Sarah Billington is the UPS Foundation Professor of Civil and Environmental Engineering at Stanford University and a Senior Fellow at the Woods Institute for the Environment. Her research program focuses on sustainable building design, human wellbeing, and ethical supply chain practices. Education: PhD, University of Texas at Austin, Structural Engineering (1997) MSE, University of Texas at Austin, Structural Engineering (1994) BSE, Princeton University, Civil Engineering & Operations Research (1990) Her lab employs interdisciplinary methods to study how built environments impact human physical, psychological, and social wellbeing. Current projects include developing tools to quantify nature exposure in buildings, exploring affordable housing's role in wellbeing, and using AI to assess forced labor risks in material supply chains. While no longer active in this area, her group previously pioneered research on sustainable construction materials like bio-based composites and ductile cement-based composites. Her recent publications address topics such as nature integration in architecture, workplace design optimization, and health impacts of building materials. The Billington Lab at Stanford fosters collaborative team science, welcoming partnerships to advance occupant-centric engineering solutions.
Brian Y. Lim is an Associate Professor in the Department of Computer Science at the National University of Singapore (NUS), leading the NUS Ubicomp Lab. His research integrates Explainable AI (XAI) , Human-Computer Interaction (HCI) , and Ubiquitous Computing to address societal challenges in healthcare , wellness , and urban sustainability . He holds a Ph.D. and M.S. in Human-Computer Interaction from Carnegie Mellon University and a B.S. in Engineering Physics from Cornell University. Education: B.S., Engineering Physics, Cornell University M.S., Human-Computer Interaction, Carnegie Mellon University Ph.D., Human-Computer Interaction, Carnegie Mellon University His research focuses on designing human-centric AI systems that prioritize transparency and user trust. Key areas include explainable AI algorithms , context-aware computing , data visualization , and trustworthy machine learning . Projects like RexNet and Directed Diversity demonstrate his work in creating relatable AI explanations and enhancing crowd creativity through algorithmic prompting. Recent publications (2022–2025) highlight advancements in faithful visual explanations , user-centric XAI frameworks , and healthcare analytics . Notable awards include the CHI'22 Best Paper Award , IMWUT Distinguished Paper Award , and MOE Outstanding Mentor Award . He has advised numerous PhD and Masters students and leads the NUS Ubicomp Lab, which explores applications like TasteHealthy (food recommendation app) and Food Loves Fellowship (narrative data visualization).
Panos Ipeirotis is a Professor at the Leonard N. Stern School of Business at New York University, affiliated with the Department of Technology, Operations, and Statistics. He also serves as the George A. Kellner Faculty Fellow and is associated with the Center for Data Science and Computer Science departments at NYU. PhD in Computer Science (Columbia University, 2004) MSc in Computer Science (Columbia University, 2001) BSc in Computer Engineering & Informatics (University of Patras, 1999) His research spans crowdsourcing, machine learning, human-AI collaboration, online labor markets, and social media analytics. He pioneered human-machine loop systems that combine human and machine intelligence to achieve superior outcomes. His work has applications in data quality assurance, visual media search (e.g., Google Project Glass), and economic valuation of user-generated content. Recent publications focus on algorithmic fairness in hiring systems, occupational segregation analysis, and theoretical advancements in crowdsourcing consensus mechanisms. Earlier work includes foundational studies on data quality in crowdsourcing platforms, economic impacts of product reviews, and query optimization for text-centric tasks. 2015 Lagrange Prize in Complex Systems NSF CAREER Award SIGKDD Test of Time Award (2020) Multiple Best Paper awards (WWW 2011, KDD 2008, SIGMOD 2006) He has received significant grants, including a $1.5 million Google Research Grant (2013) for integrating crowdsourcing with machine learning algorithms. His work bridges computer science, economics, and social psychology, with implications for policy-making and business strategy.
