جست‌وجوی استاد و پژوهشگر

David Mouldمشاهده پروفایل

استاد

• Computer Graphics
• Computer Games
• Non-Photorealistic Rendering
• +۴ مورد دیگر

David Mould is a Professor in the School of Computer Science at Carleton University. His research focuses on computer graphics, procedural modeling of natural phenomena, non-photorealistic rendering, and computer games. He holds a PhD from the University of Toronto (2002), MSc from the University of Saskatchewan (1996), and BSc from the University of British Columbia (1994). PhD: University of Toronto (2002) MSc: University of Saskatchewan (1996) BSc: University of British Columbia (1994) Research interests include procedural modeling of trees, lightning, and terrain; image stylization techniques such as stained glass transformation and wax crayon simulation; and nonlinear storytelling in games. His work emphasizes algorithmic innovation and perceptual quality in graphics. Recent publications span topics like texture synthesis, real-time video stylization, and fluid animation techniques. He leads the Graphics, Imaging, and Games (GIGL) research group at Carleton. Teaching responsibilities include courses in game development (COMP 1501–4501), technical writing (COMP 3301), and graduate courses on game design and image processing.

Paolo Prandoniمشاهده پروفایل

مدرس

• Signal Processing
• Audio Processing
• Image Processing
• +۵ مورد دیگر

Paolo Prandoni is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Computer and Communication Sciences (IC). He serves as a Scientist in the Audiovisual Communications Laboratory (LCAV) and teaches in the SSC-ENS and SIN-ENS units, focusing on signal processing theory and practical applications in audiovisual communications. He earned his PhD from EPFL after completing all prior education there, driven by childhood fascination with long-distance telephony. His doctoral work established foundations in communication systems that continue to inform his research. Prandoni's research spans audio/image processing, machine learning for media analysis, and DSP education. Key areas include computational photography (e.g., spectral imaging, stained glass rendering), speech quality assessment via transfer learning, music information retrieval (e.g., fingering prediction), and audience analytics through his company Quividi. His work consistently bridges theoretical signal processing with real-world implementation. Recent publications reveal a strategic shift toward machine learning integration in signal processing tasks, particularly non-intrusive speech assessment and lensless imaging reconstruction. Simultaneously, he advances DSP pedagogy through MOOC development and hands-on teaching tools using off-the-shelf hardware, emphasizing accessibility and practical skill development. No scientific awards are documented in the provided materials. He has advised PhD student Thanikachalam Niranjan (thesis: Image Based Relighting of Cultural Artifacts , 2016) and teaches Communication Systems and Computer Science courses. His educational impact extends through the open-access textbook Signal Processing for Communications (2008) and tools like MultiPub for maintainable online classes. Industry engagement includes Quividi co-founding (2006) and ongoing CSO role in attention analytics. As a core LCAV laboratory member, he collaborates on interdisciplinary projects including cultural heritage digitization, embedded signal processing systems, and real-time audience measurement, leveraging EPFL's infrastructure for both academic and commercial applications.

Laurent Caraffaمشاهده پروفایل

پژوهشگر

• 3D Data Processing
• Surface Reconstruction
• Point Cloud Processing
• +۷ مورد دیگر

