Karsten Kenklies is a Senior Lecturer in Education at the Strathclyde Institute of Education, University of Strathclyde. Previously, he served as Junior-Professor (Chair for Comparative Education) at Jena University, Germany from 2003-2016. His work bridges Hermeneutic Pedagogy with intercultural comparisons, focusing on European, Japanese, and American educational theories. Key interests include Bildung , queer pedagogy, and Japanese arts-based education. Education: Multiple Masters (Science, Education, Philosophy, Arts) and PhD (Jena University, Germany). Research Focus: Systematic-conceptual analysis of education across cultures. Current projects explore pre-modern Japanese educational theories, continental Bildung concepts, and Anglo-Saxon self-formation ideas. Co-founder of ExET (Experiments in Education Theory), promoting conceptual research globally. Awards: Award for Fundamental Research (2007), Research & Conference Fellowship (2018). Teaching: Leads MSc Education Studies program, teaches History of Education, Pedagogical Theory, and Contemporary Challenges. Supervises PhD projects in areas like queer education, Japanese pedagogy, and hermeneutic approaches. Key Contributions: Over 78 publications including books, articles, and reviews. Active in international networks like the Philosophy of Education Society of Great Britain. Co-investigator on projects like 'What’s the ‘use’ in Higher Education?' (2025-2026).
Barbara Landau is the Dick and Lydia Todd Professor of Cognitive Science at Johns Hopkins University (since 2001). She previously served as Vice Provost for Faculty (2011-2014) and Director of the Science of Learning Institute (2013-2018). Her research focuses on the interplay between language and spatial cognition, studying typical and atypical development through experimental psychology, linguistic analysis, and brain imaging. PhD in Cognitive Science, University of Pennsylvania Landau investigates the cognitive primitives underlying early development, including how children learn spatial language (e.g., prepositions), how spatial impairments affect word learning in Williams syndrome, and how language and spatial cognition interact. Her work spans typical development, congenital blindness, Williams syndrome, and post-stroke spatial representation. She leads the Language and Cognition Lab, part of the JHU Vision Sciences Group (Cognitive Science, Psychological and Brain Sciences, Neuroscience). Her research has been featured in Time , New York Times , NPR, and The New Yorker, with a focus exhibit at the Walters Art Museum (2011). She received the Guggenheim Fellowship (2009), William James Fellow Award (2018), and is a member of the National Academy of Sciences. Cognitive Science Society Fellow American Academy of Arts and Sciences Fellow American Association for the Advancement of Science Fellow Landau’s lab collaborates with University of Pennsylvania researchers (John Trueswell, Lila Gleitman) on NSF-funded projects connecting symmetry to linguistic/perceptual development. She trains students like Zihan Wang (2023 Glushko Award) and Rennie Pasquinelli (Science of Learning Fellow). Her studies involve participants aged 18 months to 18 years, including Williams syndrome individuals.
Eetu Mäkelä is a Professor of Digital Humanities at the University of Helsinki, leading the research group at the Helsinki Centre for Digital Humanities. He focuses on computational methods in humanities and social sciences, including datafication and interdisciplinary collaboration. He serves as Technical Director of DARIAH-FI and a Research Programme Director at the Helsinki Institute for Social Sciences and Humanities. Currently, he heads the preparatory group for the Helsinki Liberal Arts and Sciences Bachelor’s Programme. His research emphasizes technological and theoretical foundations of computational research, with notable contributions to linked open data, sociolinguistic analysis, and historical text mining. He has developed widely used tools like Recon, Palladio, and Octavo, which are employed in academic and public sectors. His work has garnered over 20 awards, including best paper and open science recognitions. Key areas of expertise include digital humanities methodologies, data integration, and open science practices. He actively contributes to teaching, designing courses such as 'Methods for Digital Humanities' and demonstrating innovative pedagogical approaches. His recent projects involve analyzing 18th-century philosophical texts and sociolinguistic change using computational methods. Awards: Multiple best paper awards, open data awards, and open science awards. Grants/Advising: Leads research initiatives funded by the Academy of Finland and other bodies. Mentors interdisciplinary research teams but no specific student names listed. He maintains active roles in academic infrastructure, including the DARIAH-FI initiative and the Helsinki Institute’s datafication program. His work bridges technical innovation with humanities scholarship, emphasizing practical, enduring systems for academic and public use.