Snježana Babić serves as Associate Professor and Head of the Graduate Study Program Informatics - Teaching Major at the Faculty of Informatics in Pula , University of Pula. With a PhD obtained in 2016 and university affiliation since 2017, she teaches across undergraduate and graduate programs focusing on ICT fundamentals, web design, digital competencies, and informatics education methodology. Her educational background includes graduation in 1993 followed by doctoral studies culminating in 2016. Though specific institutions aren't detailed in the text, her career trajectory shows progression from foundational ICT teaching to specialized informatics education leadership. Babić's research centers on educational technology adoption with particular emphasis on programming pedagogy for young learners through the MEMA method. Her work investigates digital competencies across multiple educational contexts, from primary schools to higher education. Recent publications demonstrate rapid adaptation to emerging technologies, with 2025 studies examining ChatGPT integration in Croatian educational settings and cross-cultural applications of programming methods. Analysis of her 15 most recent publications reveals consistent focus on technology acceptance models applied to Croatian educators and students. The research trajectory shows evolution from foundational e-learning systems (2016-2020) to cutting-edge AI and robotics applications (2022-2025), maintaining strong methodological focus on teacher motivation , implementation barriers , and culturally-specific adoption factors . Her teaching portfolio demonstrates deep integration with research interests: Undergraduate: ICT Fundamentals, Web Design and Application Graduate: Digital Competencies in Education, Informatics Education Methodology, Practicum for IT Educators Cross-disciplinary: ICT in Nursing, Healthcare Informatics Babić actively contributes to institutional leadership as program head while maintaining robust research productivity. Her work bridges theoretical technology adoption frameworks with practical implementation challenges in Croatian educational contexts, particularly examining how emerging technologies like humanoid robots and AI assistants can be effectively integrated into teaching practices.
Professor Chris Holmes is a Professor of Biostatistics at the University of Oxford, where he moved from Imperial College London in February 2004. He is a Fellow at St Anne's College and works in the Department of Statistics. His research focuses on applications and statistical methods development in genomic sciences and genetic epidemiology, holding a prestigious Programme Leaders Grant in Statistical Genomics from the Medical Research Council. Prior to his position at Oxford, Professor Holmes completed his doctorate in Bayesian statistics at Imperial College London, investigating novel nonlinear pattern recognition methods. This was followed by a post-doctoral position and then a lectureship at Imperial. Before his academic career, he worked in industry for several years in scientific computing, developing techniques for real-time pattern recognition models in defense and SCADA systems. Professor Holmes has a broad interest in the theory, methods and applications of statistics and statistical modeling, with a particular foundation in Bayesian statistics which he views as providing a unified framework for stochastic modeling and information processing. His specific research interests include: Bayesian statistics and stochastic simulation Markov chain Monte Carlo methods Pattern recognition and nonlinear, nonparametric methods Spatial statistics Statistical genetics and genomics Genetic epidemiology His recent publications (2023-2025) demonstrate a strong focus on the intersection of biostatistics, artificial intelligence, and healthcare applications. His work spans multiple domains including AI-driven disease classification in neurology, genomic data analysis for health equity, machine learning tools for healthcare prediction, and addressing bias in medical AI systems. A notable trend across his research is the application of advanced statistical methods to solve pressing problems in genomics, epidemiology, and medical diagnostics, with an increasing emphasis on health equity and the ethical implications of AI in healthcare. Professor Holmes currently supervises PhD students Oscar Clivio, Sahra Ghalebikesabi, and Natalia Garcia Martin. His research is supported by multiple grants, including the MRC Programme Leaders Grant in Statistical Genomics which funds his work in statistical genomics. He is actively involved in three research groups at Oxford that reflect the breadth of his scholarly interests: Computational Statistics and Machine Learning Statistical Genetics and Epidemiology Statistical Theory and Methodology