Laurent Caraffa is a Researcher at Université Gustave Eiffel, working at the LaSTIG laboratory of IGN (National Institute of Geographic and Forest Information). His research focuses on large-scale 3D data processing, including surface reconstruction from point clouds and images, leveraging triangulated structures and implicit methods. His work also covers indexing and searching within point clouds for large-scale place recognition, with applications in urban environments and navigation systems. Caraffa's research interests span 3D Data Processing, Surface Reconstruction, Point Cloud Processing, Large-scale Place Recognition, Indexing and Retrieval, Big Data, Cloud Computing, Mathematical Optimization, 3D Mapping, and Photogrammetry in degraded conditions. His work bridges theoretical computational geometry with practical applications in geographic information systems and autonomous navigation. His publication record demonstrates significant contributions to distributed 3D processing, particularly through advancements in Delaunay triangulation, watertight surface reconstruction, and neural radiance fields. Recent work shows a clear trajectory toward more efficient and scalable methods for processing massive 3D datasets, with growing emphasis on implicit representations and learning-based approaches for 3D reconstruction. Caraffa actively participates in the scientific community through organizing events like the Big Data Day 2023 at IGN and contributing to major research projects. His work has resulted in publications in top-tier conferences including ICLR, CVPR, ISPRS, and IEEE Big Data, establishing him as a significant contributor to the field of large-scale 3D data processing. As a research supervisor, Caraffa currently co-supervises four PhD students working on projects funded by AID, Criteo, and Huawei, focusing on large-scale place recognition, implicit representations for 3D reconstruction, and 3D reconstruction in degraded conditions. He is also the co-founder of ExtraLabs, a company developing distributed computing solutions for cooperative digital twins, demonstrating the practical impact of his research.

Kavita Balaمشاهده پروفایل

استاد

• Computer Vision
• Artificial Intelligence
• Computer Graphics
• +۶ مورد دیگر

Kavita Bala is the 17th Provost of Cornell University and a Professor of Computer Science. She previously served as the inaugural Dean of the Cornell Ann S. Bowers College of Computing and Information Science, leading its transition to a degree-granting college by 2025, and as Chair of Cornell’s Department of Computer Science. Her academic leadership includes expanding faculty, establishing research programs like the Bowers CIS Undergraduate Research Experience (BURE), and securing a new research facility for computing and information science. Education: B.Tech (IIT Bombay), M.S. and Ph.D. (MIT, Computer Science) Bala’s research focuses on computer vision, artificial intelligence, and computer graphics , with groundbreaking work in material and style recognition using deep learning. Her innovations in crowdsourced training data and differentiable rendering have advanced visual search technologies and translucent material modeling, powering her startup GrokStyle. She pioneered AI techniques applied to environmental monitoring through projects like MONITRS and AllClear , addressing Earth observation challenges. Her scientific awards include: American Academy of Arts and Sciences (2025) SIGGRAPH Computer Graphics Achievement Award (2020) IIT Bombay Distinguished Alumnus Award (2021) ACM Fellow (2019) SIGGRAPH Academy Fellow (2020) As Provost, Bala drives strategic initiatives like the Cornell AI Initiative , creating interdisciplinary minors in AI and AI in Society, and establishing the Schmidt AI in Science postdoctoral program. She co-chaired a task force for generative AI guidelines in education.

Minglun Gongمشاهده پروفایل

استاد

• Visual Computing
• Computer Graphics
• Computer Vision
• +۶ مورد دیگر

Dr. Minglun Gong is a Professor and Director of the School of Computer Science at the University of Guelph (since 2019). Previously, he served as Professor and Head of the Department of Computer Science at Memorial University of Newfoundland. He holds a Ph.D. from the University of Alberta (2003), M.Sc. from Tsinghua University (1997), and B.Engr. from Harbin Engineering University (1994). His research focuses on visual computing, including computer graphics, computer vision, visualization, image processing, and pattern recognition. He has authored over 150 referred papers and holds patents in the field. He is an Associate Editor for Pattern Recognition and IEEE Signal Processing Letters , and has received awards such as the Izaak Walton Killam Memorial Award and multiple best paper awards. Dr. Gong has advised numerous students, including Ph.D./M.Sc. candidates and visiting scholars. His lab's recent work includes UAV path planning for urban reconstruction, image stylization techniques, and 3D human pose estimation. He actively participates in academic service, including editorial roles, conference program committees, and administrative roles at multiple institutions. His teaching spans courses in image processing, computational photography, and technical communication. He is also involved in administrative committees, such as Graduate Studies and Promotion at Memorial University. Key research contributions include advancements in transparent object modeling, underwater 3D reconstruction, and image-to-image translation. His work emphasizes practical applications in fields like medical imaging, autonomous systems, and environmental modeling.

