and browser optimization. Panchekha has received significant recognition for his research contributions: NSF Fellowship ARCS Foundation Fellowship Adobe Research Fellowship Wissner-Slivka Foundation Fellowship 2015 PLDI Distinguished Paper Award for work on the Herbie numerical analysis and repair tool As an advisor, Panchekha mentors a substantial group of students across multiple levels. He currently advises six students: Marisa Kirisame (PhD), Bhargav Kulkarni (PhD), Yumeng He (PhD), Artem Yadrov (MS), Jesus Ponce (BS), and Jonas Regehr (BS). Previously, he has advised over twenty students including PhD candidates like Ian Briggs and numerous MS and BS students. His advising spans theoretical topics in programming languages and practical applications in web browsers and numerical computing. Panchekha leads research groups focused on programming languages applications to web browsers and numerical analysis. His work on the Herbie tool for floating-point accuracy improvement has become influential in the programming languages community, and his more recent work on browser internals is shaping how researchers understand and optimize modern web rendering engines. He is currently developing a textbook on web browsers that aims to synthesize knowledge about browser architecture and implementation.

• Sound and Music Computing
• Human-Computer Interaction
• Human-Centered Computing
• +6 more

Dr. Sam Ferguson is a Senior Lecturer at the School of Computer Science, University of Technology Sydney (UTS), with a multidisciplinary background in music performance, cognitive science, and psycho-acoustics. His research explores the intersection of sound, music, and human experience through creative coding, machine learning, and interactive systems. Key Research Areas: Sound and Music Computing, Human-Computer Interaction, Creative Coding, Cognitive Science, Installation Art, and Acoustics. Current Projects: ARC Linkage project on creative coding and multiplicitous media; industry collaborations on IoT-based audiovisual systems. Recent Publications: Focus on spatial audio complexity, gestural interaction with networked sound, music emotion recognition frameworks, and robotic performance through genre-based cultural platforms. Leadership Roles: Director of Teaching & Learning Engagement; former Deputy Head of School (Teaching and Learning); active in ACM Creativity and Cognition Steering Committee. Teaching: Courses like Digital Media Studio , Prototyping Physical Interaction , and Data Processing using R within UTS's interdisciplinary Software Development Studio.

Jeppe Revall FrisvadView profile

Associate Professor

• Computer Graphics
• Digital Image Synthesis
• Realistic Rendering
• +7 more

Jeppe Revall Frisvad is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), within the Visual Computing group. His work bridges computer graphics, material science, and applied optics, focusing on realistic rendering and material appearance modeling. Ph.D. in Computer Graphics, DTU Informatics (2008) M.Sc. in Engineering (Applied Mathematics), DTU (2004) Education in University Teaching, LearningLab DTU (2008–2009) His research centers on computing material appearance from physical and chemical properties, developing faster and more accurate physically based rendering methods. Key areas include light scattering, bidirectional reflectance distribution functions (BRDF), anisotropy, and translucency modeling. Applications span computer games, movies, digital prototyping, architectural visualization, and training simulators. His recent publications (2024–2025) reflect a strong trend in digitizing material appearance, especially for 3D printing and food science, using spectrophotometry and optical validation. The work combines computer graphics with interdisciplinary applications in food structure and biological imaging, emphasizing measurement, modeling, and simulation. Scientific Awards: Research travel prize from AEG Elektronfonden (2006) IGDA scholarship for GDCE 2005 DTU Ph.D. scholarship (2004) He actively supervises Ph.D. students and leads multiple research projects, including those on organ-on-chip imaging, cheese quality analysis, and optical modeling of teeth. He collaborates across disciplines and institutions, with external research stays at UC San Diego and the University of Otago. He is involved in projects integrating AI with 3D imaging and material digitization. Jeppe is a key member of the Visual Computing research environment at DTU, contributing to both fundamental rendering algorithms and practical applications in industry and science.

Iliyan GeorgievView profile

Researcher

• Computer Graphics
• Light Transport Simulation
• Monte Carlo Rendering
• +4 more

Iliyan Georgiev is a research scientist at Adobe, specializing in advanced computer graphics and physically based rendering. He holds a Bachelor's degree in Computer Science from Sofia University, Bulgaria, and a Master's degree from Saarland University, Germany, supported by a fellowship from the Max-Planck Institute. His work focuses on improving rendering efficiency through Monte Carlo methods, light transport simulation, and neural rendering techniques. Georgiev's research bridges the gap between theoretical and applied graphics, with contributions to bidirectional rendering algorithms, importance sampling, and 3D scene modeling. His publications highlight innovations in variance reduction, path sampling, and material-aware rendering. He has collaborated with leading institutions and companies, including Intel Visual Computing Institute, Disney Research Zürich, Weta Digital, Chaos Group, and Autodesk. Notable scientific awards include the Best Student Paper Award at ICPRAM 2025 and the Best Paper Award at EGSR 2024.

