Olga Sorkine-Hornung is a Professor of Computer Science at ETH Zurich and head of the Institute of Visual Computing. She leads the Interactive Geometry Lab, focusing on theoretical and practical advancements in digital content creation, geometry processing, and shape modeling. Current position: ETH Zurich, Department of Computer Science Previous roles: Courant Institute (NYU), Technical University of Berlin Education: BSc and PhD from Tel Aviv University, postdoc at TU Berlin Her research spans shape representation, digital fabrication, computer animation, and fundamental geometry processing. Key contributions include Laplacian surface editing, as-rigid-as-possible deformation, and generalized winding numbers. She works on applications in VR, AR, and autonomous systems. Awards include Test of Time Awards (2024), ACM Fellow (2020), ERC Consolidator Grant (2020), and EUROGRAPHICS Young Researcher Award (2008). She has supervised numerous students and co-developed software libraries like libigl and Instant Meshes . Co-chair roles for SIGGRAPH, Eurographics, and Pacific Graphics Editorial board member for ACM Transactions on Graphics and other journals Keynote speaker at VMV, CVPR, and SIAM conferences Her work bridges mathematical rigor with practical implementation, advancing computer graphics and geometry processing through intuitive algorithms that maintain surface detail while enabling efficient computation.
Alexander Schwing is an Associate Professor in the Department of Electrical and Computer Engineering and Computer Science at the University of Illinois at Urbana-Champaign, affiliated with the Coordinated Science Laboratory. His research focuses on machine learning and computer vision with applications in 3D scene understanding, generative modeling, and multi-agent systems. Education: Diploma in Electrical Engineering and Information Technology, Technical University of Munich (TUM) PhD in Computer Science, ETH Zurich Postdoctoral Fellow, University of Toronto Research Interests: Structured prediction in deep learning Generative adversarial networks and stability Multi-modal vision-language models 3D scene reconstruction from single images Embodied agent collaboration Semantic segmentation with temporal coherence Recent Publications: Highlight trends in neural rendering, video object segmentation, and reinforcement learning with applications to 3D modeling and multi-agent systems. Notable innovations include SAIL-VOS dataset for amodal segmentation and NeRFDeformer for single-view scene transformation. Scientific Awards: NSF CAREER Award, 3M and Amazon research awards, multiple student recognition awards, ETH Zurich PhD medal, and best paper at Intelligent Tutoring Systems 2014. Teaching: Offers graduate courses in Pattern Recognition (ECE 544) and Machine Learning (CS 446/ECE 449). Previously taught at University of Toronto and ETH Zurich. Labs & Collaborations: Leads research at Coordinated Science Laboratory (UIUC) with collaborations across University of Toronto, ETH Zurich, and industry partners like Samsung SAIT and Amazon.
Mathieu Salzmann is a Senior Scientist and Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Computer Vision Laboratory (CVLAB) in the School of Computer and Communication Sciences (IC). He also holds a courtesy appointment with the EPFL College of Humanities and serves as Deputy Chief Data Scientist at the Swiss Data Science Center (SDSC). He has held concurrent roles in teaching units including SIN, SODH, and SSC, reflecting his interdisciplinary engagement. His research focuses on the intersection of machine learning and computer vision, particularly in deep learning for 2D and 3D visual scene understanding, efficient and robust models, domain adaptation, and interpretable AI. These interests are evident across his extensive publication record in top-tier venues. His recent publications (2023–2024) show a consistent trend in advancing deep learning methods for visual recognition, with strong representation at CVPR, ICCV, ECCV, ICML, ICLR, and NeurIPS. Topics include domain generalization, 3D understanding, model robustness, and multimodal learning, often with applications in real-world systems. His editorial roles as Associate Editor for IEEE TPAMI and Action Editor for TMLR further highlight his leadership in the field. Area Chair: ICML 2023, CVPR 2023, ICCV 2023, NeurIPS 2023, AAAI 2024, ECCV 2024 Associate Editor: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI) Action Editor: Transactions on Machine Learning Research (TMLR) Mathieu Salzmann has supervised numerous PhD students at EPFL, both current and past, including Bouquet Yann Yanis, Javed Saqib, Li Shuangqi, and others. He has also been involved in research grants and collaborative projects, such as his work with S. Süsstrunk and R. Baroni on comics reconfiguration. His part-time role as Senior GNC Engineer at ClearSpace (2020–2024) illustrates his applied research engagement in aerospace systems. He is actively involved in EPFL’s data science and AI research ecosystem through SDSC and multiple labs.
