Prof. Bernt Schiele is a Max Planck Director at the Max Planck Institute for Informatics and holds a Professorship at Saarland University. His research focuses on understanding multimodal sensor data, with key areas in computer vision, 3D object recognition, and machine learning. He leads the Computer Vision and Machine Learning group, addressing challenges in sensor fusion, scene understanding, and human activity recognition. Schiele has held academic roles at TU Darmstadt, ETH Zurich, and MIT, and contributes to top journals like IEEE Transactions on PAMI and conferences like ECCV. His work emphasizes robust models, interpretability, and domain adaptation for real-world applications. Education: PhD (1997, Grenoble), MSc (1994 Karlsruhe/1993 Grenoble) Key Positions: MIT (1997-2000), ETH Zurich (1999-2004), TU Darmstadt (2004-2010) Research interests span 3D scene understanding, multimodal sensor processing, and machine learning techniques for large-scale data. His recent work advances robust object detection, explainable AI, and domain-invariant training methods. He also chairs major conferences like ECCV 2018 and co-chairs ICCV 2011. Publications highlight innovations in interpretable vision transformers, certified explanations, and test-time adaptation. Despite no listed awards, his contributions shape foundational areas of computer vision and multimodal AI.
Zakia Hammal is an Assistant Research Professor with dual appointments at Carnegie Mellon University, holding positions in the Robotics Institute within the School of Computer Science and the Department of Biomedical Engineering in the College of Engineering. Her work bridges computer science, machine learning, artificial intelligence, and social/behavioral psychology to advance computational models for human behavior analysis. Dr. Hammal's educational background includes a PhD in Computer Science, a Master of Artificial Intelligence and Algorithmic with specialization in Image Processing, and an Engineer's degree in Computer Science with specialization in Computer Systems. Her academic journey has positioned her at the intersection of technical expertise and healthcare applications. Her research focuses on multimodal human behavior modeling in social interaction, with particular emphasis on health informatics and affective computing (Emotion AI). Dr. Hammal's work has pioneered computational models for multimodal assessment of psychiatric disorders, including depression severity evaluation, automatic pain intensity measurement, assessment of expressiveness in children with facial abnormalities, analysis of non-verbal communication in mother-infant interaction, and identification of behavioral markers in autism spectrum disorder. Her approach integrates computer vision, machine learning, and behavioral psychology to create systems that can objectively measure human behaviors that are often subjective in clinical settings. Analysis of her recent publications reveals a consistent trajectory toward more sophisticated multimodal approaches to healthcare challenges, particularly in pain assessment and mental health diagnostics. Her work increasingly emphasizes interpretable AI models that can translate complex behavioral patterns into clinically meaningful insights, with growing attention to applications for vulnerable populations including infants, elderly patients, and those with craniofacial abnormalities or autism spectrum disorder. Women in AI Awards North America 2023 – AI Researcher of the Year Award Outstanding Reviewer Award at FG 2015 Best Paper award at ACII 2015 Outstanding Paper award at ICMI 2012 Dr. Hammal has secured significant research funding, primarily from the U.S. National Institutes of Health, including an R01 grant for developing a Multimodal Behavioral AI platform for pain assessment and management, and additional grants for automatic pain assessment in older adults with dementia. Her leadership extends to mentoring through her involvement in organizing workshops and conferences that train the next generation of researchers in affective computing and health informatics. As an active leader in her field, Dr. Hammal serves as ACM ICMI Steering Board Committee Member, Associate Editor for IEEE Transactions on Affective Computing and IEEE Transactions on Multimedia, and has organized numerous influential workshops including the International Workshop on Automated Assessment of Pain and Face and Gesture Analysis for Health Informatics. She is set to serve as Program Chair for FG 2025, ACII 2025, and ICMI 2026, demonstrating her growing influence in shaping the future direction of research in multimodal interaction and affective computing.
