Gül Varol is a permanent researcher at École des Ponts ParisTech's IMAGINE group, an ELLIS Scholar, and Guest Scientist at Max Planck Institute. She holds a PhD from Inria Paris/ENS with awards from ELLIS and AFRIF. Her academic service includes Program Chair at ECCV'24 and Area Chair roles at major conferences. Current affiliations: IMAGINE group (École des Ponts ParisTech), Max Planck Institute Previous roles: Postdoctoral researcher at University of Oxford Her research focuses on vision-language applications, particularly in 3D human motion synthesis, sign language technology, and audio description generation. Key techniques include text-conditioned diffusion models, temporal context modeling, and synthetic data utilization. Scientific contributions recognized through: Google Research Scholar award (2023) ELLIS PhD Award (2020) AFRIF PhD thesis award (2020) Best application paper at ACCV'20 Recent publications demonstrate expertise in: Text-driven 3D motion editing (MotionFix, 2024) Cross-dataset generalization studies (TMR++, 2024) Temporal action composition frameworks (TEACH, 2022) Sign language dense annotation methods (BOBSL, 2022) Zero-shot audio description generation (AutoAD-Zero, 2024) She actively contributes to dataset development including BOBSL (British Sign Language corpus) and SURREACT synthetic action dataset, while pioneering new evaluation metrics for audio description quality and motion retrieval benchmarks.
Kathleen C. Howell is the Hsu Lo Distinguished Professor of Aeronautics and Astronautics at Purdue University's School of Aeronautics and Astronautics. Her expertise spans orbit mechanics, spacecraft trajectory optimization, and mission design in multi-body systems. She holds degrees from Iowa State University (B.S., 1973), Stanford University (M.S., 1977; Ph.D., 1983). B.S. in Aerospace Engineering, Iowa State University, 1973 M.S. in Aeronautical & Astronautical Engineering, Stanford University, 1977 Ph.D. in Aeronautical & Astronautical Sciences, Stanford University, 1983 Her research focuses on libration point orbits, solar sail trajectories, and trajectory optimization in Earth-Moon and interplanetary systems. She has pioneered methods for analyzing Lissajous trajectories, invariant manifolds, and low-thrust mission design. Recent work includes solar sail applications for lunar coverage and ARTEMIS mission trajectory analysis. Publications highlight innovations in multi-body dynamics, with contributions to journals like Acta Astronautica , Journal of Guidance, Control, and Dynamics , and AIAA/AAS Conference Proceedings . Her work emphasizes practical mission design tools and visualization techniques. Awards: Fellow, AIAA (2013) W.A. Gustafson Teaching Award (2012) Dirk Brouwer Award (2004) Presidential Young Investigator Award (1984) Multiple Elmer F. Bruhn Teaching Awards Her advising and grants include leadership in space mission design, formation flight, and solar sail technology. She has collaborated on projects like the TRIANA mission and contributed to the Cassini end-of-mission analysis. Active in professional societies, she has edited conference proceedings and delivered invited lectures globally.
Iro Laina is a Departmental Lecturer in Computer Vision at the University of Oxford's Visual Geometry Group. She holds a PhD (Dr. rer. nat.) from the Technical University of Munich (TUM), where her dissertation earned the ECVA PhD Award. Her research focuses on unsupervised and language-supervised learning for 3D scene understanding, image/video perception systems, and geometric reconstruction. Education: PhD in Computer Science (TUM), MSc in Biomedical Computing (TUM), Diploma in Electrical & Computer Engineering (NTUA). Research Interests: 3D Reconstruction and Generation Unsupervised Learning Multi-View and Video Analysis Generative Diffusion Models Geometry-Aware Networks Her recent work emphasizes scalable 3D scene synthesis, training-free methods, and cross-modal fusion with LLMs. Over 15+ publications since 2021 reflect her leadership in geometric deep learning. Awards: ECVA PhD Award (2020), Recognized in multiple international conferences. Advising: Mentors DPhil students in creative AI applications (e.g., gameplay design). Active in Oxford's Robotics and Biomedical Engineering networks. Labs/Tech: Core member of the Visual Geometry Group, collaborating on projects like IMAD2025 with the ZERO Institute.
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.