Prof. Laura Leal-Taixé is a Professor at the Technical University of Munich (TUM) in the Department of Informatics, leading the Dynamic Vision and Learning group. She holds the Rudolf Mößbauer Tenure Track Assistant Professorship, promoted to W3 Associate Professorship in 2022. Her research focuses on computer vision and machine learning, particularly video analysis, multi-object tracking, and autonomous driving applications. Education: B.Sc./M.Sc. in Telecommunications Engineering (Technical University of Catalonia, UPC) Ph.D. in Information Processing (Leibniz University of Hannover, Germany) Postdoctoral Research at ETH Zürich (Switzerland) and TUM Research Interests: Laura’s work addresses challenges in video analysis, including motion analysis, semantic segmentation, and integrating social dynamics into urban traffic modeling. Her project socialMaps , funded by the Sofja Kovalevskaja Award, explores decoupling vehicle and pedestrian traffic using dynamic maps. Her research combines optimization techniques, deep learning, and sensor data for real-world applications like autonomous systems. Recent Trends: Her publications highlight advancements in multi-object tracking, 3D LiDAR segmentation, and trajectory forecasting, emphasizing neural networks and geometric approaches. Collaborations with industry and academia underscore her work’s practical impact. Awards: 2017 Sofja Kovalevskaja Award (€1.65M, Humboldt Foundation) 2017 DAAD Australia-German Joint Research Grant Travel grants from Women in CV, CVPR Doctoral Consortium, and Vodafone Foundation Labs & Projects: Leads the Dynamic Vision and Learning group at TUM, focusing on vision-based AI for autonomous systems and environmental monitoring. Active in developing datasets like DynamicEarthNet for semantic change analysis.
Suhas Diggavi is a Professor in the Department of Electrical and Computer Engineering at the University of California, Los Angeles, within the Henry Samueli School of Engineering and Applied Science. His primary research area is Signals and Systems, with a strong focus on information theory and its interdisciplinary applications. His research interests span Information Theory , Machine Learning , Differential Privacy , Federated Learning , Cyber-Physical Systems , and Bio-informatics . He investigates fundamental limits and practical algorithms for secure, efficient, and robust data processing in distributed and networked environments. The recent publications highlight a strong trend in privacy-preserving machine learning, particularly in the shuffled model of differential privacy , communication-efficient distributed SGD , and robust optimization . His work bridges theoretical information-theoretic foundations with real-world applications in federated learning, wireless networks, and genomic data analysis. Notable scientific awards include: Guggenheim Foundation Fellow (2021) ACM CCS Best Paper Award (2021) IEEE Fellow (2013) IEEE Donald G. Fink Prize Paper Award (2006) Multiple Google, Amazon, and Facebook Research Awards Suhas Diggavi actively advises graduate students and leads a research group focused on learning, information, and optimization. His work is supported by major industry grants and collaborations, particularly in privacy and distributed learning. He has made significant contributions to information-theoretic models in bio-sequencing and wireless security. He leads the LIOS (Learning, Information, Optimization, and Stochastic Systems) research group at UCLA, where his team develops theoretical frameworks and practical algorithms for next-generation data-driven systems.
Yu-Ru Lin is an Associate Professor at the School of Computing and Information, University of Pittsburgh, and serves as Research & Academic Director at the Institute for Cyber Law, Policy and Security (Pitt Cyber). She leads the Pitt Computational Social Dynamics Lab (PICSO Lab) and holds secondary appointments in Political Science, Computer Science, and the Intelligent Systems Program. PhD in Computer Science from Arizona State University Postdoctoral research at Harvard University and Northeastern University Her research focuses on computational approaches for: Networked social dynamics High-dimensional social information summarization Trust and distrust propagation Misinformation detection Policy diffusion analysis Recent publications span 2014-2020, covering: Social media crisis response Graph visualization techniques Policy diffusion patterns Misinformation mitigation Temporal topic modeling Scientific support includes: National Science Foundation (NSF) awards Minerva/ONR funding AFOSR grant for distrust modeling DARPA Understanding Group Biases program As director of PICSO Lab, she leads interdisciplinary research teams on: Digital accountability Urban mobility patterns Health informatics via crowdsourcing Trust-influence dynamics
Kevin D. Ashley is a Professor of Law at the University of Pittsburgh School of Law. He is also a senior scientist at the Learning Research and Development Center and an adjunct professor of computer science at the University of Pittsburgh. His interdisciplinary work bridges artificial intelligence, legal analytics, and ethical reasoning. JD, Harvard Law School M.A. and PhD, University of Massachusetts BA, Princeton University His research focuses on computational modeling of legal reasoning , AI and ethics , legal text analytics , and case-based reasoning . He has pioneered AI applications for legal decision support, automated argumentation, and bias detection in legal corpora. Recent publications include studies on large language models in legal annotation, argument mining, and empirical legal analysis. His work has been supported by multiple National Science Foundation grants, emphasizing fairness in AI and access to justice. 2022 Codex Prize for Computational Law 2015 University of Massachusetts Outstanding Achievement Award 2002 AAAI Fellow for AI in Law contributions 2000 Chancellor’s Distinguished Research Award A former President of the International Association for Artificial Intelligence and Law, Ashley has held visiting roles at the University of Bologna, Stanford CodeX, and IBM Watson Research Center. He co-edits the journal Artificial Intelligence and Law and teaches courses on applied legal analytics.