Ewelina RupnikView profile

Researcher

• Photogrammetry
• Pose Estimation
• Image-based 3D Reconstruction
• +7 more

Ewelina Rupnik is a prominent researcher in photogrammetry and remote sensing, currently holding a researcher position at the Laboratory on Geographic Information Science (LaSTIG) at the French National Institute of Geographic and Forest Information (IGN) since 2017. She also serves as an associate researcher at the Paris Institute of Earth Physics (IPGP). Rupnik has established herself as a leading expert in historical imagery processing, neural radiance fields, and bundle adjustment techniques. Her educational background includes a PhD in photogrammetry from the Vienna University of Technology (2015), an MSc in Engineering in Photogrammetry from AGH University of Science and Technology, Poland (2005-2010), and an Erasmus exchange at the Technische Universitaet Muenchen, Germany (2009/2010). She recently completed her Habilitation from Université Gustave Eiffel in June 2025. Rupnik's research focuses on advancing photogrammetric techniques for processing historical imagery, developing novel neural radiance field approaches for satellite imagery, and improving bundle adjustment methodologies. Her work bridges traditional photogrammetry with modern deep learning techniques, particularly in the context of sparse satellite views and historical multi-epoch imagery. She has made significant contributions to the open-source MicMac photogrammetry software project. Her recent publications demonstrate a strong trend toward integrating deep learning with traditional photogrammetric methods, with a particular emphasis on satellite and historical imagery analysis. The research spans from fundamental algorithm development (like SparseSat-NeRF and Pointless Global Bundle Adjustment) to practical applications in earth sciences (landslide, earthquake, and glacier volume mapping). 2022 EuroSDR PhD Award for Corona satellite imagery processing Best Paper Award for BRDF-NeRF research Outstanding Reviewer at CVPR 2025 Rupnik actively contributes to the academic community through editorial and leadership roles, including serving as Editor-in-Chief of the French Journal for Photogrammetry and Remote Sensing (2021-2025), Advisory Board Member of the International Journal of Photogrammetry and Remote Sensing (2024-present), and Co-chair of the ISPRS Working Group on Image Orientation and Sensor Fusion (2022-2026). She regularly teaches at Université Paris Cité & ENSG (~30 hours/year since 2015) and has conducted numerous workshops worldwide on photogrammetry with historical images and MicMac software.

• Computer Vision
• Photometric Stereo
• 3D Reconstruction
• +3 more

Fumio Okura is a professor at Osaka University , specializing in Computer Vision and 3D Reconstruction . His work bridges Photometric Stereo , Neural Rendering , and Medical Imaging , with a focus on cognitive decline prediction and plant modeling . He collaborates extensively with researchers like Hiroaki Santo and Yasuyuki Matsushita . Education: Ph.D. in Computer Science (Osaka University) Research Interests span Computer Vision , Photometric Stereo , 3D Reconstruction , and Biomedical Applications . His recent work includes HoGS for object reconstruction and TreeFormer for botanical structure estimation. Publications trend toward neural rendering , reflectance modeling , and augmented reality . Notable contributions include PPGCN for cognitive detection and MVCPS-NeuS for multi-view photometric stereo. Labs & Collaborations include the Osaka University Computer Vision Lab , working with teams on photometric analysis and medical imaging .

James FerwerdaView profile

Associate Professor

• Imaging Science
• Computer Graphics
• Visual Perception
• +2 more

James A. Ferwerda is an Associate Professor in the Chester F. Carlson Center for Imaging Science at the Rochester Institute of Technology. His research focuses on computational models of human vision derived from psychophysical experiments and developing advanced imaging systems. He holds a B.A. in Psychology, M.S. in Computer Graphics, and Ph.D. in Experimental Psychology from Cornell University. Education: Ph.D. Experimental Psychology, Cornell University M.S. Computer Graphics, Cornell University B.A. Psychology, Cornell University Dr. Ferwerda's research explores visual perception mechanisms, material appearance modeling, and psychophysical methods. His work bridges computer graphics with human vision science to create practical imaging tools and measurement techniques. Recent publications (2018-2023) show a consistent focus on material perception under varied environmental conditions. Key trends include: development of psychophysics tools (FechDeck), multispectral visualization techniques, and surface property analysis. His 15 most recent articles demonstrate strong cross-disciplinary integration of computer vision, VR technology, and perception science. Scientific Awards: Seminal Graphics Paper, ACM SIGGRAPH 50th Anniversary (2023) Top 2% most-cited scientist globally (Stanford University, 2022) Xerox Chair in Imaging Science (2011) 3 Best Paper awards at major conferences National Academy of Engineering Frontiers of Engineering (2003, 2005) Dr. Ferwerda has advised over 20 graduate students including 15 Ph.D. candidates. Major grants include NSF IIS-1064410 ($398,810 for material appearance modeling) and collaborations with Corning, Xerox, and Hewlett-Packard. He directs the Visual Perception Laboratory and collaborates with international teams on material perception research.