Xiaoming Liu is the Anil K. and Nandita Jain Endowed Professor of Engineering and MSU Foundation Professor in the Department of Computer Science and Engineering at Michigan State University . Holding a Ph.D. from Carnegie Mellon University (2004), he leads cutting-edge research in computer vision and machine learning. Research Interests : Computer Vision Pattern Recognition Image and Video Processing Machine Learning Medical Image Analysis Multimedia Retrieval Recent Research Trends : Focus on 3D object detection and depth estimation Development of robust biometric recognition systems Integration of radar-camera fusion for autonomous systems Advancements in self-supervised and multimodal learning Exploration of adversarial AI security Creation of interpretable forgery detection frameworks Teaching : Spring 2013: CSE891-006 Computer Vision Seminar Fall 2012-2015: CSE803 Computer Vision Spring 2014-2017: CSE 471 Media Processing and Multimedia Contact Information : Email: liuxm@cse.msu.edu Office: EB 3137, Michigan State University Phone: +1 (517) 355-2359
Prof. Justus Thies is Full Professor for 3D Graphics & Vision at the Technical University of Darmstadt and leads the Neural Capture & Synthesis research group at the Max Planck Institute for Intelligent Systems. His research develops AI methods to capture and synthesize the real world using commodity hardware, focusing on markerless motion capture of faces and bodies, and photorealistic neural rendering. His work has been recognized with the German Pattern Recognition Award, Eurographics Young Researcher Award, and an ERC Starting Grant (all 2024). Recent publications focus on Gaussian-based avatars, neural human reconstruction, and diffusion models for scene synthesis.
Jan Grimell is an Associate Professor and Senior Lecturer at the Department of Sociology, Umeå University. His research focuses on military chaplaincy, veterans' mental health, and identity formation, combining sociological and religious perspectives. He is affiliated with the research groups Sociology of Organizations, Sociology of Religion, and Sociology of Work. Key research themes include Moral Injury, PTSD, and the transition challenges veterans face when leaving military service. His work explores existential and spiritual dimensions of health, identity reconstruction, and the role of chaplaincy in armed forces and healthcare settings. Recent projects include studies on Ukrainian military chaplaincy during the Russia-Ukraine war, the impact of moral injury among healthcare providers during the pandemic, and the long-term identity struggles of Swedish veterans. His publications span journals like Frontiers in Sociology , Journal of Pastoral Care & Counseling , and Health and Social Care Chaplaincy , with a focus on interdisciplinary approaches linking social psychology, medical sociology, and religious studies.
Neil Selwyn is a Professor in the Faculty of Education at Monash University with over 30 years of research experience focusing on the integration of digital technology into educational settings. His work centers on the 'real-life' constraints and problems faced when implementing technology-based education, with expertise in AI, automation, data-driven education, and digital degrowth. His primary research interests span digital technology and society, the use of digital technologies in educational settings, digital sociology of education, and environmental sustainability issues associated with digital technology use. Professor Selwyn is recognized internationally for his critical approach to educational technology and has led numerous nationally-funded projects examining educational data, learning analytics, and AI technologies. His recent publications reflect a growing focus on the environmental implications of AI in education, digital degrowth, and the practical challenges teachers face when implementing AI tools in classrooms. This work demonstrates a shift toward examining the sustainability and ethical dimensions of educational technology. Fellow of the Academy of Social Sciences in Australia (2024) Professor Selwyn serves as a media commentator for major outlets including the NYT, WSJ, CNN, Guardian, BBC, and ABC, and hosts the 'Education Technology Society' podcast. He maintains significant international collaborations, currently holding visiting positions at Lund University (2025), University of Oxford (2023-2027), and previously at the University of Gothenburg (2016-2017).
Ron Fedkiw is the Canon Professor of Computer Science at Stanford University's School of Engineering. He holds a PhD in Applied Mathematics from UCLA. His research focuses on computational algorithms for applications in computational fluid dynamics, computer graphics, biomechanics, and machine learning. Fedkiw has pioneered techniques for simulating natural phenomena in film and video games, earning two Academy Awards for his contributions to visual effects. He leads the PhysBAM lab and collaborates with industry through consulting roles at Epic Games and former work with Industrial Light & Magic. Education: PhD in Applied Mathematics, UCLA (1996). Notable awards include the National Academy of Science Award, Packard Fellowship, and multiple teaching honors. His lab has graduated 40 PhD students, many of whom have made significant impacts in academia and industry. Research interests span fluid dynamics, cloth simulation, facial animation, and integrating machine learning with physical models. Key contributions include algorithms for two-way fluid-solid coupling, muscle-based facial modeling, and neural network approaches for cloth and deformable bodies. Current projects explore physics-informed machine learning and real-time interactive simulations. Scientific Awards include two Oscars, PECASE, and Okawa Foundation grants. His work bridges computational physics and visual effects, with over 140 research papers and a textbook on level set methods. Advising and grants: Supervised 40 PhD students, securing funding through NSF, ONR, and industrial partnerships. Lab collaborations include SAIL (Stanford AI Lab) and Epic Games. Future work focuses on AI-driven physical simulations and biomedical applications.
