Dr. George Cantwell is an Assistant Professor in the Department of Engineering at the University of Cambridge, affiliated with Cambridge Infectious Diseases. He specializes in computational methods for inference problems, particularly in disease spreading across networks. Education: PhD in Physics from the University of Michigan; postdoctoral fellowship at the Santa Fe Institute His research focuses on network science , complex systems , and statistical inference , with an emphasis on computational approaches. His work spans theoretical and applied domains, including: Message passing algorithms for heterogeneous networks Bias correction in social network analysis (friendship paradox) Statistical inference of network structure from noisy data Modeling judicial voting behavior through network interactions Computational cognitive neuroscience of category learning Recent publications highlight interdisciplinary applications in epidemiology, physics, and cognitive science. He actively mentors students in networks, complex systems, and statistical inference.
Prof. Sadettin Emre Alptekin is a full Professor of Industrial Engineering at Galatasaray University, Faculty of Engineering and Technology, where he also serves as Vice Dean. Since joining the university as a research assistant in 2000, he has steadily advanced through the academic ranks, becoming an Assistant Professor (2006–2010), Associate Professor (2010–2023), and finally Professor in 2023. Education: PhD (Dr), Industrial Engineering, Istanbul Technical University, Institute of Science and Technology, 2001–2006 MSc, Industrial Engineering, Galatasaray University, Faculty of Engineering and Technology, 1999–2001 BSc, Industrial Engineering, Istanbul Technical University, Faculty of Management, 1995–1999 Languages: Advanced English (C1), Upper-Intermediate French (B2), Advanced German (C1) Research Interests: Prof. Alptekin’s research focuses on Computer Learning , Fuzzy Sets and Systems , and Decision Support Systems . His work integrates artificial intelligence, machine learning, and soft-computing techniques to solve complex industrial and managerial problems in areas such as supply chain management, quality function deployment, blockchain adoption, and mental-health prediction. Publication Trends: Across more than 50 refereed publications, Prof. Alptekin has consistently explored hybrid intelligent models that combine fuzzy logic, machine learning, and multi-criteria decision-making. Recent articles emphasize deep-learning-based anomaly detection in industrial time-series data, blockchain adoption in supply chains, and machine-learning applications in subjective well-being and mental-health modeling. Scientific Awards & Honors: No specific awards or medals are listed in the provided documents. Research Leadership & Funding: Since 2008 he has been the principal investigator (executive) of 12 nationally funded projects, covering topics such as Industry 4.0 sub-system design, Internet of Things applications, artificial neural networks in organizational decision-making, big-data analytics, and strategic decision processes. Graduate Advising: He has formally supervised at least 8 master’s theses and numerous undergraduate projects. Representative thesis titles include Gaussian-process-regression-based man-hour prediction, machine-learning-driven human-behavior modeling, recommender-system design for e-commerce, thyroid-nodule diagnosis from scintigraphic images, software-effort estimation via neural networks, spreadsheet heuristics for joint-replenishment problems, cross-selling decision systems in insurance, and profitability analyses of Turkish banks under disinflation. Laboratories & Teams: While no dedicated laboratory name is disclosed, his continuous role as Vice Dean and principal investigator implies active leadership of the Industrial Engineering department’s research clusters in intelligent systems and decision support technologies.
Dr. Alastair Key serves as Director of Studies in Archaeology and Official Fellow in Archaeology at Queens' College, University of Cambridge. His research bridges Paleolithic archaeology, stone tool technology, and hominin behavioral evolution through experimental and computational approaches. Director of Studies and Official Fellow at Queens' College, Cambridge Specializes in Paleolithic stone tool analysis, Acheulean technology, and hominin adaptation Conducts experimental archaeology and computational modelling to assess tool functionality Key's research focuses on Acheulean handaxe production , lithic microwear patterns , and ergonomic constraints in prehistoric tool use . He has extensively published on topics including glacial-stage hominin occupations , Oldowan toolmakers , and machine learning applications to archaeological analysis . His recent publications (2025-2023) span diverse subfields: Acheulean chronology , hominin tool use biomechanics , experimental projectile testing , and computational morphometric methods . The work often integrates multidisciplinary datasets and open-source analytical tools to address fundamental questions about human technological evolution. Current research directions include stone tool sharpness quantification , handaxe social signaling potential , and cross-species tool use comparisons through primate studies.