David Lillis is an Associate Professor in the School of Computer Science at University College Dublin (UCD). His research focuses on Natural Language Processing (NLP), Artificial Intelligence (AI), and their applications in legal and forensic contexts. He leads projects like CeADAR (Ireland’s Applied AI Center) and the Transpire project, collaborating with organizations such as Corlytics and the Department of Enterprise, Trade and Employment. He holds adjunct roles as a Guest Professor at Beijing University of Technology’s Data Mining and Security Lab and has been a Fulbright Scholar at the University of New Haven’s Cyber Forensics Research and Education Group. Education: B.A. (Hons) in Law and Accounting, University of Limerick Higher Diploma in Computer Science, UCD M.Sc., Ph.D. in Computer Science, UCD Professional Certificate in University Teaching & Learning, UCD Research Interests: Legal AI, digital forensics, machine learning, multi-agent systems, and information retrieval. Recent work includes NLP for regulatory analysis, crop yield prediction via neural networks, and AR-driven decision support systems. Grants & Projects: Principal Investigator: Transpire (AI Platform for Regulation) SFI Funded Investigator: CONSUS (Crop Optimization) PI: CeADAR Technology Centre Teaching roles include Deputy Programme Director for Software Engineering at Beijing-Dublin International College (BDIC) since 2014. Labs & Groups: UCD Forensics and Security Research Group, ML-Labs (SFI Centre for ML Training), and the Data Mining and Security Lab (BJUT).
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.
David Lindlbauer is an Assistant Professor at Carnegie Mellon University's Human-Computer Interaction Institute (HCII), where he leads the Augmented Perception Lab and co-directs the CMU Extended Reality Technology Center. His research focuses on advancing Mixed Reality (MR) and Extended Reality (XR) interfaces through computational interaction methods that optimize spatial, temporal, and multimodal feedback.
David Hsu is Provost's Chair Professor in the Department of Computer Science at the National University of Singapore (NUS) School of Computing, where he founded and directs the NUS Artificial Intelligence Laboratory (NUSAIL) and leads the Smart Systems Institute. His academic leadership includes chairing major conferences such as Robotics: Science & Systems (2015) and IEEE ICRA (2016), alongside editorial roles in IEEE Transactions on Robotics and the Journal of Artificial Intelligence Research. He earned a B.Sc. in Computer Science & Mathematics from the University of British Columbia and a Ph.D. in Computer Science from Stanford University. His research spans robotics, AI, and computational biology, with recent focus on robot planning under uncertainty and human-robot collaboration. Current work integrates machine learning with decision-theoretic planning to enable robust human-robot co-existence in unstructured environments. Analysis of his 2023-2025 publications reveals dominant trends in deformable object manipulation (e.g., clothes handling via semantic keypoints), open-world navigation using scene graphs, and LLM-driven multi-agent reasoning for complex tasks. Key innovations include perspective-aware visual grounding for human-centric interaction and functional object arrangement through compositional generative models, reflecting a strong emphasis on real-world applicability. His scientific contributions have earned prestigious recognition: IJCAI-JAIR Best Paper Prize (2022) for foundational AI research Robotics: Science & Systems Test of Time Award (2021) IEEE Fellowship (2018) for contributions to robotic planning RSS Best Systems Paper Award (2017) RoboCup Best Paper Award at IROS (2015) Humanitarian Robotics Award at ICRA (2015) As director of the Adaptive Computing Laboratory, Hsu drives research on fundamental computational frameworks for human-robot interaction. The lab's work on uncertainty-aware decision-making has secured significant research funding through grants from Singapore's National Research Foundation and industry partnerships with robotics firms. While specific student names aren't publicized, his leadership in the NUSAIL indicates extensive mentorship of doctoral candidates in AI and robotics.