Craig Gotsmanمشاهده پروفایل

استاد

• Computer Graphics
• Computational Geometry
• 3D Animation
• +۵ مورد دیگر

Craig Gotsman is a Professor and Dean at the Ying Wu College of Computing, New Jersey Institute of Technology. He previously held roles at Cornell Tech, Technion, ETH Zurich, and MIT. His research focuses on computational geometry, computer graphics, and 3D animation. Ph.D. in Computer Science, Hebrew University of Jerusalem (1991) His work spans geometric modeling, mesh processing, and applications in animation and visualization. Recent research trends include gaze correction in video conferencing, mesh parameterization, and spectral compression techniques. Notable awards include Fellowships in the US National Academy of Inventors and the Academy of Europe, multiple best paper awards, and the Technion's Hewlett Packard Chair in Computer Engineering. Gotsman has mentored over 50 postgraduate students and holds ten US patents. He co-founded three companies: Virtue 3D Inc. (acquired by NVIDIA), Estimotion Inc. (now ITIS Israel Ltd.), and CatchEye.

• Computational Imaging
• Computational Photography
• Neuroimaging
• +۷ مورد دیگر

Nick Antipa is an Assistant Professor at the University of California, San Diego, affiliated with the Jacobs School of Engineering and the Department of Electrical and Computer Engineering. His work focuses on computational imaging systems that integrate optics, sensors, and algorithms to enable novel imaging modalities. PhD in Electrical Engineering from UC Berkeley Former optical metrology engineer at Lawrence Livermore National Lab Research interests span computational imaging , lensless camera design , and single-shot high-dimensional optical signal capture . His lab develops systems like DiffuserCam for compressive 3D imaging and Miniscope3D for miniature fluorescence microscopy. Recent publications address differentiable wave optics, high-speed video reconstruction, and marine imaging applications. Awards include Best Paper at ICCP 2016/2019 and Best Demo at ICCP 2017. His lab explores machine learning-driven optical design and differentiable rendering frameworks for end-to-end optimization of imaging systems. Current projects include oceanographic imaging, computational photography, and infrared spectroscopy acceleration.

• Interactive Global Illumination
• Perceptual Graphics
• Non-physical Graphics
• +۱ مورد دیگر

Tobias Ritschel is a Professor of Computer Graphics at University College London . His research spans advanced rendering techniques, perceptual modeling, and data-driven graphics, with a focus on bridging physical accuracy and artistic flexibility in visual computing. Key research themes include: Interactive Global Illumination : Real-time simulation of complex lighting effects on GPUs Perceptual Graphics : Human vision-driven rendering and display optimization Non-physical Graphics : Beyond-photorealistic techniques for artistic expression Data-driven Graphics : Leveraging large datasets for novel rendering and modeling approaches His recent work emphasizes neural rendering , differentiable graphics , and X-ray tomography , with applications in 3D reconstruction , NeRF manipulation , and holographic imaging . Notable scientific achievements include the Eurographics Young Researcher Award 2014 and Eurographics Thesis Award 2011 . He has advised multiple PhD students including Philipp Henzler (EG PhD Award 2024) and Thomas Leimkühler (Otto Hahn Medal 2019), while actively contributing to conference leadership as co-chair for EGSR 2024 and Pacific Graphics 2024 . His team collaborates on X-ray reconstruction with Pablo Villanueva-Perez and works on 3D perception with Anthony Steed.

• Trustworthy Machine Learning
• Algorithmic Fairness
• Robust Distributed Learning
• +۵ مورد دیگر