Michele Gelfand is a Professor of Organizational Behavior at the Stanford Graduate School of Business and Professor of Psychology by courtesy at Stanford University . She is renowned for her research on cross-cultural organizational behavior, negotiation, conflict management, and cultural psychology. Her work explores the concept of tight and loose cultures and their impact on global leadership, organizational dynamics, and societal responses to threats. PhD in Social and Organizational Psychology, University of Illinois, Urbana-Champaign (1996) Her research spans interdisciplinary approaches, integrating computational methods, neuroscience, and behavioral science to understand cultural evolution and its multilevel consequences. She has led groundbreaking studies on the effects of cultural tightness-looseness on political polarization, pandemic responses, and corporate mergers. Recent publications highlight her work on social norm dynamics, threat-driven cultural adaptation, and the psychological underpinnings of political and terrorist ideologies. Her articles frequently appear in top journals such as Science , Nature Human Behaviour , and Proceedings of the National Academy of Sciences . Notable scientific awards include the 2020 Katzell Award , 2016 Diener Award , and 2011 Anneliese Maier Research Award . She has also been elected to the National Academy of Sciences and the American Academy of Arts and Sciences . Gelfand’s Culture Lab at Stanford investigates topics like cultural norms, revenge, forgiveness, and diversity. She actively mentors postdoctoral researchers and serves as an editor for the Oxford Handbook of Cross-Cultural Management . Her executive education programs focus on global leadership and negotiation strategies.
David Lindlbauer is an Assistant Professor at the Human-Computer Interaction Institute (HCII) within Carnegie Mellon University's School of Computer Science. He leads the Augmented Perception Lab and co-directs the CMU Extended Reality Technology Center (XRTC) . His research bridges Human-Computer Interaction, Computer Graphics, and Computer Vision to create adaptive interfaces that enhance human-digital interaction. Education : PhD (summa cum laude) from Technische Universität Berlin , MSc and BSc from University of Applied Sciences Upper Austria Previous Affiliation : Postdoctoral Researcher at ETH Zurich (2018-2020) David's research focuses on understanding human perception of digital information and developing computational approaches to optimize AR/VR interface usability. Key areas include: Context-aware adaptive interfaces Visual saliency and attention modeling Spatial audio-haptic systems Optimal placement algorithms Object manipulation in Remixed Reality Diminished/ambient MR interfaces His 15 most recent publications (2024-2025) span topics in adaptive XR interfaces, multimodal notifications, haptic systems, and spatial cognition. These works appear at venues like ACM CHI, ACM UIST, IEEE VR, and Frontiers in VR. Common themes include: Machine learning for interface adaptation Human factors in XR design Real-time environment analysis Privacy-aware display systems Collaborative MR interfaces Accessibility enhancements Scientific Recognition : Best Paper Honorable Mention Award (ACM CHI 2024) Best Paper Award (ACM ISS 2023) ETH Zurich Postdoctoral Fellowship Multiple best paper recognitions at CHI, UIST, and IEEE VR Teaching & Leadership : Course developer for CMU's "Interactive Extended Reality" Mentor for NASA SUITS Challenge team Co-chair roles at CHI and UIST Overseeing PhD students and research interns
Barbara Plank is a full professor and chair for AI and Computational Linguistics at Ludwig Maximilian University of Munich (LMU), where she heads the Munich AI and NLP (MaiNLP) lab and co-directs the Center for Information and Language Processing (CIS). She additionally serves as a visiting full professor at the IT University of Copenhagen, maintaining active dual institutional affiliations in computational linguistics and NLP research. Her research focuses on human-centric natural language processing challenges, particularly learning under sample selection bias (domain adaptation, transfer learning) and annotation bias, learning with limited data through continual/semi-supervised/weakly-supervised methods, multimodal learning at language-vision-speech interfaces, and fortuitous supervision for variety-space aware language understanding. She pioneers methodologies addressing human label variation as a critical factor in model robustness rather than mere noise. Recent publications (2024-2025) reveal dominant trends in modeling human label variation across NLP tasks, especially natural language inference and entity recognition, alongside dialectal language processing and LLM evaluation frameworks. Her work systematically investigates how human disagreement in annotations can be leveraged to build more robust, adaptable systems rather than treated as errors. Scientific recognition includes: ERC Consolidator Grant for the DIALECT project advancing natural language understanding for non-standard languages and dialects ACL 2024 Area Chair Award for the paper 'VariErr NLI: Separating Annotation Error from Human Label Variation' Leading the MaiNLP lab at CIS (LMU), she directs research integrated with MCML (Munich Center for Machine Learning), Munich Intelligent Robotics, ELLIS Unit Munich, UniDive, and COST action. Current projects include ERC-funded DIALECT and KLIMA-MEMES, focusing on human-facing NLP solutions for real-world language diversity challenges. She actively shapes the field through ACL leadership as VP-Elect and numerous keynotes emphasizing human-centric approaches. The MaiNLP lab at Akademiestr. 7, 80799 Munich, drives innovation in computational linguistics through interdisciplinary collaboration, maintaining strong ties with European research networks while developing practical applications for language variation and robust NLP systems. The lab's work directly informs her teaching in LMU's Computational Linguistics programs, bridging research and education in cutting-edge NLP methodologies.