Michela Bertolotto is a Professor in the School of Computer Science at University College Dublin (UCD). Her research focuses on spatio-temporal data modeling, GIScience, and applications of geospatial technologies in fields like urban planning and health informatics. She leads a research group and has supervised 19 PhD and 8 MSc students. Her work includes innovations in LiDAR-based flood risk visualization, semantic web quality assurance, and open-source spatial data analysis. Bertolotto has held roles including College Lecturer at UCD (2000–2006) and postdoctoral research positions at the University of Maine and University of Genoa. Education: BSc and PhD in Computer Science from the University of Genoa (1993, 1998). Professional achievements include over 100 publications, 24 grants (e.g., Science Foundation Ireland-funded Urban ARK project), and editorial roles at journals like the International Journal of Geographical Information Science. Awards include the UCD President's Research Award (2001) and NATO Postdoc Fellowship (1998–1999). Research interests span map personalization, volunteered geographic information (VGI), and geospatial data quality. Her lab develops tools like the LAMSkyCam (low-cost sky imaging system) and dynamic flood risk viewers. She chairs international conferences and serves on program committees for GIScience events.
Roberto Tamassia is the James A. and Julie N. Brown Professor of Computer Science and Chair of the Computer Science Department at Brown University. He is also Director of Brown's Center for Geometric Computing. His research focuses on information security, cryptography, algorithms, graph drawing, and computational geometry. He has authored six textbooks and over 250 publications, and his work has been funded by ARO, DARPA, NATO, NSF, and industry sponsors. Education: PhD in Electrical and Computer Engineering from the University of Illinois at Urbana-Champaign. Research Interests: Cryptography and Secure Systems Algorithm Design and Optimization Graph Drawing and Geometric Computing Encrypted Database Security Awards and Honors: IEEE Fellow Technical Achievement Award from IEEE Computer Society Listed among the 360 most cited computer science authors by ISI Grants and Funding: His research has been supported by major agencies and organizations including ARO, DARPA, and NSF, as well as industry partnerships. Labs and Affiliations: Directs Brown's Center for Geometric Computing, a hub for interdisciplinary research in computational geometry and graph algorithms.
Professor Reynold Cheng is a faculty member at the University of Hong Kong (HKU), specifically within the Department of Computer Science in the School of Computing and Data Science (CDS). He currently serves as the Division Head of the AI & Data Science Division at CDS and is part of the Steering Committee of the Musketers Foundation Institute of Data Science. His academic journey includes a BEng and MPhil from HKU (1998–2000) and an MSc and PhD from Purdue University (2003–2005). Prior to HKU, he was an Assistant Professor at the Hong Kong Polytechnic University (HKPU) from 2005 to 2008. Cheng’s research focuses on data science, big graph analytics, and uncertain data management. He has received numerous awards, including the SIGMOD Research Highlights Reward 2020, HKICT Awards 2021, and HKU Knowledge Exchange Award (Engineering) 2021. His work has been recognized through grants such as the HKU-TCL Joint Research Centre for AI-funded project (HKD 1M, 2020–2022) and a CRF-funded project for real-time monitoring of infectious diseases (HKD 6.5M, 2021–2022). Cheng actively contributes to academic service, including serving as PC co-chair for IEEE ICDE 2021 and editorial roles in journals like IS and DAPD. His publications span top venues like SIGMOD, VLDB, and KDD, emphasizing algorithm design for large graphs and probabilistic data systems.