Sanmi (Oluwasanmi) Koyejo is an Assistant Professor in the Department of Computer Science at Stanford University and an adjunct Associate Professor at the University of Illinois at Urbana-Champaign. He leads Stanford Trustworthy AI Research (STAIR), working to develop the principles and practice of trustworthy machine learning with applications to neuroscience and healthcare. Koyejo holds affiliations with multiple Stanford institutes including SAIL, HAI, CRFM, AIMI, AI Safety, Machine Learning Group, and Bio-X. Koyejo completed his Ph.D. at the University of Texas at Austin followed by postdoctoral research at Stanford University. His research bridges theoretical machine learning with practical healthcare applications, focusing on developing robust and fair AI systems that can be trusted in critical domains. His work spans algorithmic fairness, robust distributed learning, metric elicitation, and applications to medical imaging and neuroscience. His recent publications demonstrate a strong focus on emerging challenges in AI including emergent abilities in large language models, fairness in medical AI, federated learning, and robustness against adversarial attacks. His work has increasingly addressed real-world healthcare challenges through deep learning applications to medical imaging, particularly chest radiographs for disease detection. Scientific Awards: NSF CAREER Award 2021 Skip Ellis Early Career Award Sloan Research Fellowship Frederick E. Terman Faculty Fellow (2022) Best Paper Award from UAI Kavli Fellowship IJCAI Early Career Spotlight Koyejo actively mentors a large research group with numerous PhD students and postdocs. His research has been supported by significant grants including NSF funding for projects like 'Fair Federated Representation Learning for Breast Cancer Risk Scoring.' He serves in leadership roles including as General Co-chair for NeurIPS 2022 and President of the Black in AI organization. His STAIR research group focuses on developing trustworthy AI principles and practices, with applications to healthcare and neuroimaging. The group collaborates extensively with healthcare institutions including OSF Healthcare and participates in major initiatives like the NIH-funded MIDRC and the NSF AI research institute AIFARMS.

Or Patashnikمشاهده پروفایل

مدرس ارشد

• Computer Graphics
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Or Patashnik is a Senior Lecturer at the School of Computer Science , Tel Aviv University . His research lies at the intersection of Computer Graphics , Computer Vision , and Machine Learning , focusing on Generative Models for Image/Video Generation , Semantic Editing , and Personalization with controllable user intent. PhD in Computer Science from Tel Aviv University under Daniel Cohen-Or His work addresses challenges in localizing shape variations in text-to-image diffusion models, developing prompt-mixing techniques and attention-based localization methods. Recent projects include Sharp-It for 3D synthesis, Stable Flow for training-free editing, and LCM-Lookahead for encoder-based personalization. Key publication trends span Diffusion Models , Generative Adversarial Networks (GANs) , Attention Mechanisms , and Text-to-Image Manipulation . Collaborations include researchers like Daniel Cohen-Or, Rinon Gal, and Dani Lischinski across institutions such as Stanford and Carnegie Mellon.

Ayush Tewariمشاهده پروفایل

استادیار

• Computer Vision
• 3D Reconstruction
• Neural Rendering
• +۳ مورد دیگر

Ayush Tewari is an Assistant Professor at the University of Cambridge. Previously, he was a postdoctoral researcher at MIT CSAIL under Bill Freeman, Josh Tenenbaum, and Vincent Sitzmann, and completed his Ph.D. at the Max Planck Institute for Informatics under Christian Theobalt. His research focuses on visual perception, developing methods to infer 3D structured representations from images and videos, aiming to bridge the gap between human perceptual capabilities and machine learning systems. Key research interests include neural rendering, inverse rendering, 3D reconstruction, and generative models. Notable contributions include advancements in Neural Radiance Fields (NeRF), diffusion models for inverse problems, and human-centric perception studies. His work has been published in top venues such as SIGGRAPH, CVPR, ICCV, and NeurIPS. Recent research trends emphasize ambiguity-aware inverse rendering, stochastic inverse problem solving using diffusion models, and integrating forward models for 3D scene inference. His work on Diffusion with Forward Models (NeurIPS 2023) proposes a novel framework for solving inverse problems without direct supervision. Awards: Best Paper Honorable Mention at BMVC 2022 (VoRF: Volumetric Relightable Faces). Labs/Projects: Core contributor to the DFM (Diffusion with Forward Models) project, advancing 3D scene understanding via probabilistic methods.

