Webb Keane is the George Herbert Mead Collegiate Professor of Anthropology at the University of Michigan. He is affiliated with the Center for Southeast Asian Studies, Global Islamic Studies Center, and the Interdisciplinary Program in Anthropology and History. His research focuses on ethics, semiotics, material culture, and religion in Southeast Asia and beyond. He holds a PhD and AM from the University of Chicago and a BA from Yale College. His major publications include Signs of Recognition , Christian Moderns , and Ethical Life , with his latest book Animals, Robots, Gods (2025) exploring non-human ethics. Keane has held fellowships from the Guggenheim Foundation and the National Endowment for the Humanities, and has lectured globally, including at Cambridge and the London School of Economics. Research Interests: Moral philosophy, semiotics, materiality, religion, media, and historical consciousness. Awards: Guggenheim Fellowship, Annette B. Weiner Memorial Lecturer, Edward Westermarck Memorial Lecturer. Teaching: Courses on language and culture, anthropology of religion, and Southeast Asian studies. Affiliations: Center for Southeast Asian Studies, Global Islamic Studies Center, Interdisciplinary Program in Anthropology and History.
David Peeters is an Associate Professor at Tilburg University's Department of Communication and Cognition, part of the Tilburg School of Humanities and Digital Sciences. His research focuses on multimodal communication, multilingualism, and digital communication, leveraging immersive virtual reality (VR) technologies combined with EEG, eye-tracking, and fMRI. He explores neurobiological underpinnings of language, including neuropragmatics, non-verbal communication, and multilingualism. His work is supported by grants such as the NWO Veni and Tilburg University Fund. Peeters teaches courses on virtual reality, language psychology, and digital literature integration in education. He is a Research Fellow at the Donders Institute and President of the Tilburg Young Academy. Key research interests include the role of gesture and iconicity in second language acquisition, bilingual language switching in immersive environments, and the impact of dataism on academic publishing. He collaborates with libraries and schools to integrate digital literature into curricula and public collections. His scientific awards include the NWO Veni Grant and a Fellowship from the International Max Planck Research School for Language Sciences. His research bridges cognitive science, linguistics, and technology, emphasizing ecologically valid experimental paradigms.
Dr Ellen Adams is a Reader in Classical Archaeology and Liberal Arts at King’s College London, affiliated with the Departments of Classics and Interdisciplinary Humanities. She holds a PhD from the University of Cambridge (2004) and has conducted archaeological fieldwork across Europe. Her research focuses on Minoan Crete, disability studies in classical contexts, and museum accessibility for sensory-impaired audiences. She co-organizes the ICS Mycenaean Seminars and founded the Museum Access Network for Sensory Impairments (MANSIL) in 2018. Her work bridges archaeology and modern accessibility, emphasizing audio description, touch tours, and British Sign Language in museums. Recent projects include Disability Studies and the Classical Body (Routledge, 2021) and collaborations with institutions like the British Museum and the Courtauld Gallery. She teaches Greek archaeology, museum studies, and interdisciplinary modules in global cultures. Adams has appeared on BBC Radio discussing Minoan civilization and classical reception. Her research projects include Making Sense of Visual Art Through a Visual Language (BSL) and Anosmia in Culture and History . She actively curates public engagement events, such as BSL storytelling in Holyrood Park and creative writing competitions for blind/visually impaired audiences.
YingLi Tian is a CUNY Distinguished Professor in the Department of Electrical Engineering at The City University of New York. Their work focuses on computer vision, machine learning, and medical imaging. Key areas include sign language recognition, medical image analysis, and AI-driven healthcare solutions. Research Interests: Artificial Intelligence applications in healthcare 3D point cloud and scene understanding Self-supervised learning and domain adaptation Sign language recognition systems Medical imaging segmentation and diagnosis Human-robot interaction and assistive technologies Notable Projects: Developed AI systems for American Sign Language recognition using RGB-D data Pioneered self-supervised feature learning techniques in medical imaging Created virtual contrast enhancement tools for CT scans Advanced sea ice motion prediction using deep learning Labs & Teams: Leads the Media and Information Technology Lab at CCNY, focusing on multimodal AI and healthcare technology innovations.
