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دانشیار
Dr. Daniel Ritchie is an Associate Professor of Computer Science at Brown University , where he co-leads the Brown Visual Computing group. His research integrates Computer Graphics , Artificial Intelligence , and Machine Learning to develop neurosymbolic methods that combine procedural models and deep neural networks for 3D shape and scene creation.
مدرس ارشد
Wilhelmina Zoe Statham serves as Senior Lecturer at Uppsala University's Department of Game Design, Campus Gotland. She specializes in interdisciplinary educational technology and inclusive pedagogy, championing student-centric learning through Universal Design for Learning (UDL), Technology-Enhanced Education (TEE), and ethical AI applications across diverse academic disciplines. Her research centers on four interconnected domains: AI in Education : Investigating how artificial intelligence transforms learning processes and equalizes access for neurodivergent, underprivileged, and underrepresented students UDL & Inclusive Pedagogy : Developing accessible, equitable learning environments through strategic implementation of universal design principles Technology-Enhanced Education : Evaluating digital tools and multimodal engagement to improve learning outcomes Scholarship of Teaching and Learning : Systematically refining assessment strategies and curriculum development through applied research Publication analysis reveals a clear trajectory from technical game design foundations (2019-2021 photogrammetry and modular architecture research) toward contemporary educational innovation (2022-2025 motion capture and AI integration studies). This evolution demonstrates her strategic pivot from game-specific applications to broader pedagogical frameworks applicable across academic disciplines. Dr. Statham actively supervises PhD and Bachelor's candidates while securing interdisciplinary funding. Key initiatives include: Co-designing the DISH project (Digital Innovations for Sustainable Heritage) with two funded PhD positions exploring digital twinning for museums Leading university-wide faculty development workshops on AI integration in teaching and research Serving on the advisory board for ERC-funded GAMEINDEX project in Prague Game Production Studies Her collaborative ecosystem spans heritage management, AI education, and game production studies through DISH and GAMEINDEX partnerships, emphasizing practical applications of digital technologies in creating equitable learning experiences and preserving cultural assets through interdisciplinary approaches.
استادیار
Anhong Guo is an Assistant Professor in Computer Science & Engineering at the University of Michigan , with a dual affiliation in the School of Information . His research focuses on the intersection of Human-Computer Interaction (HCI) and Artificial Intelligence (AI) , creating systems that empower people (especially those with disabilities) to design and customize technology for their unique needs. NSF CAREER award recipient Forbes 30 Under 30 Scientist Google Research Scholar Snap Inc. Research Fellow His work has been recognized with best paper awards at CHI, UIST, ASSETS, and MobileHCI, as well as the 10-year impact award at ISWC for wearable technologies in warehouse settings. Recent projects include HandProxy (expanding speech interfaces in XR), WorldScribe (context-aware visual descriptions), and A11yShape (3D modeling for blind programmers). Guo's research team has produced significant work in visual question answering , non-visual image editing , and augmented reality collaboration tools . His lab (Human-AI Lab, HAIL) focuses on addressing long-tail accessibility needs through human-AI co-creation.
Julian Togelius is Associate Professor of Computer Science and Engineering at NYU Tandon School of Engineering, where he co-directs the Game Innovation Lab. His research focuses on artificial intelligence for games, including procedural content generation, player modeling, and believable agent behavior using evolutionary computation and neural networks. Research domains include: AI-driven game design automation Evolutionary content generation General video game AI Large language models for game creation Recent innovations include GAVEL (game generation via evolution/LLMs), PCGRL+ (scalable RL level generators), and Neural MMO competitions. He established benchmark frameworks for procedural content generation and organizes international AI competitions. As Editor-in-Chief of IEEE Transactions on Games, he oversees academic publishing in game AI. His research received coverage in WIRED, IEEE Spectrum, and New Scientist. He teaches courses on AI and game development.