Prof. Marc Stamminger is a Professor of Visual Computing at FAU since 2002, leading the Chair of Computer Science 9 (Computer Graphics). His work focuses on algorithms for synthesizing and analyzing images through 3D modeling, LiDAR/Radar capture, and light simulation. He co-leads FAU Solar, applying 3D modeling for environmental lighting analysis under varying conditions. Stamminger has published over 250 papers, winning prestigious awards like the Siggraph Test-of-Time Award. He holds executive roles in Eurographics and is Vice Dean of FAU's Technical Faculty. Research interests span neural rendering , 3D reconstruction , radar imaging , and medical visualization . Recent work emphasizes radiance field rendering (e.g., VR-Splatting, INPC) and radar-based human motion tracking. His lab's FAU Solar project integrates large-scale 3D models with environmental lighting simulations. Publications trends highlight neural rendering optimizations , radar-MIMO systems , and agricultural digital twins . Key collaborations involve medical imaging (e.g., vocal fold reconstruction) and autonomous driving data generation. Awards: Siggraph Test-of-Time (2023?), 2× Siggraph Best-Of-Show Grants/Teams: FAU Solar Lab, Eurographics leadership, FAU Vice Dean Labs: Chair of Computer Science 9, FAU Solar Initiative
Ira Kemelmacher-Shlizerman is a Full Professor of Computer Science at the Paul G. Allen School of Computer Science & Engineering at the University of Washington and Director of the UW Reality Lab. She also serves as a Principal Scientist at Google, where she leads the Shopping Gen AI visuals teams focusing on Virtual Try-On, 3D, and product videos. Her research spans computer vision, computer graphics, and Generative AI, with particular contributions to virtual try-on technology, 3D modeling, and augmented reality applications. Professor Kemelmacher-Shlizerman's research interests focus on Generative AI applications in visual computing. Her work bridges the gap between theoretical computer vision and practical applications, particularly in e-commerce and virtual reality. She has made significant contributions to virtual try-on technology, 3D editing with generative models, and AI applications for shopping experiences. Her research combines deep learning with traditional computer vision techniques to solve challenging problems in image and video synthesis. Her recent publications demonstrate a strong trend toward Generative AI applications for visual shopping experiences, virtual try-on technology, and 3D content creation. The work spans multiple top conferences including CVPR, SIGGRAPH, and ICCV, with a focus on practical applications of computer vision and graphics. Her research has evolved from foundational work in face reconstruction and aging to current applications in virtual shopping and 3D content generation. Google faculty award Madrona prize GeekWire Innovation of the Year Award Covers of CACM and SIGGRAPH Best student paper honorable mention at CVPR'21 Best demo runner up MobiSys'22 Senior member of IEEE Distinguished Member of ACM Professor Kemelmacher-Shlizerman has successfully tech-transferred multiple research projects to industry. She founded Dreambit, a startup acquired by Meta, and previously built and launched the Face Movies feature at Google. She currently leads Google's Shopping Gen AI visuals teams, focusing on 10x improvements to shopping journeys. Her UW Reality Lab serves as a hub for AR/VR research with industry partnerships. She has mentored numerous PhD students who have become researchers in both academia and industry, with several publications featuring student co-authors receiving recognition at top conferences. Professor Kemelmacher-Shlizerman leads the Graphics and Imaging Laboratory (GRAIL) and the UW Reality Lab, which focuses on augmented and virtual reality research with industry partnerships including Google. The labs work on cutting-edge projects in virtual try-on, 3D modeling, and immersive experiences, bridging academic research with real-world applications.
Heather Zheng is the Neubauer Professor of Computer Science at the University of Chicago, co-directing the SAND Lab (Security, Algorithms, Networking and Data) with Prof. Ben Y. Zhao. She holds IEEE (2015) and ACM (2023) Fellowships, and was recognized as MIT TR35 (2005) for cognitive radio research. Her work bridges mobile/IoT security, adversarial ML, and generative AI ethics. Zheng earned her PhD in Electrical and Computer Engineering from University of Maryland in 1999, with prior roles at Bell-Labs, Microsoft Research Asia, and UCSB. Education: PhD in Electrical and Computer Engineering, University of Maryland, College Park (1999) Research Focus: Explores cutting-edge challenges in: Security implications of mobile/IoT sensors (e.g., bracelet-of-silence countermeasures) Adversarial machine learning defense mechanisms (e.g., Blacklight attack detection) Generative AI governance (Glaze, NightShade copyright tools) Awards: ACM Fellow (2023) IEEE Fellow (2015) World Technology Network Fellow Labs & Collaborations: Leads SAND Lab focusing on security, ML, and networked systems. Active in UChicago's Systems Group exploring cloud/edge computing architectures.