• Kansei informatics
• Soft computing
• Colour emotion
• +6 more

Keiichi Muramatsu is Associate Professor (tenure-track since 2025) at Waseda University’s Global Education Center, Japan. Holding a Doctor of Human Sciences from Waseda, he has successively served as Assistant Professor at Saitama University and as part-time Lecturer at Bunkyo University. He is an active member of the Japan Society of Kansei Engineering, the Japanese Society for Artificial Intelligence and the Color Society of Japan. Education: Doctoral Program, Human Sciences, Waseda University (2009-2014) Master’s Program, Human Sciences, Waseda University (2007-2009) Bachelor, Department of Human Informatics and Cognitive Sciences, Waseda University (2003-2007) Research interests: Muramatsu integrates Kansei informatics and soft computing to quantify human emotional and aesthetic responses. His work spans colour-emotion modelling for lighting and butterfly aesthetics, bio-signal driven emotion recognition using chaotic fingertip plethysmography, and gait classification via deep learning for personalised rehabilitation and stumble prevention. He also develops ontological frameworks to share emotional knowledge across educational and product-service system design domains. Publication trends: Across 62 Scopus papers (h-index 7), Muramatsu’s recent outputs demonstrate a convergence of affective engineering and AI: convolutional networks visualise gait features for training feedback, neural predictors estimate learner states in intelligent tutoring systems, and psychophysiological models link colour, fragrance and 3-D sound to emotional dimensions. Studies consistently combine subjective evaluations with objective indices such as fNIRS, skin conductance and chaotic exponents. Grants & industry links: He has led JSPS KAKENHI grants and internal Waseda special research projects, collaborated with Toyota on driver affective scales, and contributed to exoskeleton development with Saitama medical device firms. His work on LED automotive lighting and mobility-scooter stress evaluation reflects ongoing industry knowledge transfer. Advising & labs: While specific student names are not disclosed, Muramatsu supervises graduate research on gait-assistive devices, emotion recognition wearables, and ITS architectures within the Global Education Center’s interdisciplinary environment.

Pascal GrittmannView profile

Research Fellow

• Global Illumination
• Monte Carlo Rendering
• Importance Sampling
• +2 more

Dr. Pascal Grittmann is a Research Fellow in Computer Graphics at Saarland University, specializing in efficient and robust rendering algorithms. His research advances Monte Carlo methods, importance sampling, and bidirectional rendering techniques for global illumination. Current DFG-funded project develops adaptive bidirectional rendering solutions requiring minimal user configuration. Research aims to create universal rendering algorithms balancing efficiency and robustness across diverse scenes. Publications demonstrate innovations in variance-aware sampling, path guiding optimizations, and caustic rendering. Served as Eurographics Symposium on Rendering conference co-chair (2021). Teaching includes practical implementation of rendering techniques using Vertex Connection and Merging methods. Maintains collaborations with international computer graphics researchers.

Ravi ChughView profile

Associate Professor

• Programming Languages
• Human-Computer Interaction
• Direct Manipulation Programming Systems
• +3 more

Ravi Chugh is an Associate Professor in the Department of Computer Science at the University of Chicago. His work bridges Programming Languages (PL) and Human-Computer Interaction (HCI), focusing on direct manipulation programming systems and hole-driven development. Affiliation: University of Chicago Role: Director of Undergraduate Studies Research interests: Direct Manipulation Programming Systems Type Systems Program Synthesis Structured Editing Visual Programming His recent work includes Ragged Blocks (UIST 2025) for structured text rendering, Code Style Sheets (OOPSLA 2025) for styling code, and projects like Sketch-n-Sketch and Hazel for bimodal programming environments. Professional Activities: Program Committee: HATRA 2025, VL/HCC 2023, PLDI 2022, POPL 2021 Artifact Evaluation Co-Chair: PLDI 2018-2020 Editorial Board: Journal of Functional Programming (2020–)