Elizabeth Phelps is the Pershing Square Professor of Human Neuroscience in the Department of Psychology at Harvard University's Faculty of Arts and Sciences. She directs the Phelps Lab, which investigates how emotions influence learning, memory, and decision-making using multidisciplinary approaches including behavioral studies, neuroimaging (fMRI), physiological measurements, and computational modeling. The lab collaborates widely across psychology, neuroscience, economics, and clinical disciplines. Her research examines: Human neuroscience of affect and cognition interactions Emotional modulation of learning and memory systems Neural mechanisms of decision-making under uncertainty Impact of emotion on social cognition and behavior Translational applications for psychological disorders Contact information: Email: phelps@fas.harvard.edu Lab email: phelpslab@fas.harvard.edu Address: Northwest Lab Building, 52 Oxford Street, Cambridge, MA 02138 The lab welcomes study participants and research assistant applicants, emphasizing diversity and inclusion in research.
Benjamin Alarie is a Professor and the Osler Chair in Business Law at the University of Toronto Faculty of Law. He is a leading scholar at the intersection of artificial intelligence, law, and taxation, and the co-founder and CEO of Blue J, an AI-powered tax research platform used by over 3,000 organizations across North America and the UK. Education: LL.M. – Yale Law School (2003) J.D. – University of Toronto (2002), with honours M.A. – University of Toronto (2002) B.A. – Wilfrid Laurier University (1999), with high distinction Research Interests: Alarie’s research spans tax law , legal theory , judicial behaviour , and the applications of machine learning in law . He introduced the concept of the “legal singularity”—the idea that legal systems may evolve toward greater clarity and predictability through AI and computational methods. His work explores how AI can enhance legal reasoning, regulatory design, and tax compliance. He is particularly focused on the ethical and practical implications of generative AI in legal and tax practice, including liability, transparency, and the future role of human practitioners in AI-augmented systems. Scientific Awards: PROSE Award (2024) for The Legal Singularity Donner Prize shortlist (2024) for The Legal Singularity vLex Fastcase 50 Honoree (2023) Top 50 Changemaker, The Globe and Mail (2022) Alan Mewett QC Prize for Excellence (2009) Advising & Grants: Alarie is a frequent advisor to courts, government agencies, and global professional networks. His work at Blue J involves strategic partnerships with CPA.com, CPA Canada, the National Association of Tax Professionals, Big 4 accounting firms, and Tax Notes. He also serves as a faculty affiliate at the Vector Institute for AI and the Schwartz Reisman Institute for Technology and Society. Labs & Teams: As CEO of Blue J, Alarie leads a multidisciplinary team of legal scholars, engineers, and data scientists. The platform is used by tax authorities and accounting firms to deliver precise, defensible, and efficient tax research and decision-making tools.
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.
Professor Vinayak Dixit serves as the IAG Chair of Risk in Smart Cities and Director of the Research Centre for Integrated Transport Innovation (rCITI) at the University of New South Wales (UNSW), within the School of Civil and Environmental Engineering. With a distinguished career spanning academia and research leadership, Professor Dixit has established himself as a leading expert in transportation risk analysis and smart city infrastructure. Professor Dixit's research focuses on studying risk in transportation infrastructure systems, with particular emphasis on highway safety, travel time uncertainty, and resilience against natural and man-made disasters. His work integrates cutting-edge approaches including quantum computing applications for transportation network optimization, analysis of connected and automated vehicles, and development of models for transportation resilience. His research interests span multiple disciplines, bridging civil engineering, transportation science, risk analysis, and computational methods. Professor Dixit has secured significant research funding from prestigious organizations including the United States National Science Foundation, Federal Highway Administration, and the Strategic Highway Research Program of the Transportation Research Board. His leadership extends to directing the Research Centre for Integrated Transport Innovation (rCITI), where he oversees a comprehensive research program addressing critical transportation challenges in smart cities through multiple focus areas including Connected Mobility Services, Deep Data and Digitization, Engineering Smart Cities & Logistics, Human-Centred And Automated Systems Design, and Integrated Infrastructure Strategic Planning. Professor Dixit previously served as the Associate Director of Research for the Gulf Coast Centre for Evacuation and Transportation Resiliency at Louisiana State University, where he founded the Driving Simulator Laboratory in collaboration with other faculty members. This demonstrates his longstanding commitment to innovative research infrastructure development and interdisciplinary collaboration across engineering, economics, computer science, and urban planning to address complex transportation challenges in the 21st century.