Professor Carlo Harvey is a creative technologist at the School of Digital Arts (SODA), Manchester Metropolitan University. His interdisciplinary research merges games , machine learning , virtual production , and cultural heritage reinterpretation . He leads industry collaborations with entities like Jaguar Land Rover and Epic Games, focusing on AI-driven interactive audio, real-time visualization, and accessibility solutions. Award-winning projects : TIGA, Innovate UK, and Epic Games MegaGrant for Accession Industry partnerships : Automotive sector, cultural institutions His research spans human-computer interaction , multisensory virtual environments , and acoustic-visual cross-modal perception . Recent publications address robotic simulations, motion alignment, and haptic feedback systems. Scientific recognition : TIGA Award, Innovate UK Funding, Epic Games MegaGrant Advocacy : Digital inclusion, creative collaboration, social impact of technology
David Lindlbauer is an Assistant Professor at the Human-Computer Interaction Institute (HCII) of Carnegie Mellon University, where he leads the Augmented Perception Lab and co-directs the CMU Extended Reality Technology Center. His research bridges human perception, extended reality (AR/VR), and computational interaction techniques, focusing on developing systems that dynamically adapt interface elements based on environmental context, user cognition, and task requirements. He completed his PhD at TU Berlin under Prof. Marc Alexa and held a postdoctoral position at ETH Zurich's Advanced Interactive Technologies lab. His work has been published extensively at top venues including ACM CHI, UIST, and IEEE VR, with research themes spanning gaze tracking, spatial audio optimization, haptic feedback, and multimodal notification systems. Media outlets like MIT Technology Review and Fast Company Design have featured his innovations. Dr. Lindlbauer has received prestigious grants from Meta, NSF, and ETH Zurich, and serves on program committees for CHI, UIST, and ISMAR. He has been recognized with Best Paper awards at ISS 2023 and CHI 2016, and his lab develops tools like MineXR for personalized XR interfaces and RealityReplay for temporal change visualization in mixed reality environments.
Professor Gabriel Brostow is a faculty member in the Department of Computer Science at University College London (UCL), where he leads research in Computer Vision and Human-Computer Interaction. He also serves as Chief Research Scientist and Senior Director of the R&D Team at Niantic, the company behind Pokémon GO. His work bridges academic research and industry applications, focusing on developing AI systems that enhance human capabilities through what he terms 'Human in the Loop AI'—now commonly referred to as Human-Centered AI. Brostow completed his BS in Electrical Engineering at UT Austin, followed by a PhD with Irfan Essa at Georgia Tech. He then pursued postdoctoral research with Roberto Cipolla's Computer Vision & Robotics Group at Cambridge University as a Marshall Sherfield Fellow, and with Marc Pollefeys in ETH Zurich's CVG Group. His research explores how AI, particularly Computer Vision, can serve as 'super-tools' for professionals across various domains including filmmaking, architecture, robotics, and scientific research. Specific interests include assistive technology for everyday life, authoring systems that maximize user effort, 3D reconstruction, depth estimation, and vision-language models. His work often involves creating systems that are validated through real-world human interaction to ensure practical utility. Analysis of his recent publications reveals a strong focus on practical applications of Computer Vision that directly interact with humans. His research spans 3D scene understanding, depth estimation, sketch-based interfaces, and multimodal AI systems. There's a clear emphasis on creating benchmarks and tools that facilitate human-AI collaboration, with applications in assistive technology, urban planning, filmmaking, and biodiversity monitoring. His work frequently appears at top conferences including CVPR, NeurIPS, ECCV, and CHI. Marshall Sherfield Fellowship Brostow actively mentors PhD students, with current advisees including Ross Murphy, Skanda Koppula, Gizem Unlu, Omiros Pantazis, and Jamie Watson. His alumni include numerous PhD graduates and MSc students who have gone on to successful careers in academia and industry. He emphasizes selecting students based on passion and potential rather than just academic credentials, valuing traits like helpfulness, drive, and hunger to learn. His research is supported through collaborations with major institutions and companies including DeepMind, MIT, and the University of Edinburgh. He leads a research group at UCL that collaborates closely with Niantic's R&D team, creating a unique bridge between academic research and industry application. His team's work frequently involves developing novel Computer Vision techniques that are validated through real-world human interaction, ensuring practical utility alongside technical innovation. The group explores blue-sky research problems with applications ranging from assistive technology to professional tools for filmmakers, architects, and scientists studying diverse environments.