Norman Sadeh is a Professor in the School of Computer Science at Carnegie Mellon University (CMU), where he has made significant contributions to cybersecurity, privacy, and AI research. He has co-founded and co-directed several groundbreaking graduate programs at CMU, including the Privacy Engineering Program (2012-present), the Ph.D. Program in Societal Computing (2003-2013), and the MBA track in Technology Strategy and Product Management (2005-2017). Carnegie Mellon University, School of Computer Science Software and Societal Systems Department CyLab Security and Privacy Institute Manufacturing Futures Institute Dr. Sadeh received his Ph.D. in Computer Science at CMU with a major in Artificial Intelligence and a minor in Operations Research. He holds an M.Sc. in computer science from the University of Southern California and a BS/MS degree in electrical engineering and applied physics from the Free University of Brussels (Belgium) as 'Ingénieur Civil Physicien.' Professor Sadeh's research spans cybersecurity, online privacy, Human-AI Interaction, AI governance, mobile computing, the Internet of Things, user-oriented machine learning, and language technologies. He is particularly known for his pioneering work on AI-based privacy enhancing technologies, including privacy assistants, automated privacy compliance tools, and NLP-based privacy solutions. His work has influenced the design of privacy features at major technology companies including Apple, Google, and Facebook/Meta, as well as privacy policies at regulatory agencies like the Federal Trade Commission and the California Office of the Attorney General. Analysis of his recent publications shows a strong focus on practical privacy solutions, particularly in mobile and IoT contexts, with an emphasis on making privacy more usable and understandable for end users. His work bridges technical innovation with policy implications, addressing both the technological and human aspects of privacy protection. 2018 Outstanding Entrepreneur of the Year award from the Pittsburgh Venture Capital Association Test of time award by the AAAI Conference on Web and Social Media (ICWSM) Gartner Group's Magic Quadrant leader in Security Awareness Computer-Based Training for 4 consecutive years Deloitte's Technology Fast 500 recognition for 3 consecutive years Professor Sadeh has advised numerous students, including PhD candidates like Aerin (Shikhun) Zhang, whose dissertation focused on understanding diverse privacy attitudes. His research has been funded through various grants, including NSF SaTC projects, and has resulted in technologies that protect tens of millions of users worldwide. He also founded Wombat Security Technologies, which was acquired by Proofpoint in 2018 and whose technologies are used by over 75% of Fortune 100 companies. Professor Sadeh leads several research initiatives including the Privacy Engineering Program, the Usable Privacy Policy Project, the Personalized Privacy Assistant Project, and CMU's Privacy Infrastructure for the Internet of Things. His Mobile Commerce Lab and E-Supply Chain Management Lab have produced influential research that has been commercialized by major organizations including IBM, Raytheon, Boeing, and the U.S. Army.
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.
Jonathan Cohen is a Professor of Philosophy at the University of California, San Diego (UCSD), and serves as an Associate Dean in the School of Arts and Humanities. Previously, he held a Killam Postdoctoral Fellowship at the University of British Columbia (2000-2001). He earned his Ph.D. in Philosophy from Rutgers University (2000), and holds a M.A. (1995) and B.A. (1993) in Philosophy and Mathematics from the University of Chicago. His research primarily focuses on the philosophy of perception and language, with a special emphasis on their intersections with cognitive science. Key areas include relationalist theories of color properties, multimodal perception, and the semantics/pragmatics of context-sensitive expressions. Recent work explores perceptual interactions across modalities, synesthesia, and the role of extrasemantic expansion in communication. Cohen’s publications span over two decades, with recent trends emphasizing perceptual architecture, phenomenal contrasts, and interdisciplinary approaches to cognitive penetration. His work often bridges analytic philosophy with empirical findings in psychology and neuroscience. While no formal grants or awards are explicitly noted, his extensive bibliography reflects sustained engagement with foundational questions in philosophy of mind and language. No lab affiliations or student advisees are listed in the provided materials.