Andrzej Majkowski is an Associate Professor at the Institute of the Theory of Electrical Engineering, Measurement and Information Systems, Faculty of Electrical Engineering, Warsaw University of Technology. His career spans over two decades of research in biomedical engineering, focusing on brain-computer interfaces, signal processing, and emotion recognition. Active in both teaching and research, he contributes to advancing methodologies in electrophysiological signal analysis. Warsaw University of Technology Institute of the Theory of Electrical Engineering, Measurement and Information Systems Faculty of Electrical Engineering Specializing in biomedical engineering , Majkowski's research bridges control systems and information technologies with neuroscience applications. His work explores brain-computer interfaces , EEG/EMG signal processing , and emotion recognition using multimodal physiological data. Recent studies focus on deep learning architectures for artifact removal and classification tasks. Recent publications highlight trends in CNN-LSTM hybrid models for signal denoising, convolutional networks for seizure detection, and machine learning applications in visual evoked potential analysis. His work spans both clinical applications (epilepsy monitoring) and human-computer interaction (emotion recognition, sign language detection). With over 98 documented publications and significant bibliometric indicators (h-index 13 in Scopus), Majkowski has supervised 95 promoted theses. His research includes one funded project and collaborations in biomedical instrumentation, though specific award details remain unspecified in available records.
M. Tamer Özsu is a University Professor of Computer Science at the David R. Cheriton School of Computer Science, University of Waterloo, where he holds a Cheriton Faculty Fellowship. He also serves as a Distinguished Visiting Professor at Tsinghua University and is the Founding Director of Waterloo-Huawei Joint Innovation Laboratory since 2018. His extensive contributions to computing have earned him numerous prestigious awards including the 2024 ACM Presidential Award for long-standing and significant contributions to the computing field. Professor Özsu's research focuses on data engineering aspects of data science, particularly addressing data management issues with two main foci: management of non-traditional data and large-scale distributed data management. He is renowned for his seminal book "Principles of Distributed Database Systems" (co-authored with Patrick Valduriez), now in its fourth edition, and the "Encyclopedia of Database Systems" (co-edited with Ling Liu), in its second edition. His work bridges theoretical foundations with practical system implementations, targeting grand societal challenges through computational approaches. His recent publications reveal a strong trend toward graph analytics, streaming data processing, and the integration of large language models with vector data management. The research shows increasing focus on GPU-accelerated graph processing, RDF query optimization, and multimodal data analysis, reflecting the evolution of data management challenges in the era of big data and AI. His work continues to address fundamental challenges in distributed data systems while adapting to emerging technologies and application domains. Scientific Awards and Fellowships ACM Presidential Award (2024) IEEE TCDE Education Award (2024) IEEE Innovation in Societal Infrastructure Award (2022) CS Can | Info Can Lifetime Achievement Award (2018/2019) ACM SIGMOD Test-of-Time Award (2015) ACM SIGMOD Contributions Award (2006) The Ohio State University College of Engineering Distinguished Alumnus Award (2008) Fellow of the Royal Society of Canada Fellow of the American Association for the Advancement of Science (AAAS) Life Fellow of the Association for Computing Machinery (ACM) Life Fellow of the Institute of Electrical and Electronics Engineers (IEEE) Fellow of the Asia-Pacific Artificial Intelligence Association (AAIA) Elected member of the Science Academy, Türkiye Professor Özsu has been deeply involved in academic leadership and community building. As Founding Editor-in-Chief of ACM Books (2013-2019), he launched a series that by 2019 had published 28 major books with another 30 under contract. His service to ACM, particularly through SIGMOD, has been exemplary and widely recognized. He directs the Waterloo-Huawei Joint Innovation Laboratory, which focuses on cutting-edge research in data management and distributed systems, fostering strong industry-academia collaboration.