Elena Agapie is an Assistant Professor in the Department of Informatics at the Donald Bren School of Information & Computer Sciences, University of California, Irvine. Her research lies at the intersection of human-computer interaction (HCI), health, and behavior change, focusing on designing technologies that support individuals in adopting and maintaining healthy behaviors through human-centered approaches. She is affiliated with the Connected Learning Lab and the Institute for Future Health at UCI. Her educational background includes a PhD in Human Centered Design and Engineering from the University of Washington, an MS in Computer Science from Harvard University, and a BS in Computer Science from Jacobs University Bremen in Germany. She has also conducted research at Microsoft Research, FXPAL, Intel Labs, and NASA Jet Propulsion Lab. Elena's research focuses on designing tools that integrate evidence-based behavioral interventions with user needs, particularly in mental health, chronic disease management, and social support systems. She explores how technology can support therapist-client collaboration, enable tailored health interventions, and leverage crowd expertise for personalized behavior change plans. Her work often involves participatory design, qualitative studies, and prototyping of interactive systems for diverse populations, including youth, hospitalized patients, and underserved communities. Her recent publications, primarily in top-tier venues like CHI and CSCW, reflect a strong trend toward social computing in health, longitudinal behavior support, and the design of inclusive digital health tools. Key themes include goal setting, conversational agents, social sharing in health tracking, and cross-disciplinary collaboration in health technology development. Best Paper Award (top 1%) at CHI 2017 Honorable Mention Best Paper Award (top 5%) at CHI 2016 Best Paper Award (top 1%) at HCOMP 2015 Honorable Mention Best Paper Award (top 5%) at CHI 2013 Elena has been funded by the National Science Foundation, National Institutes of Health, and UCI internal grants. She actively mentors PhD students including Bruna Oewel, Meeshu Agnihotri, and Mehreen Masood, and is currently recruiting students interested in HCI, health, and behavior change. She previously led the outreach program in the HCDE department at the University of Washington, creating workshops that introduced K–12 students to engineering and human-centered design, reaching over 2,000 students across public schools and community events. She runs initiatives to broaden participation in computing and advises undergraduate students in outreach and curriculum development. Her lab collaborates with clinical partners and interdisciplinary teams to ensure real-world impact of her research.