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.
Professor George Siemens is a leading academic in the field of learning analytics and AI-driven education, serving as Professor and Director of the Centre for Change and Complexity in Learning at UniSA Education Futures, University of South Australia. His work focuses on advancing educational practices through data analytics, artificial intelligence, and understanding online learning dynamics. His research spans MOOCs, social and emotional learning analytics, and the ethical integration of AI in education. Notable contributions include the development of frameworks like the MOOC Replication Framework (MORF) and the DAIR infrastructure for educational AI research. Key publications include studies on student agency in AI environments, practicum effectiveness in teacher education, and synthetic data fairness in learning analytics. He collaborates internationally, with affiliations previously including the University of Texas Arlington. As a Research Degree Supervisor, he guides students in transformative educational technology research. His work emphasizes actionable intelligence for educators and scalable solutions for lifelong learning in the digital age.
Nicholas Mattei is an Associate Professor of Computer Science at Tulane University and Co-Director of the Tulane Center for Community Engaged AI. He holds a Ph.D. from the University of Kentucky (2012) and researches artificial intelligence, machine learning, and decision-making systems. His work combines theory, data, and experiments to develop algorithms supporting individual and group decision-making. Dr. Mattei's research spans AI ethics, fairness in algorithms, computational social choice, and preference learning. He has published over 100 academic articles and received multiple grants from organizations including Google, IBM, and the National Science Foundation, including a 2024 NSF CAREER Award. He co-authored 'Computing and Technology Ethics: Engaging Through Science Fiction' from MIT Press. Prior to joining Tulane, he held research positions at IBM Research, Data61/CSIRO, and NASA Ames Research Center. His teaching portfolio includes courses on Discrete Mathematics, Data Science, Artificial Intelligence, and Multi-agent Systems.
Miguel Nacenta is a Professor in the Department of Computer Science at the University of Victoria (UVic), Canada, and a founding member of the Victoria Interactive eXperiences with Information (VIXI) research group. Previously affiliated with the University of St Andrews (UK), his work bridges Human-Computer Interaction (HCI), Information Visualization, and Cognitive Science. He specializes in designing interactive systems that enhance human cognition, with a focus on Infotypography (using typography to encode data), collaborative problem-solving tools, and perceptual input/output devices. Research Interests: His key areas include cognitive augmentation, visualization techniques for complex tasks, multi-display environments, and tools for constraint problem-solving. Notable projects include the WriteReason tool for essay writing, InfoTypography studies on perceptual typographic parameters, and Solvi for visual constraint modeling. Grants & Collaborations: He collaborates internationally, including with the University of St Andrews on PhD scholarship programs. His work is supported by grants focusing on HCI innovations and accessibility. He actively mentors students (e.g., Adam Binks, Johannes Lang) and supervises postdoctoral researchers. Affiliations: Member of the VIXI group,他曾是St Andrews计算机科学学院的教授, 并参与多个学术服务活动, including conference program committees and journal reviews. Labs & Teams: Leads the VIXI lab at UVic, focusing on interactive technologies for cognitive tasks. Collaborates with industry partners on projects like TypoCartographer for infoTypographic maps and HaptiQ for accessible graph exploration.
Professor Maia Angelova is a leading academic in data science and mathematical physics at Aston University's Aston Digital Futures Institute (ADFI) and College of Engineering and Physical Sciences. Her research focuses on interdisciplinary AI applications in healthcare, including precision medicine, chronic disease modeling, and athlete performance analytics. She previously held roles as Professor of Data Analytics at Deakin University (2017–2023) and Professor of Mathematical Physics at Northumbria University (1997–2016), with early experience as a College Lecturer at Oxford University (1991–1996). Education: PhD, MSc, and BSc in Physics from Sofia University 'St. Kliment Ohridski'. Research interests span AI-driven healthcare solutions, dynamical systems modeling, and sports performance analysis. Her work addresses sleep disorders, diabetes management, chronic pain, and athlete performance using advanced machine learning and data analytics. She has secured over £5M in research funding and supervised over 30 PhD students and postdoctoral researchers. Awards include Fellowship of The Institute of Physics. Professional memberships include The London Mathematical Society, Australian Mathematical Society, and Complex Systems Society. Key achievements include founding the Data to Intelligence research centre (2018–2020) and leading large-scale interdisciplinary projects. Current initiatives focus on precision healthcare through AI integration in clinical decision-making systems.