Matthew B. Blaschko is a Professor in the Department of Electrical Engineering at KU Leuven, Belgium. He serves as director of the KU Leuven ELLIS unit and is a fellow in the ELLIS Health program. He is a Core PI in the Flanders AI Research Program, working as a workpackage lead for Decision Support Systems and Medical Imaging. Blaschko is also a member of the KU Leuven Institute for Artificial Intelligence and one of the leaders of the working group on Machine Learning and Data Science. Professor Blaschko received his B.S. from Columbia University, M.S. from the University of Massachusetts Amherst, and Dr. rer. nat. from Technische Universität Berlin (awarded for work at Max Planck Institutes Tübingen). He was a Newton International Fellow at the University of Oxford and received his Habilitation (HDR) from École Normale Supérieure de Cachan. Prior to joining KU Leuven, he was a Permanent Research Scientist in the INRIA Saclay Research Center and a Faculty Member at Ecole Centrale Paris. His research focuses on machine learning techniques applied to visual data, with particular emphasis on calibration in deep learning, medical image analysis, and federated learning. Blaschko's work bridges theoretical foundations with practical applications, as evidenced by technology developed in his research being incorporated into MONA, software for ophthalmic image analysis. His research group has made significant contributions to the fields of model calibration, uncertainty estimation, and medical imaging analysis, with recent publications showing strong trends toward improving reliability of AI systems in medical contexts and advancing theoretical understanding of calibration metrics. Professor Blaschko has been recognized with several awards including the Université Paris-Saclay STIC Doctoral School Best Scientific Contribution Award, Best Paper Award at CVPR 2008, Main Award at DAGM 2008, and Best Student Paper Award at ECCV 2008. Professor Blaschko has supervised numerous PhD and Master's students, with current and former students including Deniz Soysal, Claire Marchal, Dongli Xu, Sebastian Gruber, Jiameng Li, Marco Mezzina, and many others working on diverse topics from Alzheimer's disease analysis to surgical phase recognition. His research has been supported by various funding sources including the Flanders AI Research Program. He has co-organized several influential workshops including the "Another Brick in the AI Wall: Building Practical Solutions from Theoretical Foundations" at CVPR 2025, Commands 4 Autonomous Vehicles workshop at ECCV 2020, and the Learning from Limited Labeled Data workshop series at NIPS 2017 and ICLR 2019. His laboratory focuses on machine learning for medical image analysis, with applications in ophthalmology, neurology, and surgical robotics. The group maintains active collaborations with medical institutions and participates in international challenges such as the KNee OsteoArthritis Prediction (KNOAP2020) challenge.
Jiajun Wu is an Assistant Professor of Computer Science and, by courtesy, of Psychology at Stanford University. He holds multiple affiliations including membership in Bio-X, Faculty Affiliate status at the Institute for Human-Centered Artificial Intelligence (HAI), and membership in both the Wu Tsai Human Performance Alliance and Wu Tsai Neurosciences Institute. Dr. Wu earned his Ph.D. and S.M. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology before joining Stanford. Dr. Wu's research program focuses on creating AI systems that understand and interact with the physical world through the integration of computer vision, machine learning, robotics, and cognitive science. His work emphasizes physics-based modeling combined with deep learning to develop systems capable of perceiving, reasoning about, and predicting physical interactions. Key research areas include 3D scene understanding, neurosymbolic AI approaches, multimodal perception (combining vision, sound, and language), and embodied intelligence for robotics applications. His lab develops novel frameworks that bridge the gap between neural networks and symbolic reasoning to create more interpretable and robust AI systems. Analysis of Dr. Wu's recent publications reveals a strong trajectory toward integrated multimodal understanding for embodied AI. His work increasingly combines vision, sound, and language processing with physical reasoning to create systems that can interact meaningfully with the physical world. There's a clear progression from foundational computer vision research toward practical robotics applications, with significant emphasis on foundation models for robotics, sim2real transfer techniques, and creating comprehensive datasets for embodied AI research. Dr. Wu's exceptional contributions have been recognized with numerous prestigious awards including the NSF CAREER award (2024), Young Investigator Programs from ONR (2024) and AFOSR (2023), the Okawa research grant (2024), and being named to IEEE Intelligent Systems' 'AI's 10 to Watch' (2024). He has received multiple best paper awards at leading conferences including ICRA (2024), SIGGRAPH Asia (2023), and CoRL (2023). Dr. Wu actively mentors a large cohort of students across multiple levels, serving as primary advisor for doctoral candidates, master's students, and numerous independent researchers. His research is supported by substantial funding from major technology companies including Google, Meta, Amazon, Samsung, and J.P. Morgan, as well as government agencies like NSF, ONR, and AFOSR, reflecting the significance and impact of his work in physical AI and multimodal perception systems. Dr. Wu leads a dynamic research group at Stanford that collaborates extensively with the Wu Tsai Neurosciences Institute and Institute for Human-Centered AI. Current projects include developing neurosymbolic models for computer graphics, creating multisensory datasets like OBJECTFOLDER 2.0 for sim2real transfer in robotics, and building foundation models for embodied intelligence that can understand and manipulate objects with human-like physical intuition.