Dimitris Metaxas is a Professor in the Department of Computer Science at Rutgers University, specializing in Artificial Intelligence, Computer Vision, and Medical Imaging. His work bridges cutting-edge AI techniques with critical applications in healthcare and urban mobility. He leads research initiatives such as the Center for Accelerated and Real Time Analytics (CARTA) and has pioneered advancements in cardiac MRI reconstruction, diffusion models, and federated learning for medical data. Education: Not explicitly detailed in text, but his academic role implies doctoral training in Computer Science/Engineering. His research interests focus on medical AI applications, including automated diagnosis via imaging, generative models for healthcare data anonymization, and real-time analytics for smart cities. Notable contributions include foundational work on diffusion models (e.g., DiMSUM, SODA), medical dataset development (MedForge), and ethical AI integration in healthcare. Recent publications highlight trends in multimodal learning (e.g., sign language recognition, text-to-image generation), federated learning for medical privacy, and urban safety innovations like bicycle lane impact analysis. Awards: MICCAI Society Fellow (202?), IEEE Fellow, NIH/NASA/NSF grants totaling millions. He advises PhD students like Bingyu Xin (first place MICCAI 2024) and Meng Ye, while leading multi-institutional grants for projects like smart city micromobility systems with the Bloustein School. His lab develops tools like the CMRxRecon challenge platform for cardiac imaging and the SnapGen-V video synthesis system. Labs/Teams: CARTA Center, Medical Imaging AI Lab, Rutgers-Industry Collaboration for Urban Mobility Solutions.
Vicky Kalogeiton is a Professor in AI at École Polytechnique's Computer Science Laboratory (LIX) and leads the VISTA team. As an ELLIS member, her research focuses on multimodal generative AI with applications in medical imaging, efficient generation, and structured output modeling. She actively publishes in top venues like CVPR, ICCV, and IJCV, and supports Slow/ Open Science principles. PhD from University of Edinburgh and INRIA Grenoble Habilitation (HDR) from École Polytechnique 2024 Hi!Paris Chaire and multiple grants (ANR, Microsoft, DIM RFSI) Her recent work explores diffusion models for visual geolocation (Around the World in 80 Timesteps), camera motion control (AKiRa & E.T. dataset), and multimodal humor detection (FunnyNet-W). She pioneered coherence-aware training frameworks and cinematic trajectory analysis methods. Scientific recognition includes CVPR 2024 Highlight paper ACCV 2022 Student Honorable Mention ICCV-W 2021 Best Paper Award Outstanding Reviewer Awards (CVPR, ICCV, ECCV) She supervises current PhD candidates and has mentored numerous students across institutions like MBZUAI, Inria, and Telecom Paris. Her teaching includes Advanced Deep Learning and Computer Vision courses at École Polytechnique.
پژوهشگر
Jonas Auda is an active researcher at University of Duisburg-Essen specializing in virtual reality, cross-reality systems, and human-computer interaction. His work focuses on novel interaction techniques, haptic feedback systems, and bridging physical and virtual environments. He has published consistently since 2014, with a significant increase in output following his 2023 PhD completion. Dr. Auda's research interests center around enhancing user experience in virtual environments through innovative interaction methods. His work spans cross-reality systems that bridge physical and virtual worlds, haptic feedback mechanisms including drone-based interfaces, and novel approaches to 3D interaction and visualization. He has made significant contributions to understanding how users navigate and interact within virtual spaces, particularly through his work on hand displacement techniques and haptic props. His publication record shows a clear trend toward increasingly sophisticated cross-reality systems, with recent work focusing on generative AI applications in virtual environments, human-in-the-loop robot training, and comparative studies of different interaction modalities. The research demonstrates strong methodological rigor with careful comparative evaluations of different techniques across multiple platforms. Dr. Auda has received recognition through publications in top venues including CHI, MobileHCI, and ACM Computing Surveys, indicating his work is well-regarded in the human-computer interaction community. His collaborative pattern shows strong ties with researchers at University of Duisburg-Essen, particularly Stefan Schneegass and Uwe Gruenefeld. His research has practical implications for remote collaboration, education technology, and advanced user interfaces for virtual environments. Current work suggests continued exploration of AI integration with virtual reality systems and refinement of cross-reality interaction paradigms.