Eliese-Sophia Lincke is a Junior Professor at the Department of History and Cultural Studies, Freie Universität Berlin, since May 2022. Her work bridges computational methods with Egyptology, focusing on digital tools for studying ancient texts. Bachelor's and Master's in Egyptology, Humboldt-Universität zu Berlin (2007) PhD in "The Conception of Spaces in Language" (TOPOI Cluster, 2012) Research interests include: Digital Humanities : Developing machine learning models for Hieroglyphic, Demotic, and Coptic text processing Linguistic Typology : Analyzing classifier systems in Ancient Egyptian and Sign Languages Spatial Linguistics : Investigating prepositions and spatial adverbs in Egyptian-Coptic Recent publications focus on Neural Lemmatization , OCR for Coptic , and Classifier Semantics , demonstrating her commitment to computational Egyptology. Scientific awards include the Humboldt-Preis 2008 for best Master's thesis and the Prize for Good Teaching 2014 . She has co-organized workshops like "Wege zum Ägyptischen" and served as Co-Editor for Lingua Aegyptia . Her teaching contributes to the Digital Studies of Ancient Texts Master's program.
Maneesh Agrawala is the Forest Baskett Professor of Computer Science at Stanford University and Director of the Brown Institute for Media Innovation. His research spans computer graphics, human-computer interaction, and information visualization. Agrawala's lab develops computational tools for visual communication, investigating how design principles improve media effectiveness. Current projects include diffusion models for image and video generation, sketch-based interfaces, and visualization tools for scriptwriting. His team creates systems that enable new forms of content creation and analysis. His publications demonstrate consistent innovation in visual computing, with recent advances in controllable generative models, video understanding, and visualization design. Agrawala has received numerous honors including the MacArthur Fellowship and ACM Fellowship for his contributions to visual computing.
Guido Pintacuda is a CNRS Research Director and Head of the Lyon High-Field NMR Center (CRMN) at École Normale Supérieure de Lyon since 2019. His work centers on advancing solid-state NMR methodologies with ultra-fast magic-angle spinning (MAS) to achieve atomic-level resolution in complex biomolecular and materials systems that are intractable to conventional techniques. Educational background: Undergraduate studies (1992-1997) and PhD in Sciences (1998-2002) at Scuola Normale Superiore in Pisa, Italy; postdoctoral research at Karolinska Institutet (2001-2004) and Australian National University (2004). Research interests focus on pushing NMR frontiers through high-field instrumentation and fast MAS (up to 160 kHz), with dual objectives: (i) biomolecular structure determination for membrane proteins, amyloid fibrils, and viral assemblies; (ii) solid-state NMR of paramagnetic materials like battery cathodes and catalysts. His innovations include proton detection in fully protonated proteins and DNP-enhanced sensitivity. Recent publications (2021-2024) show heavy emphasis on proton-detected NMR under fast MAS for structural biology, alongside growing work in paramagnetic materials. Key trends include method development for μs–ms dynamics, miniature rotor protocols for membrane proteins, and collaborations with Bruker for 150+ kHz probe technology. Scientific awards: ERC Consolidator Grant (P-MEM-MAS, 2015-2021) Sackler Prize (2017) ISMAR Fellow (2020) Mentoring and grants: Principal investigator for major projects including ERC (2.5 M€), ANR CTRbyNMR (384 k€), and EU PANACEA (5 M€, co-coordinator). Actively mentors PhD student Clément Ollier and postdocs (Z. Sun, S. Medina-Gomez) at ENS Lyon and international schools. Labs and teams: Directs CRMN (UMR 5082 CNRS/ENS Lyon/UCBL), a world-class NMR facility with unique high-field equipment. Leads a research group developing 150+ kHz MAS probes in partnership with Bruker Biospin and maintains strong ties to the University of Delaware (T. Polenova) and European networks.