Ruohan Gao is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park , with affiliate appointments at the University of Maryland Institute for Advanced Computer Studies (UMIACS) , Maryland Robotics Center (MRC) , and Artificial Intelligence Interdisciplinary Institute at Maryland (AIM) . His research focuses on Computer Vision and Machine Learning , emphasizing Multisensory Machine Intelligence that integrates sight, sound, and touch . He aims to enable machines to perceive, understand, and interact with the world as humans do, with applications in robotic manipulation , audio-visual localization , and differentiable rendering . Article Trends : Span 2018–2025 , centering on audio-visual perception , multisensory datasets , and robotics . Recurring themes include object-centric learning , sound synthesis , and cross-modal consistency . Scientific Awards : Michael H. Granof Award (UT Austin’s Top 1 Doctoral Dissertation, 2021) Best Paper Award Runner-Up (BMVC 2021) Best Paper Award Finalist (CVPR 2019) Highlight Paper (CVPR 2023) He leads the UMD Multisensory Machine Intelligence Lab and collaborates with institutions like Stanford and The University of Texas at Austin . Contact: rhgao@umd.edu .
Wesley Willett is an Associate Professor in the Department of Computer Science at the University of Calgary, holding the NSERC CRC II Chair in Visual Analytics. His primary research focuses on information visualization, human-computer interaction, and new media applications. He leads the Data Experience Lab and Interactions Lab, exploring innovative methods for data representation and interaction in augmented/virtual reality environments. Education includes a B.S. in Computer Science from the University of Colorado (2006) and a Ph.D. in Computer Science from UC Berkeley (2012). His work bridges technical innovation with user-centered design principles, emphasizing ethical considerations in data visualization and inclusive representation. Key research contributions include: spatial visualization techniques for large environments, gesture-based interfaces for AR/VR, and physical data representations through projects like Cetonia (swarm robotics visualization) and Data Embroidery. His work has been recognized with Best Paper awards at CHI 2015 and Pervasive 2010. Current research emphasizes immersive analytics, wearable visualization systems, and demographically diverse anthropographics. He collaborates with urban designers, neurologists, and environmental scientists to apply visualization in diverse domains like epilepsy surgery planning and air quality monitoring.
Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at University of Maryland, College Park, with a joint appointment in the Institute of Advanced Computer Studies (UMIACS). Previously, he served as an Assistant Professor at the same institution from August 2018 to June 2024, and spent one year as a Visiting Research Scientist at Google Research from September 2017 to August 2018. His educational background includes: PhD in Robotics and Artificial Intelligence from Carnegie Mellon University (2017), advised by Abhinav Gupta, with thesis titled 'Discovering and Leveraging Visual Structure for Large-scale Recognition' MS in Artificial Intelligence from Carnegie Mellon University (2011), supervised by Alyosha Efros and Martial Hebert BTech in Computer Science and Engineering from Jaypee Institute of Information Technology (2010) Professor Shrivastava's research focuses on computer vision and machine learning, with particular expertise in object detection, image recognition, and neural representations. His work bridges theoretical advances with practical applications, exploring how visual systems can discover and leverage structure in large-scale recognition problems. He has made significant contributions to understanding the role of supervision in vision transformers, developing novel approaches for object-state composition recognition, and creating efficient neural representations for videos and 3D scenes. His research often addresses fundamental challenges in visual recognition, including handling novelty in open-world environments and improving the efficiency of visual systems. An analysis of his recent publications reveals a strong emphasis on neural representations, particularly for dynamic content like videos and 3D scenes. His work demonstrates increasing sophistication in handling open-world vision problems, with research spanning object discovery, localization, and representation learning. The publications show a clear progression toward more efficient and scalable models, with recent work focusing on model compression, sparse representations, and addressing the challenges of working with limited annotations. His scientific contributions have been recognized with several prestigious awards: Best Paper Award (Applications) at IEEE Winter Conference on Applications of Computer Vision (2020) Microsoft Research PhD Fellowship (2014-2016) Best Student Paper Award at IEEE Winter Conference on Applications of Computer Vision (2014) Outstanding Reviewer Award at IEEE CVPR (2015) Professor Shrivastava has successfully mentored numerous graduate students, many of whom have become prominent researchers in computer vision. His Amazon Research Awards (2020 and 2023) have supported innovative projects including 'The pursuit of knowledge: discovering and localizing new concepts using dual memory' and 'Audio-conditioned Diffusion Models for Generating Lip-synchronized Videos.' He has served as Area Chair for major conferences including ICCV, CVPR, and AAAI, demonstrating his leadership in the computer vision community. His research has attracted significant funding from both academic and industry sources, supporting his exploration of fundamental questions in visual recognition and representation learning.