Deng Cai is a Professor at Zhejiang University's College of Computer Science, working in the State Key Laboratory of CAD&CG in Hangzhou, China. He also maintains an affiliation with Tencent AI Lab, demonstrating his strong connection between academic research and industry applications in artificial intelligence. His academic background includes a PhD from the University of Illinois at Urbana-Champaign, Department of Computer Science (2009). Professor Cai's research spans multiple domains within artificial intelligence, with particular emphasis on computer vision, deep learning, and their applications. His work shows strong focus on 3D object detection, lane detection for autonomous vehicles, and the application of large language models to various vision tasks. He has made significant contributions to traffic forecasting, trajectory prediction, and CAD generation systems. His recent work increasingly integrates large language models with computer vision tasks, demonstrating the evolving nature of his research interests toward multimodal AI systems. The trajectory of Professor Cai's publications reveals a clear progression from foundational computer vision and machine learning research toward increasingly complex and applied systems. His work shows strong emphasis on practical applications in autonomous driving, with numerous papers on 3D object detection, lane detection, and trajectory prediction. More recently, his research has expanded to include generative models for CAD systems and video customization, often leveraging large language models in innovative ways. The consistent publication output across top-tier venues including CVPR, ICCV, AAAI, and NeurIPS demonstrates sustained research productivity and impact. Professor Cai has established significant research collaborations, particularly with Xiaofei He (161 joint publications), Haifeng Liu (50), Zhou Zhao (42), Wenxiao Wang (41), and Binbin Lin (39). His work appears across diverse publication venues including IEEE Transactions on Pattern Analysis and Machine Intelligence, IEEE Transactions on Image Processing, and proceedings of major AI conferences. The breadth of his publication venues reflects the interdisciplinary nature of his research spanning theoretical machine learning to applied computer vision systems. Professor Cai leads research activities within Zhejiang University's College of Computer Science, particularly focusing on the State Key Laboratory of CAD&CG. His work bridges academic research with practical industry applications through his affiliation with Tencent AI Lab. The laboratory environment supports research in computer vision, machine learning, and their applications to real-world problems in autonomous systems, content generation, and intelligent transportation.
Niloy J. Mitra is a Professor of Geometry Processing at the Department of Computer Science, University College London (UCL). He holds a MS and PhD in Electrical Engineering from Stanford University, with postdoctoral research at Technical University Vienna. His research focuses on shape analysis, geometry processing, computational fabrication, and neural rendering. Notable contributions include work on structure-aware 3D representations and data-driven methods for scene understanding. Mitra has received prestigious awards such as the ACM Siggraph Significant New Researcher Award (2013) and the Eurographics Outstanding Technical Contributions Award (2019). He leads the SmartGeometryProcessing research group and has authored over 150 papers in top venues like SIGGRAPH and CVPR. His service roles include chairing conferences like Symposium on Geometry Processing and serving on editorial boards for journals like ACM Transactions on Graphics. Mitra's work bridges computer graphics, machine learning, and geometry, with applications in 3D modeling, animation, and generative AI. Education: PhD in Electrical Engineering (Stanford University, 2006), MS in Electrical Engineering (Stanford University, 2003), B.Tech in Computer Science & Engineering (IIT Kharagpur, 1999). Research interests include neural rendering, diffusion models for 3D generation, and procedural modeling. His lab develops tools like ShapeLib and Diff3F for 3D content creation. Awards also include the ERC Starting Grant (2013) and Eurographics Fellowship (2021). Mitra's hobbies include rock climbing, cooking, and reading, with a focus on technical and fiction literature.
Foaad Khosmood is a Professor of Computer Science and Forbes Professor of Computer Engineering at California Polytechnic State University. As Research Director of the Institute for Advanced Technology and Public Policy (IATPP), he leads projects focusing on digital government transparency, AI applications in legislative analysis, and data science for historical research. His work spans NLP/AI, game design, and systems engineering with notable contributions to computational humor analysis, legislative data tools like Digital Democracy, and game jam methodologies documented in his Springer book Game Jams—History, Technology, and Organisation (2023). Education: PhD in Computational Linguistics from UC Santa Cruz (2011). Research interests include artificial intelligence, digital humanities, and applying NLP to policy analysis. Collaborations involve institutions like the University of Miami, Graz University of Technology, and Imperial College London. He actively develops tools for state legislative transparency, historical data recovery (e.g., African Californios project), and AI-driven journalism support systems. His recent publications emphasize explainable AI for humor analysis, legislative stance detection systems, and game-based learning frameworks. Projects like AI4Reporters and Central Coast Data Science Partnership highlight his commitment to bridging technology with civic engagement. He regularly contributes to conferences like DH, ACL, and FDG, advancing interdisciplinary applications of computational methods.