Oisin Mac Aodha is a Reader (Associate Professor) in Machine Learning at the School of Informatics, University of Edinburgh. He is also an ELLIS Scholar and founder of the Turing interest group on biodiversity monitoring and forecasting, having previously served as a Turing Fellow from 2021-2025. Mac Aodha completed his undergraduate degree in electronic engineering from the University of Galway in Ireland, followed by his MSc and PhD at University College London (UCL). His academic journey includes postdoctoral positions at UCL (2013-2016) working with Prof. Gabriel Brostow and Prof. Kate Jones, and at Caltech (2016-2019) in Prof. Pietro Perona's Computational Vision Lab as part of the Visipedia team. His research centers on computer vision and machine learning with emphasis on 3D understanding, human-in-the-loop methods, and AI for conservation and biodiversity monitoring. He has made significant contributions to monocular depth estimation (including the influential Monodepth2 paper), fine-grained visual categorization, and biodiversity monitoring systems. His work bridges theoretical machine learning with practical ecological applications, developing tools for species identification, range estimation, and conservation efforts. Recent publications reveal a strong trend toward ecological applications while maintaining fundamental contributions to 3D vision and representation learning. His major scientific achievements include: Turing Fellow (2021-2025) ELLIS Scholar Founder of the Turing interest group on biodiversity monitoring and forecasting Co-organizer of the Fine-Grained Visual Categorization (FGVC) workshop series at major vision conferences Mac Aodha advises multiple PhD students and postdocs working on computer vision for biodiversity monitoring, 3D understanding, and human-in-the-loop learning. His team has developed practical tools like Whombat (an open-source annotation tool for bioacoustics) and contributed to field-deployed biodiversity monitoring systems. He has served as Area Chair for top conferences including NeurIPS, CVPR, ICCV, and ICML, demonstrating his standing in the computer vision community. His research group collaborates extensively with ecologists at University College London, particularly with Prof. Kate Jones' team, bridging machine learning expertise with ecological domain knowledge. The Vision at Edinburgh group he contributes to focuses on developing practical AI tools that address real-world conservation challenges while advancing fundamental computer vision research.
Professor Byung S. Lee is a distinguished faculty member in the Department of Computer Science at the University of Vermont's College of Engineering and Mathematical Sciences. He joined UVM in 1999 and continues to be actively engaged in teaching, research, and service. His office is located in Innovation Hall at the Burlington campus, where he maintains regular office hours and oversees his research lab. Professor Lee holds a Ph.D. from Stanford University, an MS from Korea Advanced Institute of Science and Technology, and a BS from Seoul National University. His educational background provided the foundation for his extensive career in computer science research and education. Professor Lee's research spans multiple domains within computer science, with a particular focus on database systems, data mining, and data science. His work increasingly integrates machine learning techniques with traditional database approaches, especially in the analysis of time series data. He has made significant contributions to graph theory applications, anomaly detection methods, and environmental data analysis. His research often bridges computer science with practical applications in healthcare, environmental science, transportation, and astrophysics through interdisciplinary collaborations. An analysis of his recent publications reveals a strong trend toward time series analysis and anomaly detection, particularly applied to environmental monitoring and healthcare data. His work demonstrates a consistent evolution from foundational database research to more applied machine learning approaches, with increasing emphasis on real-world problem solving across multiple scientific domains. Professor Lee has served as primary advisor for numerous graduate students across multiple cohorts, including PhD candidates, Master's students, and postdoctoral researchers. His advising portfolio reflects the breadth of his research interests, with students working on topics ranging from graph neural networks to medical informatics applications. He has also been actively involved in professional service, serving on program committees for major conferences including SAC, PAKDD, DASFAA, and CIKM. Professor Lee leads a vibrant research laboratory that focuses on cutting-edge data science methodologies and their applications. His team collaborates extensively with researchers in environmental science, hydrology, and healthcare, demonstrating the interdisciplinary nature of modern data science research. The lab maintains active projects in time series analysis, graph analytics, and environmental monitoring systems, often working with large-scale datasets from real-world applications.