Alisa JungView profile

Researcher

• Computer Graphics
• Rendering
• Physically-based Rendering
• +4 more

Dr. Alisa Jung is a researcher at Karlsruhe Institute of Technology's Computer Graphics Group, specializing in physically-based rendering techniques. Her work addresses fundamental challenges in spectral rendering, fluorescence modeling, and path space optimization for photorealistic image synthesis. Research focuses on Physically-based Rendering , Spectral Rendering with Fluorescence , and Path space regularization . She develops novel algorithms for handling complex light transport phenomena, particularly fluorescence effects in materials, and improves Monte Carlo rendering efficiency through path guiding and bias detection methods. Recent publications demonstrate consistent innovation in spectral rendering (2018-2020), with emphasis on fluorescence modeling, upsampling techniques, and statistical validation of renderers. Her work bridges theoretical light transport models with practical implementation in rendering systems. Dr. Jung actively supervises Master's and Bachelor's theses on path guiding, spectral rendering, and GPU-based graphics algorithms, reflecting her expertise in rendering optimization and material modeling. Teaching assignments include core computer graphics courses and advanced seminars at KIT since 2017. The Computer Graphics Group at KIT, where she is based, operates within the Institute for Visual Design and Computer Graphics, focusing on cutting-edge research in rendering algorithms, material appearance, and visual computing systems.

Jonas UngerView profile

Professor

• Computer Graphics
• Image Processing
• Artificial Intelligence
• +3 more

Jonas Unger is a Professor at Linköping University's Department of Engineering and Natural Sciences (ITN) and Media and Information Technology (MIT). As a research leader in computer graphics and image processing, he contributes to advanced visualization techniques, GPU computing, and AI applications in healthcare and climate adaptation. Specializes in BRDF modeling, spectral light field imaging, and augmented intelligence Active in AI4Climateadaptation and Wallenberg Autonomous Systems Program (WASP) Recipient of the 2023 Chester Carlson Research Prize Develops AI methods for medical imaging (SCAPIS dataset) and autonomous systems His recent work focuses on deep learning for material acquisition, real-time rendering optimizations, and visual analytics pipelines for disaster response systems. Current projects include multi-agent reinforcement learning for air traffic control and GPU-accelerated signal processing techniques. Scientific awards include: Chester Carlson Research Prize (2023) He collaborates with Visual Sweden's Augmented Intelligence platform, co-leads major visualization conferences, and contributes to Horizon 2020 Marie Skłodowska-Curie funded research. Contact: jonas.unger@liu.se

Ehsan MiandjiView profile

Assistant Professor

• Computer Graphics
• Computer Vision
• Machine Learning
• +4 more

Ehsan Miandji is an Assistant Professor and Docent at Linköping University's Department of Science and Technology (ITN), part of the Faculty of Science and Engineering. His research focuses on computer graphics, computer vision, and machine learning, with a particular emphasis on BRDF modeling, light field imaging, compressed sensing, and sparse representation techniques. He is affiliated with the Computer Graphics and Image Processing group and the Wallenberg Autonomous Systems Program (WASP). His work spans both theoretical advancements and applied methodologies in visual data processing. Recent research includes optimizing BRDF acquisition via FROST-BRDF, advancing multidimensional compressed sensing for spectral light fields, and developing sparse representation frameworks for bidirectional texture functions (BTF). Miandji collaborates with interdisciplinary teams within the Media and Information Technology (MIT) division, contributing to projects that bridge computational imaging, algorithm design, and real-world applications. His publications reflect a strong commitment to pushing boundaries in visual data compression, rendering efficiency, and perceptual quality assessment of material models.

Shlomi SteinbergView profile

Assistant Professor

• Computer Graphics
• Light Transport Simulation
• Wave Optics
• +3 more

Shlomi Steinberg is an Assistant Professor in the Department of Computer Science at the University of Waterloo, Faculty of Engineering. He holds a Ph.D. in Computer Science from the University of California, Santa Barbara (2023), an M.Sc. in Computer Science and Mathematics from the Weizmann Institute of Science (2017), and a B.Sc. in Computer Science and Mathematics from the Open University of Israel (2015). His research focuses on advancing computer graphics and rendering techniques, particularly in wave-optical light transport, diffraction modeling, and light-matter interaction. Key areas include developing accurate simulations of light behavior in complex environments, optimizing optical systems for imaging and energy concentration, and modeling phenomena like speckle patterns and birefringence-induced iridescence. His work integrates mathematical frameworks such as Hermite-Gauss functions with computational methods to bridge physical optics theory and practical rendering applications. Steinberg's publications demonstrate contributions to both foundational optics research and applied computer graphics, with recent emphasis on wave-based rendering and anisotropic material simulation. His research bridges disciplines like physics, mathematics, and engineering to address challenges in realistic visual simulation and optical system design. He advises students in advanced computer graphics topics and contributes to academic communities through roles in the Professors group at Waterloo. His work is accessible via his personal webpage here .