Dr. Gordon Parker is a Professor in the Mechanical Engineering - Engineering Mechanics Department at Michigan Technological University. He teaches and researches dynamic systems and controls, utilizing MATLAB, Simulink, and MuPAD for educational content creation. His research focuses on renewable energy systems, particularly wave energy converter (WEC) technologies. Research Focus: Develops advanced control strategies for wave energy conversion, including nonlinear model predictive control and hydrodynamic modeling. Recent work examines array interactions, device optimization, and experimental validation of WEC systems. Technical Contributions: Designs low-friction testbeds for WEC control validation and investigates polyphase power solutions for marine energy grids. His work bridges theoretical control systems and renewable energy applications.
دانشیار
Elif Surer is an Associate Professor and Associate Director at Middle East Technical University's Graduate School of Informatics, Department of Multimedia Informatics. She leads the Entertainment Computing and Interactive Systems Laboratory (ECISLab) and actively contributes to METU's entrepreneurship initiatives through GIMER (Entrepreneurship Research Center). Her academic leadership extends to international projects including EU Horizon Europe initiatives and collaborations with institutions like Stanford University's University Innovation Fellows program. Dr. Surer's research focuses on the intersection of game technologies, extended reality, and practical applications across diverse domains. Her work spans serious games for CBRNe (Chemical, Biological, Radiological, Nuclear, and Explosive) training, virtual reality applications for mining safety, archaeological visualization, and educational technology. She has developed frameworks for heterotopias as discursive playgrounds, modular serious game development for virtual laboratories, and advanced techniques in persona building for game design. Her interdisciplinary approach bridges computer science with fields like archaeology, healthcare, and occupational safety, creating innovative solutions that address real-world challenges through immersive technologies. Her publication record demonstrates consistent contributions to top journals including IEEE Transactions on Games, Virtual Reality, and JMIR Serious Games. Recent work shows increasing focus on biological network visualization in XR environments, advanced testing frameworks for deep learning systems, and sophisticated persona modeling techniques that go beyond traditional approaches. Her research trajectory reflects growing integration of AI with immersive technologies, particularly in specialized training applications and scientific visualization. METU Performance Awards (2020, 2021) Best Poster award at METU Graduate School of Informatics Open Research Day TÜBİTAK Above-Threshold-Awards Featured in Researchers Active in Technical Games Research list Dr. Surer actively supervises numerous graduate students across multiple projects, with recent completions including theses on clickbait detection, VR for mining safety, and adaptive serious games for children with learning difficulties. She leads significant research funding including EU Horizon Europe projects like eNOTICE-2 (focused on CBRNe training), the MR4MS project for mine safety, and the Quantum Flagship project QuTE4E. Her laboratory environment fosters collaboration between computer science, architecture, and other disciplines, creating opportunities for students to engage in cutting-edge research with practical applications. As director of ECISLab, she maintains strong connections with industry partners including METU Teknokent, and actively participates in entrepreneurship initiatives through METU GIMER. Her work with the University Innovation Fellows program demonstrates commitment to developing entrepreneurial skills among students, while her research on virtual reality applications continues to expand into new domains including healthcare, archaeology, and emergency response training.