Dr. Tatsuya Mori is a Professor at the Department of Computer Science and Communication Engineering , Faculty of Science and Engineering, Waseda University . He also holds visiting researcher positions at RIKEN Center for Advanced Intelligence Project (since 2018) and National Institute of Information and Communications Technology (since 2019). Education: Ph.D. in Information Science (2005), Waseda University Research Interests: Spanning information security and privacy across emerging technologies like autonomous driving , AI , 3D sensing , VR , biometric measurement , and Web3 . His work focuses on offensive security and interdisciplinary research , including physical-layer attacks on sensors and behavioral studies on phishing detection. Scientific Awards: Recipient of multiple prestigious awards, including the Distinguished Paper Award Runners-Up at IEEE EuroS&P 2024 , IPSJ Outstanding Paper Award 2024 , and CSS2024 Concept Research Prize . His research has been recognized in top conferences like USENIX Security , NDSS , and ACM CCS . Professional Leadership: Active in academic governance as Chief Investigator for NISC Working Groups and Committee Member for JST Research Areas . He serves on program committees for NDSS , IMC , and ACM CCS .
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.
Professor Peter Watkinson serves as Professor of Intensive Care Medicine at the University of Oxford and is an NHS consultant in intensive care at the Oxford University Hospitals NHS Foundation Trust. He leads the Critical Care Research Group based at the Kadoorie Centre for Critical Care Research & Education at the John Radcliffe Hospital, Oxford. His work bridges clinical practice with academic research in the field of critical care medicine through the Nuffield Department of Clinical Neurosciences. Professor Watkinson's research primarily focuses on the identification of deteriorating patients in hospital settings. His work encompasses: Design and implementation of studies on wearable monitoring devices Exploration of non-contact monitoring technologies Analysis of standard electronically-recorded patient descriptors Pattern recognition in vital signs data to predict clinical deterioration Development of electronic monitoring systems Application of human factors techniques for technology integration in healthcare Assessment of long-term effects of critical illnesses on patient quality of life The Critical Care Research Group maintains a strong collaborative link with the University of Oxford Institute of Biomedical Engineering. Using data collected from thousands of patients' vital signs both in Oxford and elsewhere, the multi-disciplinary team investigates patterns that precede and predict clinical deterioration in hospitalized patients. Recent publications indicate a strong focus on early warning scores, patient monitoring technologies, and the application of machine learning approaches to critical care data. Professor Watkinson's research output demonstrates consistent productivity with numerous 2024-2025 publications spanning systematic reviews of early warning systems, development of novel monitoring technologies, and analytical approaches to predicting patient deterioration. His work frequently employs rigorous methodology including systematic reviews, meta-analyses, and innovative study designs to address critical questions in intensive care medicine. As leader of the Critical Care Research Group, Professor Watkinson oversees a multi-disciplinary team investigating vital sign patterns and developing predictive algorithms that have direct clinical applications. The group's research has significant implications for improving patient safety through earlier recognition of clinical deterioration and more effective resource allocation in hospital settings.
Garreth Tigwell is an Assistant Professor in the School of Information at RIT, co-directing the CAIR Lab with Dr. Kristen Shinohara. His research focuses on accessibility in digital design, particularly for disabled users, addressing challenges faced by novice and expert creators. His work spans topics like accessible prototyping tools, cultural considerations in design, and inclusive mixed reality interfaces. Education: BSc in Psychology, University of Dundee, 2012 MSc (Distinction) in User Experience Engineering, University of Dundee, 2014 PhD in Human-Computer Interaction (HCI), University of Dundee, 2019 Research Interests: Designing accessible digital systems for blind, deaf, and low-vision users Adaptable user interfaces for mixed reality and textured surfaces Cultural dimensions in accessibility pedagogy Authentication methods for visually impaired users Articles Trends: Recent work emphasizes AR/VR accessibility, cultural design frameworks, and haptic authentication. His studies often involve collaborations with global researchers and industry partners. Awards: Best Paper (MobileHCI 2022) CHI Honorable Mention (2023, 2024) NSF-funded research projects Advising & Grants: Active in mentoring graduate students and securing grants. His lab focuses on born-accessible AI tools and inclusive design education. Teaches courses like HCI Research Methods and Future Interactions. Labs/Teams: Co-leads the CAIR Lab, which develops technologies to bridge accessibility gaps in digital design and prototyping.