Furkan Alaca is an Assistant Professor at Queen's University's School of Computing, part of the Faculty of Arts and Science. His research focuses on user authentication systems, addressing security and usability challenges. He holds a Ph.D. (2018) in Computer Science from Carleton University, an M.A.Sc. (2012) in Electrical and Computer Engineering, and a B.Eng. (2010) in Communications Engineering, all from Carleton University. His academic career includes teaching roles at Queen's University and the University of Toronto Mississauga, where he taught courses such as Cryptography, Cybersecurity, and Discrete Mathematics. He is affiliated with Queen's Security Research Group and Computer Security Research Lab. Research interests include computer and internet security, usable security, authentication mechanisms, and systems security. He has contributed to advancements in web authentication frameworks, malware analysis, and privacy-preserving technologies. His work spans conferences like IEEE and ACM, with notable publications in cybersecurity, machine learning, and network efficiency. Current teaching includes CISC 447 (Introduction to Cybersecurity) and CISC 468 (Cryptography). He has advised on courses ranging from undergraduate programming to graduate-level security topics.
Scott T. Acton is the Lawrence R. Quarles Professor and Chair of Electrical and Computer Engineering at the University of Virginia, with a courtesy appointment in Biomedical Engineering. He leads the VIVA lab, specializing in biological image analysis, machine learning, and AI for education. His research spans medical imaging, signal processing, and computer vision. Professor Acton holds a B.S. (Virginia Tech, 1988), M.S. (UT Austin, 1990), and Ph.D. (UT Austin, 1993) in Electrical Engineering. He has authored over 325 publications and served as Editor-in-Chief of IEEE Transactions on Image Processing and General Co-Chair of the IEEE International Symposium on Biomedical Imaging. His research interests include bioimage analysis, machine learning applications, and medical imaging technologies. The VIVA lab focuses on problems like cell tracking in bacterial biofilms, gait recognition using LiDAR, and AI-driven classroom activity analysis. Awards: IEEE Fellow (2013), All-University Teaching Award (2009), Outstanding Young Electrical Engineer (1996). Courses Taught: How the iPhone Works, Digital Image Processing, Signals and Systems. Labs/Teams: VIVA - Virginia Image and Video Analysis lab. Recent work emphasizes AI for education (e.g., automated classroom activity classification) and medical imaging advancements like 3D biofilm segmentation and LiDAR-based human identification. His contributions bridge engineering and healthcare, with applications in neuroscience and clinical decision support.
Remko Van Hoek serves as Professor of Practice in the Department of Supply Chain Management at the Sam M. Walton College of Business, University of Arkansas. He teaches Sourcing and Procurement courses across undergraduate, master's, and doctoral programs. Prior to joining the University of Arkansas, Dr. Van Hoek held academic positions in Europe and served as a visiting professor at Cranfield School of Management, complemented by extensive industry experience as a supply chain and procurement executive at global corporations including Nike, PwC, and The Walt Disney Company. He currently serves on the Council of Supply Chain Management Professionals (CSCMP) Board of Directors and acts as executive director of the CSCMP Supply Chain Hall of Fame hosted by the Walton College. Dr. Van Hoek's research centers on digital transformation in procurement and supply chain management, with particular focus on blockchain implementation, artificial intelligence applications, and sustainable sourcing practices. His work investigates how emerging technologies reshape procurement processes, supplier relationship management, and risk mitigation strategies. He explores innovative approaches to supplier diversity programs, ethical sourcing frameworks, and the strategic evolution of procurement from cost reduction to value creation. His research consistently bridges academic theory with industry practice through case studies from major corporations, developing actionable frameworks for practitioners facing digital disruption and sustainability challenges. Analysis of his 15 most recent publications (2023-2025) reveals dominant themes in AI-driven supply chain risk prevention, blockchain implementation for transparency, and sustainable supplier engagement. His work demonstrates consistent industry collaboration, drawing from case studies at Walmart, Moet Hennessy, and Bayer to address post-pandemic resilience challenges. A significant portion examines procurement's strategic evolution beyond transactional functions, with recurring emphasis on ethical considerations in digital transformation and the practical implementation barriers for emerging technologies in global supply networks. As a Professor of Practice, Dr. Van Hoek integrates executive-level industry experience into curriculum development and student mentorship. His industry-engaged teaching model includes guest lecturer programs connecting students with supply chain practitioners, as evidenced by his co-authored work on integrating industry insights into supply chain education. While specific grant funding details are not publicly documented, his leadership in the CSCMP Supply Chain Hall of Fame demonstrates commitment to professional development and industry-academia knowledge transfer. Dr. Van Hoek directs the CSCMP Supply Chain Hall of Fame initiative, which documents transformative contributions to supply chain management through interviews with industry pioneers and historical case studies. This platform serves as both an educational resource for Walton College students and a professional development tool for supply chain practitioners globally. The Hall of Fame preserves critical industry knowledge while highlighting contemporary innovations in supply chain strategy and technology implementation.