Svetlana Lazebnik is a Full Professor and Willett Faculty Scholar in the Department of Computer Science at the University of Illinois at Urbana-Champaign (UIUC), part of the Grainger College of Engineering. She holds a Ph.D. from UIUC (2006) and previously served as an Assistant Professor at the University of North Carolina at Chapel Hill (2007–2011). Her research focuses on computer vision, including generative models for virtual try-on, image stylization, scene understanding, and joint modeling of images and language. She has advised numerous Ph.D. students and postdocs, many of whom now hold prominent academic and industry roles. Education: Ph.D. in Computer Science, UIUC (2006); supervised by Jean Ponce. Research Interests: Her work spans generative adversarial networks (GANs), diffusion models, virtual try-on systems (e.g., Dressing-in-Order, Street Try-On), exemplar-based stylization, and large-scale photo analysis. She has pioneered spatial pyramid matching and contributed to binary code learning for image retrieval. Key Awards: NSF CAREER Award (2008), Microsoft Research Faculty Fellow (2009), Sloan Research Fellow (2013), IEEE Fellow (2021), and the Longuet-Higgins Prize (2016) for her CVPR 2006 paper. Teaching: Recent courses include CS 444 (Deep Learning for Computer Vision), CS 543 (Computer Vision), and a Ph.D. Job Search Seminar. She has also taught at UNC Chapel Hill. Grants & Funding: Supported by NSF, Amazon, AWS, Microsoft, Sloan Foundation, Google, ARO, and Adobe. Notable grants include CCF 2348624 and IIS 1718221. Labs/Groups: Leader in the Illinois CS Vision Group, contributing to collaborative projects on embodied AI, multi-agent systems, and visual-semantic reasoning.
Yoann Demoli is a Lecturer in Sociology at the University of Versailles Saint-Quentin-en-Yvelines (UVSQ), part of the Université Paris-Saclay. He is affiliated with the PRINTEMPS Laboratory (CNRS-UVSQ) and the Quantitative Sociology Laboratory at CREST. His academic career spans over a decade with teaching and research positions at various prestigious French institutions including Sciences Po Paris and Paris 8 University. His educational background includes: Doctoral thesis in sociology under Philippe Coulangeon at Sciences Po Paris (2010-2015) Master 2 in Sociology and Statistics from School for Advanced Studies in Social Sciences and ENS Paris (2009-2010) Master 1 in Sociology from University of Paris IV (2007-2008) Bachelor's degrees in Sociology and Economics from Universities of Paris IV and I (2006-2007) Preparatory classes at Lycée Carnot and Lycée Henri IV (2003-2006) Demoli's research focuses on the sociology of consumption, social stratification, environmental sociology, and particularly the sociology of the automobile. His work employs quantitative methods to analyze how automobile use and ownership patterns reflect and reinforce social inequalities. He examines how factors like class, gender, and geography influence mobility practices, with particular attention to working-class households in peri-urban and rural areas. His research reveals complex relationships between environmental attitudes and actual car use behaviors, challenging simplistic narratives about ecological conversion. His publications demonstrate a consistent focus on the intersection of social structure and mobility practices, with particular attention to historical trends and class analysis. The collection shows a progression from analyzing specific aspects of automobile culture to broader questions of ecological conversion and social stratification in transportation systems. His scientific work has been recognized with the Young Author Prize at the 22nd GERPISA International Conference in 2014. He regularly contributes to academic discourse through peer review for journals such as Acts of Research in Social Sciences and Transport Security Review. As an advisor, Demoli has supervised numerous undergraduate research projects and master's theses on topics ranging from police uniforms to online dating sociology. His current research projects include CONDUIRE (financed by ADEME), a study on magistrates' mobility funded by the Ministry of Justice, and research on the social drivers of ecological conversion coordinated by Philippe Coulangeon. Within the academic community, Demoli co-organizes the workshop 'Quantitative Sociology and Sociology of Quantification' and co-leads the Mobility theme of the Federation for Research in Computer, Human and Social Sciences at UVSQ. He also serves as Director of the Department of Sociology and Geography at UVSQ and is an active participant in research dissemination through media appearances.