Ohad Fried is an Associate Professor of Computer Science at Reichman University. He was previously a postdoctoral research scholar at Stanford University under Prof. Maneesh Agrawala and completed his PhD with Prof. Adam Finkelstein as part of the Princeton Graphics group. He holds an M.Sc. in Computer Science and a B.Sc. in Computational Biology from The Hebrew University. His research lies at the intersection of computer graphics, computer vision, and Generative AI , focusing on tools, algorithms, and paradigms for photo and video editing and synthesis . His work has been widely recognized in top conferences including CVPR, SIGGRAPH, and ECCV, with recent contributions to tiled diffusion models, expressive 4D facial motion generation, and synthetic image detection. Ohad has received numerous awards, including the Israel Science Foundation personal research grant (2021) , the Outstanding faculty researcher at Reichman University (2022) , and the Siebel Scholar award (2017) . He has advised multiple students in research projects, and his work is covered by media outlets like Wired , The Washington Post , and CNN . Teaching roles include courses at Reichman University such as "GenAI for Games & Entertainment" and "Synthetic Media Detection", and at Stanford University "Computational Video Manipulation". Key Research Themes: Neural Rendering Diffusion Models 3D Facial Animation Image/Video Editing Media Forensics Scientific Awards: ISF Personal Grant (2021) Siebel Scholar (2017) Google PhD Fellowship (2014-2016) Gordon Y.S. Wu Fellowship (2012-2013) Excellence Scholarships
Daniel J. Sorin is a Professor of Electrical and Computer Engineering at Duke University's Pratt School of Engineering, where he also serves as Associate Chair of Education. He holds joint appointments in both the Electrical and Computer Engineering department and Computer Science department, and is recognized as a Bass Fellow for his contributions to education and research. His research focuses on computer architecture with specific expertise in memory systems, cache coherence protocols, fault tolerance, and verification-aware design. Dr. Sorin's work bridges theoretical computer architecture with practical implementations, often incorporating coding theory to solve architectural challenges. His research group has made significant contributions to automated protocol generation, hardware acceleration, and robot motion planning systems. Dr. Sorin's publications reveal a consistent focus on memory consistency models, cache coherence protocols, and verification techniques. His recent work has expanded into robot motion planning acceleration, FPGA resource management, and novel error correction techniques for emerging memory technologies. The trend shows increasing interdisciplinary work connecting computer architecture with robotics and machine learning applications. Program Chair of HiPEAC 2017 Co-chair of IEEE Micro's Top Picks selection committee (2016) Lois and John L. Imhoff Distinguished Teaching Award (2011) NSF CAREER Award recipient IEEE Micro Top Pick awards (2011, 2015) ACM Senior Member As an advisor, Dr. Sorin has mentored numerous PhD students who have gone on to successful careers at leading technology companies including Google, Microsoft, Oracle, and Nvidia. His research group maintains strong industry connections and has produced influential work in cache coherence protocols, memory systems, and fault-tolerant architectures. He has also authored the widely-used textbook 'A Primer on Memory Consistency and Cache Coherence' (2nd edition). Dr. Sorin leads an active research laboratory focused on next-generation computer architecture challenges, with ongoing projects in hardware acceleration, memory systems, and robot motion planning. His group collaborates with researchers across multiple disciplines including robotics, coding theory, and semiconductor design.
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.
Dr. Mario Slugan serves as Senior Lecturer in Film Studies at Queen Mary University of London's School of Languages, Linguistics and Film. His research bridges film studies, philosophy, and German studies with emphasis on theoretical frameworks, historical analysis, and cross-disciplinary methodologies. Slugan's research focuses on film philosophy and film theory , particularly examining fiction/non-fiction distinctions, early cinema practices, and montage aesthetics. His work explores German cinema , Eastern European film traditions , and techniques of visual immersion from Hale's Tours to VR, demonstrating how philosophical aesthetics informs cinematic experience across historical periods. Analysis of his recent publications reveals consistent exploration of fictional frameworks in non-fiction contexts and historical epistemology of cinematic forms . His work shows increasing engagement with pandemic-era film genres while maintaining core interests in German and Eastern European cinemas, with empirical approaches complementing philosophical inquiry. His scholarly contributions have earned significant recognition: 2021 BAFTSS Best Monograph Award (Second Place) for 'Fiction and Imagination in Early Cinema' 2022 BAFTSS Best Edited Volume Award (Third Place) for 'The Fiction/Nonfiction Distinction' 2023 BAFTSS Best Edited Volume Award (Second Place) for 'New Perspectives on Early Cinema History' 2017 BAFTSS Best Journal Article Award (Third Place) Fellowship in the Society for Cognitive Studies of the Moving Image Slugan currently supervises five PhD candidates examining Hitchcock's legacy, post-war German cinema, intersemiotic translation, Iranian avant-garde, and immersive museum theory. As Co-Investigator on the Croatian Science Foundation's 'Aesthetic Education through Narrative Art' project (2021-2026), he explores intersections of narrative art, philosophy, and educational practice while securing continuous research funding through international collaborations.