Dr. Kate Farrahi is an Associate Professor in the ECS department at the University of Southampton, where she leads research in the Vision, Learning and Control (VLC) Group. Previously, she was a Research Assistant at the Idiap Research Institute and earned her PhD in Computer Science from the Swiss Federal Institute of Technology in Lausanne (EPFL). Her work focuses on the intersection of machine learning and digital health, particularly in developing human sensing methods using vision and wearable technologies. She currently supervises four PhD students in Computer Science and actively accepts new PhD applications. Her research interests span machine learning applications in healthcare, including wearable device analytics, epidemiological modeling via AI, and drug discovery through generative methods. She has been recognized with a Best Paper Award (2022) and contributes to interdisciplinary research groups such as the Institute for Life Sciences and Centre for Machine Intelligence. Her work bridges computational methods with real-world health challenges, emphasizing practical deployment of AI solutions in clinical and public health contexts. Research Groups: Vision, Learning and Control; Institute for Life Sciences; Centre for Health Technologies; Centre for Machine Intelligence Key Collaborations: Cross-disciplinary projects combining computer science with biomedical engineering and public health
Sarah Ebling is a Full Professor of Language, Technology and Accessibility at the University of Zurich's Faculty of Arts and Social Sciences. She leads the Language, Technology and Accessibility research group within the Institute for Computational Linguistics. Her work focuses on computational linguistics applications for assistive technologies targeting disabilities such as hearing impairments, visual impairments, and cognitive disorders. Key areas include sign language technologies, automatic text simplification, and audio description systems. She directs the large-scale Swiss innovation project 'Inclusive Information and Communication Technologies' (2022-2026, CHF12 million budget) and collaborates on EU H2020 and SNSF Sinergia projects. Education: Holds a doctoral degree (summa cum laude, 2016) from the University of Zurich with research on automatic translation to Swiss German Sign Language. Completed studies in German Linguistics, Computational Linguistics, and English Linguistics at Universities of Zurich and Heidelberg, with research stays in Dublin, Chicago, and Rochester. Research emphasizes multimodal accessibility solutions, including sign language fluency assessment, gesture-based interaction, and AI-driven text adaptation. Current projects explore audio description translation systems (SwissADT), sign language corpus development (SwissSLi), and digital tools for comprehensibility assessment in simplified texts. Her work bridges computational linguistics with ethical considerations in assistive technology deployment. Grants and Leadership: Principal Investigator on major accessibility-focused grants, including the CHF12M Swiss innovation project. Supervises PhD candidates in areas like sign language assessment tools and text simplification algorithms. Active in international collaborations, publishing extensively in computational linguistics and accessibility journals/conferences. Technology Development: Created the 'DigiSpon' benchmark for language sample analysis and developed open-source tools for sign language translation baselines. Her team's innovations include the SignCLIP model connecting text and sign language via contrastive learning, and pose estimation frameworks for sign language recognition.
Dr. Esme Cleall is a Senior Lecturer in the History of the British Empire at the University of Sheffield's School of History, Philosophy and Digital Humanities. She holds a B.A. and M.A. from the University of Sheffield and a Ph.D. from University College London (UCL). Her research focuses on the politics of colonial difference, particularly intersections of race, gender, disability, and religion in the British Empire. She has held an AHRC Leadership Fellowship and a British Academy Small Grant. Her major works include Colonising Disability (2022) and Missionary Discourses of Difference (2012), analyzing disability in colonial contexts and missionary writings. Current projects explore mental distress in colonial archives and the history of deafness across the British World. She teaches courses on cultural imperialism and empire, and actively engages in decolonizing curricula. Publications span journals like Historical Journal and History Workshop Journal , with key themes including: missionary justice in India, deaf communities in colonial contexts, and disability as a tool of imperial control. She supervises PhD students researching topics from missionary writing to disability and technology. Cleall collaborates with community groups such as Sheffield Voices to produce accessible historical narratives, including films about learning disability history. She also contributed to the 'Indian Heritage in the Peak District' project, highlighting South Asian connections to the region.