Robert S. Allison is a Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. His research focuses on human perceptual responses in virtual environments, stereoscopic vision, and eye movement analysis. He is affiliated with the York Centre for Vision Research, Sensorium (Digital Arts & Technology), and the Centre for Innovation in Computing at Lassonde. His research interests include depth perception in natural and virtual environments, human-computer interface design for VR, machine vision applications, and the measurement of human motion. He has supervised multiple graduate students and contributed to over 260 publications. His work spans topics like cybersickness mitigation, display lag effects, and perceptual adaptation in VR. Key grants include NSERC-funded projects on perception in virtual environments and collaborations with institutions like the Australian Research Council. His teaching includes courses on human perception in human-computer interaction and digital logic design. Recent articles highlight advancements in understanding motion perception, VR-induced sickness, and multisensory integration. He collaborates widely, with affiliations including the VISTA program and York's Connected Minds initiative.
Yingying Wang is an Assistant Professor in the Computing and Software department at McMaster University , where she joined in January 2022. Her research focuses on generating expressive animations for AR/VR applications and games through interdisciplinary approaches combining Computer Graphics , Artificial Intelligence , and Human Behavior Analysis . Education : Bachelor and Master degrees from Nanjing University , Ph.D. from University of California, Davis (2017) Her research explores: Generative models for human motion style transfer Physics-based motion simulation Audio-driven character synthesis Dance choreography for virtual characters Cartoon animation perception Conversational character gesture synthesis Markerless hand motion capture Recent publications focus on 3D hand pose estimation , motion style transfer , gesture-locomotion coordination , and personality perception in virtual agents . Key methodologies include deep learning , multimodal data analysis , and real-time animation systems . Scientific contributions recognized through: $240,000 Labarge Catalyst Grant in Mobility in Aging (interdisciplinary team award) US Patent 10,796,482 (3D hand pose estimation) US Patent 9,811,937 (gesture-locomotion coordination) Teaching includes graduate and undergraduate courses in Computer Animation (CAS 737), Computer Graphics (COMPSCI 3GC3/SFWRENG 3GC3), and Software Development (COMPSCI 2ME3). Research group actively recruits Ph.D. and Master's students in graphics + deep learning domains.
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.
Sergey Tulyakov is the Director of Research at Snap Inc. , leading the Creative Vision team. His work focuses on enhancing creator capabilities through computer vision , machine learning , and generative AI , with applications in 2D/3D/4D video generation, editing, and personalization. He pioneered video generation frameworks like MoCoGAN and First Order Motion Model , and has been recognized for BEST IN SHOW AWARD at SIGGRAPH Real-Time Live! 2020. PhD (2012-2017): University of Trento, Italy MSc (2010): Belorusian State University of Informatics and Radioelectronics B.Eng (2009): Belorusian State University of Informatics and Radioelectronics His research interests span computer vision , generative models , 3D reconstruction , and personalization , with a focus on making large models efficient and mobile-compatible . Recent publications highlight advancements in 4D video generation , text-guided 3D composition , and lightweight architectures . Key scientific awards include the SIGGRAPH Real-Time Live! 2020 Best in Show for Interactive Video Stylization. He has also served on technical program committees for top-tier conferences like CVPR, ICCV, SIGGRAPH, and NeurIPS since 2022. His team organizes tutorials and keynotes, including courses on Deep Generative Models and Efficient Neural Networks . While no direct student names are listed, his collaborative work spans 60+ top-tier publications.