Serge CARDINAL is a Full Professor at the Department of Art History, Cinema, and Audiovisual Media at Université de Montréal. His research focuses on the intersections of sound, music, and cinema, blending philosophical inquiry with creative practice. He leads projects exploring musicality in film, interdisciplinary analysis methods, and the legacy of cinema through installations and exhibitions. Education: The text does not explicitly detail his formal education, but his extensive academic output and professorial role suggest advanced degrees in cinema studies and related fields. Research Interests : Musicality in film, research-creation, Deleuzean philosophy applied to cinema, actor studies, and interdisciplinary audiovisual practices. His work bridges theory and practice, using soundscapes, installations, and performances to analyze cinematic material. Recent Projects : Includes installations like Tombeau de Gilles Groulx (2019) and The Political Glenn Gould (2024), as well as collaborative research networks like the OICRM. He has explored digital tools for cinematic analysis (Numalyse, 2025) and sound transcription in intermedia contexts (Studio Glenn Gould, 2021). Grants & Funding : Recipient of grants from FRQSC and SSHRC for projects on sound in Quebec cinema, research-creation methodologies, and interdisciplinary music-cinema studies. Leads teams in strategic research programs and individual creation grants. Labs/Teams : Member of the Observatoire interdisciplinaire de création et de recherche en musique (OICRM) and the research-creation lab La création sonore: cinéma, arts médiatiques, arts du son .
Professor Hossein Rahmani serves at the School of Computing and Communications , Lancaster University , with a focus on Computer Vision and Machine Learning . His career spans institutions like the University of Western Australia (PhD), Shahid Beheshti University (MSc), and Isfahan University of Technology (BSc). Research Interests : Computer Vision, Machine Learning, Video Analysis, Action Recognition/Detection, Object/Human Pose Estimation, 3D Reconstruction, Diffusion Models, Human-Object Interaction Editorial Roles : Associate Editor for IEEE Transactions on Neural Networks and Learning Systems , Pattern Recognition , ACM Computing Surveys ; Area Chair for CVPR 2025, ICLR 2025, ECCV 2024, IJCAI 2024 His recent work leverages diffusion models for domain-generalized object pose estimation, 3D scene editing, and human mesh recovery, published in top venues like TPAMI , CVPR , ICCV , and ECCV . He received the Best Scientific Paper Award from the International Conference on Pattern Recognition and actively supervises 5 PhD students with interdisciplinary projects in digital health and data science.
Serge Belongie is a Professor at the Department of Computer Science (DIKU) at the University of Copenhagen, where he holds dual affiliations with the Pioneer AI research section and the Image Analysis, Computational Modelling, and Geometry section. His academic position places him at the forefront of interdisciplinary research connecting computer vision with language models, geospatial analysis, and cultural understanding. Professor Belongie's research program encompasses several critical domains in modern artificial intelligence: Advanced computer vision and image analysis techniques Vision-language model integration and multimodal systems 3D point cloud processing and semantic segmentation Geospatial representation learning for environmental applications Fine-grained object recognition and detection Cultural context understanding in AI systems His recent publication record reveals a sophisticated trajectory toward developing precise control mechanisms for vision-language models, with applications spanning forensic analysis, cultural heritage preservation, and social media understanding. The research demonstrates increasing sophistication in handling cultural context and enabling fine-grained manipulation of visual content through natural language interfaces. Professor Belongie maintains an active research group producing significant scholarly output, with over 280 research publications documented in his academic profile. His work is supported by research funding that enables cutting-edge exploration in multimodal AI systems with practical societal impact. He plays a key role in the Pioneer AI center at the University of Copenhagen, which focuses on advancing artificial intelligence through interdisciplinary collaboration and innovative research approaches that bridge theoretical computer science with real-world applications.
Ryo Suzuki is an Assistant Professor at the ATLAS Institute within the University of Colorado Boulder's Computer Science department. His research focuses on innovative intersections of Human-Computer Interaction (HCI), Augmented Reality (AR), and robotics. He explores systems that blend AI, haptics, and shape-changing interfaces to create enriched user experiences. Key areas of investigation include embedding interactivity into static educational materials (e.g., textbooks), developing AI-driven AR tools for procedural instruction, and creating shape-changing robotics for tactile feedback. His work often emphasizes practical applications in education, remote collaboration, and creative industries. Recent projects include MapStory (LLM-driven map animation), RealityEffects (3D volumetric video augmentation), and HoloDevice (holographic cross-device collaboration).