Dr. Deblina Bhattacharjee is a Lecturer in the Department of Computer Science at the University of Bath. Her research focuses on integrating Computer Vision with Arts and Cultural Heritage, emphasizing generative models, 3D reconstruction, and ethical AI applications. She holds a PhD in Computer Science from the Swiss Federal Institute of Technology (EPFL) and a Master's from Kyungpook National University. She actively organizes initiatives like the Women in Computer Vision Workshop at CVPR and chairs the Department’s Self-Assessment Team (DSAT), promoting diversity in STEM. Her teaching spans Visual Computing, AI Ethics, and Machine Learning, prioritizing inclusive education and student collaboration on AI projects. Research interests include depth estimation in comics, visual saliency, and multitask learning for cultural heritage preservation. Recent work includes the AI4VA dataset for comics analysis and CoDA for domain adaptation. She has mentored over 50 students across doctoral, master’s, and bachelor’s programs globally. Her contributions to UN SDGs emphasize education and responsible AI development. Education: PhD (EPFL, 2023), MSc (Kyungpook National University, 2017) External Roles: Postdoctoral Scientist (EPFL, 2023–2024), Google Research Intern (2019), Samsung Research Engineer (2017–2019)
استادیار
Roni Sengupta is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill, where she leads the Spatial & Physical Intelligence (SPIN) Lab. Her academic journey includes a Ph.D. from the University of Maryland (2019), a postdoctoral position at the University of Washington (2019-2022), and an undergraduate degree in Electronics and Tele-Communication Engineering from Jadavpur University in India. Her research lies at the intersection of Computer Vision and Computer Graphics, with four primary themes: Inverse Rendering : Recovering physical scene properties like geometry, material reflectance, and lighting 3D Perception from Endoscopy : Monocular depth estimation and SLAM for medical imaging applications Inverse Physics : Recovering 3D geometry and physical properties from sparse video inputs Generative Facial Editing : Developing personalized, training-free methods for facial attribute manipulation Her work has practical applications in visual content creation, telepresence, AR/VR, robotics, and healthcare, with several technologies adopted by companies including Microsoft. Dr. Sengupta's publication record shows a clear trajectory toward increasingly sophisticated inverse problem solving, with recent work (2023-2025) focusing on neural approaches to inverse rendering, medical imaging applications, and personalized generative models. Her research demonstrates strong interdisciplinary connections between computer vision, graphics, and medical applications. Her scientific recognition includes: NIH NIBIB Trailblazer Award for New and Early Stage Investigators (2024) UNC Junior Faculty Development Award (2024) UNC CS Student Association Excellence in Teaching Award (2023) CVPR Best Student Paper Honorable Mentions (2021) Dr. Sengupta actively mentors a diverse team of researchers, including 5 current PhD students, 3 MS students, and several undergraduates. Her former students have gone on to positions at Google, Kitware, Databricks, and Capitol One. She has secured significant research funding, with recent work supported by NIH and industry partners. Her teaching portfolio includes undergraduate and graduate courses in computer vision, 3D generative models, and neural rendering. The SPIN Lab maintains strong industry connections, with research collaborations and technology adoption by Microsoft, NVIDIA Research, and Snapchat Research. The lab's work on background matting has been particularly influential, receiving recognition at CVPR 2021 and being adopted by multiple companies.
Full Professor of Computer Science at the University of Modena and Reggio Emilia. Previously held faculty positions at Sapienza Università di Roma (Associate and Full Professor), Dartmouth College (Assistant and Associate Professor), and Cornell University (Visiting Assistant Professor). Also worked in the R&D division of Pixar Animation Studios. Dr. Pellacini received an MS and Ph.D. in Computer Science from Cornell University and a Laurea degree in Physics from the University of Parma. His research focuses on using Computer Graphics methods to solve design problems, with emphasis on the design, creative and entertainment industries. His group combines algorithms and efficient systems to allow professional designers as well as novices to create 3D scenes with significantly less effort. They investigate algorithms based on numerical methods and machine learning to enable intuitive and interactive editing of complex environments. Key research areas include appearance design (patterns, materials, and lights), appearance fabrication, evaluation and visualization of designers' workflows, and cloud-based collaborative design. His recent publications demonstrate a strong focus on procedural modeling, appearance design, and collaborative 3D content creation. His work bridges the gap between theoretical computer graphics research and practical applications for designers and artists, with significant contributions to node-based editing systems, boolean operations on surfaces, and differentiable rendering techniques for appearance design. NSF CAREER Award Alfred P. Sloan Fellowship Nominated as Junior Faculty Fellow for Sapienza's Scuola Superiore di Studi Avanzi Dr. Pellacini has supervised numerous students and has been actively involved in research grants related to computer graphics and design systems. He teaches Computer Graphics, Introduction to Scientific Python, and Programming courses at the University of Modena and Reggio Emilia, continuing a long teaching career that spans multiple institutions including Sapienza University of Rome and Dartmouth College. He leads a research group focused on developing innovative tools for 3D design and content creation, with applications spanning from entertainment to industrial design. His work on collaborative design systems, appearance editing interfaces, and procedural modeling techniques continues to shape how designers interact with 3D content creation tools.