Prof. Dr. Birgit Eickelmann is a Professor of School Pedagogy at the Institute of Educational Science within the Faculty of Arts and Humanities at Paderborn University. She has held this position since October 2012, initially as a W2 professor until January 2014, and then as a W3 professor (full professor) from February 2014 onward. Her research focuses on school and lesson development in the digital age, school pedagogy under digital transformation conditions, empirical school research, teacher education, and school leadership with emphasis on digital learning leadership. Her educational background includes a habilitation in Educational Science in May 2012, a PhD in Educational Science with summa cum laude in July 2009, and state examinations for teaching Mathematics and Physics in December 1996 and January 1999. Prof. Eickelmann's research centers on the digital transformation of educational systems, particularly examining how schools develop digital competencies among students and teachers. Her work emphasizes equitable access to digital learning opportunities, the role of school leadership in digital transformation, and the development of computational thinking skills. She investigates how schools can become resilient in the face of digital challenges, with special attention to organizational factors that support successful digital integration. Her publication record reveals a strong focus on international comparative studies, particularly the IEA's ICILS (International Computer and Information Literacy Study) across multiple cycles (2013, 2018, 2023). Her research spans digital literacy assessment, school-level factors influencing digital competence development, and policy implications for educational systems undergoing digital transformation. Prof. Eickelmann leads several major research initiatives including the National Research Center for the IEA Study ICILS 2023 (2021-2026), the German coordination of the Horizon-2020 project 'DigiGen' (2019-2022), and previous leadership of ICILS 2018 (2015-2021) and ICILS 2013 (2012-2015). She is actively involved in policy advising regarding digital education in Germany. She is a member of numerous scholarly organizations including the World Educational Research Association (since 2021), the Society for Empirical Educational Research (since 2016), and the German Society for Educational Science (since 2014), among others. Her work bridges academic research with practical implementation in schools through projects like 'Navigator Bildung Digitalisierung' and 'schultransformNEXT'.
Leia Stirling is an Associate Professor in Robotics and Industrial Operations Engineering at the University of Michigan, serving as Associate Chair of Undergraduate Studies in Robotics. She is Core Faculty at the Center for Ergonomics and Affiliate Faculty at the Space Institute. Her research focuses on human-system interactions in robotics, biomechanics, and decision support systems. Key areas include wearable motion sensing for decision-making, co-adaptive exoskeleton algorithms, and space mission operations tools. Her group blends human factors, biomechanics, and robotics to design technology that enhances human performance in complex tasks. She leads the Stirling Group, which develops metrics for injury risk assessment, exoskeleton usability, and space crew readiness evaluation. Her lab’s work has applications in industrial safety, healthcare rehabilitation, and astronaut training. Research Thrusts: Wearable Motion Sensing: Creates metrics for musculoskeletal injury risk, balance rehabilitation, and quantitative assessment of agility/coordination. Exoskeleton Usability: Develops co-adaptive control algorithms and studies trust dynamics between users and wearable robots. Space Operations: Designs tools for astronaut readiness, human-aware robotics, and extravehicular activity planning. Recent work includes studies on neonatal ventilation systems, space inspection trajectory optimization, and augmented reality for sensorimotor assessments. She teaches robotics courses including ROB 204: Introduction to Human-Robot Systems. Her work has been featured at IROS 2023 and NASA-related initiatives. Lab Website: stirlinglab.org