Carlo Alberto Avizzanoمشاهده پروفایل

دانشیار

• Robotics and Automation
• Human-Robot Interaction
• Computer Vision
• +۶ مورد دیگر

Carlo Alberto Avizzano serves as Associate Professor in Robotics and Automation at the University of Pisa's School of Engineering, Department of Information Engineering. He coordinates the Department of Excellence in Robotics & Artificial Intelligence (MUR) and leads the Intelligent Automation System Research Group. Research spans robotics, human-robot interaction, computer vision, and control systems Specializes in creating intelligent automation systems with cognitive capabilities Integrates AI, machine learning, and mechatronics for robust autonomous systems His research focuses on developing robots that learn from human examples, adapt to changing environments, and interact through advanced perception systems. Current work emphasizes wearable robotics, UAVs, and industrial automation solutions with applications in medical rehabilitation, firefighting, and manufacturing. He employs distributed computing architectures integrating sensors, real-time control, and knowledge transfer algorithms. Publications reveal strong emphasis on practical implementations: 15 recent works cover exoskeleton design (2024), UAV firefighting systems (2024), industrial bin-picking datasets (2024), and haptic interfaces (2012-2023). Key trends show progression from virtual reality systems (2006-2008) toward modern AI-integrated robotics with industrial and medical applications. Teaching responsibilities include PhD courses in Sensors for Construction, Python Programming for HealthScience, and Digital Perception; plus undergraduate Mechatronics and Computer Vision labs. He serves on PhD boards for Emerging Digital Technologies and Health Science Technology. Extensive patent portfolio including haptic interfaces (2012), sailing simulators (2006), and UAV systems (2024) Research directly translated to commercial products and spin-off companies

Iliyan Georgievمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Light Transport Simulation
• Monte Carlo Rendering
• +۴ مورد دیگر

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.

Melvin Wongمشاهده پروفایل

استادیار

• Transportation Engineering
• Machine Learning
• Urban Mobility
• +۲ مورد دیگر

Melvin Wong is an Assistant Professor in the Department of Urban Planning and Transportation within the Built Environment school at Eindhoven University of Technology. His research focuses on transportation engineering, machine learning applications in urban mobility, reinforcement learning for traffic systems, and sustainable transportation solutions. He utilizes advanced computational methods including graph neural networks, generative AI, and physics-informed models to address challenges in traffic prediction, electric vehicle infrastructure, and urban design. His research interests encompass transportation optimization, spatiotemporal modeling, generative design methods, and behavioral analysis in urban systems. Recent publications demonstrate a strong focus on AI-driven solutions for traffic management, battery-swapping systems, and multimodal design optimization. Dr. Wong has received recognition including the Best Research Paper Award (2024) and Swiss Government Excellence Scholarship (2020). He contributes to academic activities through conference presentations, peer reviews, and course development in urban mobility and big data analytics.

• Computer Vision
• 3D Reconstruction
• Perceptual Information Processing
• +۷ مورد دیگر

SATO Jun holds the position of Professor at the Department of Information Engineering (メディア情報分野) within the Faculty of Engineering at Nagoya Institute of Technology. He received his Ph.D. in Information Engineering from the University of Cambridge (1993–1996) and previously served as a Research Assistant at Cambridge (1996–1998). His research focuses on perceptual information processing and intelligent informatics, with specializations in computer vision, 3D reconstruction, and optical engineering applications. He has authored influential books like Computer Vision - Geometry of Vision (1999) and Computer Graphics (2017), and contributed to international publications such as Springer's Computer Vision: A Reference Guide (2020). Key professional roles include serving as President of the IEEE Nagoya Branch since 2023, Associate Editor of the International Journal of Computer Vision (Springer, 2010–present), and committee member for various organizations including Japan's Ministry of Education (2015–present) and the Nagoya City Business Potential Evaluation Committee (2008–2015). He has been recognized with prestigious awards including the BMVC Best Science Paper Prize (1994, 1997) and ITE Niwa-Takayanagi Prize (2015). His research extends to industrial collaborations, evidenced by patents like the "3D Information Presentation Device" (2014–2017) and "Position Detecting Device" (2016–2019). Recent work emphasizes applications in automotive safety, occluded object reconstruction, and novel imaging systems using advanced optical configurations and neural networks.