Associate Professor Adam Irwin is an NHMRC Emerging Leadership Fellow and Associate Professor in Infectious Diseases at the University of Queensland (UQ) and Queensland Children’s Hospital. He holds positions within the UQ Centre for Clinical Research, Faculty of Health, Medicine and Behavioural Sciences. His work focuses on early sepsis recognition, antimicrobial stewardship, and pediatric infectious diseases. Dr. Irwin completed his medical degree at the University of Birmingham and his PhD in 2016 from the University of Liverpool’s Institute of Infection and Global Health. He has led initiatives like the Queensland Paediatric Sepsis Program, recognized with a Global Sepsis Alliance Award and Queensland Health Excellence Awards. His research spans sepsis pathway implementation, antimicrobial resistance trends, and rapid diagnostic tools. Notable grants include NHMRC funding for studies on multi-drug resistant infections and Children’s Hospital Foundation support for genomics and antimicrobial stewardship projects. He supervises PhD candidates in areas like bloodstream infections and molecular diagnostics. Dr. Irwin collaborates across institutions, contributing to projects such as the RAPIDS Study Group and the PAEDS surveillance network. Awards include the NHMRC Emerging Leadership Fellowship and recognition for sepsis program excellence. His work emphasizes translating research into clinical practice, improving outcomes for critically ill children through evidence-based protocols and innovative diagnostics.
Catherine Lai is a Reader (~Associate Professor) in the Department of Linguistics and English Language at the University of Edinburgh, with strong affiliations to the Centre for Speech Technology Research (CSTR) and the Institute for Language, Cognition and Computation (ILCC) in the School of Informatics. She is based in the School of Philosophy, Psychology and Language Sciences and is actively involved in research, teaching, and academic service. Department: Department of Linguistics and English Language School: School of Philosophy, Psychology and Language Sciences Research Institutes: Centre for Speech Technology Research, Institute for Language, Cognition and Computation Email: C.Lai@ed.ac.uk Her research centers on the role of prosody—non-lexical aspects of speech—in spoken communication. She investigates how prosody contributes to discourse structure, information structure, and affect in dialogue, using interdisciplinary methods from linguistics and machine learning. Her work bridges theoretical linguistics and practical speech technology, aiming to improve spoken language understanding and synthesis systems. She is particularly interested in how prosody shapes listener expectations and how affect and topic are expressed and perceived in conversation. Her recent publications reflect a strong focus on self-supervised learning in speech models, emotion recognition, ASR error correction using large language models, cognitive state classification, and ethical considerations in language technology. She explores topics such as the uncanny valley in synthetic speech, gender expression through voice, and community-centered development of language technologies. Prize from Scopus Profile Catherine Lai has supervised several PhD students, including Leimin Tian and Yuanchao Li, and has been involved in significant research projects, such as a Toyota-funded initiative on spoken dialogue for robot companions. She has secured multiple grants and leads a research agenda that integrates theoretical inquiry with real-world applications in assistive technologies and social science. Her academic service includes organizing major conferences like Interspeech and UK and Ireland Speech. She is a key member of research teams at CSTR and ILCC, collaborating across disciplines to advance the understanding of spoken communication and the development of robust, ethical speech technologies.
Juan Pablo Bello is Professor of Music Technology and Computer Science & Engineering at NYU. He directs the Music and Audio Research Lab (MARL) and the Center for Urban Science and Progress (CUSP). He holds a Ph.D. in Electronic Engineering from Queen Mary University of London (2003) and a B.Eng. from Universidad Simón Bolívar (1998). Research integrates signal processing, machine learning and acoustics to analyze music/audio. Current projects include urban sound mapping, music structure discovery, and audio event detection. Funded by NSF and industry partners, with 100+ publications in IEEE/ACM venues. Honors include an NSF CAREER Award and Fulbright scholarship. He teaches graduate courses in music informatics and fosters industry-academia partnerships in audio AI.
Dr. Oana Cocarascu is a Senior Lecturer in Artificial Intelligence at the Department of Informatics, Faculty of Natural, Mathematical & Engineering Sciences, King's College London. She holds a PhD and MEng in Computing (Artificial Intelligence) from Imperial College London and conducts applied research focusing on how artificial intelligence can be deployed to support real-world applications, with machine learning and natural language processing as core components of her work. Her research interests span argument mining, explainable AI, machine learning, natural language processing, and symbolic reasoning. She is particularly focused on developing AI systems that can provide transparent, accountable, and ethical decision-making processes. Her work addresses critical challenges in bias mitigation, fairness metrics, fact verification systems, and argumentation-based explanations for complex AI decisions. Analysis of her recent publications (2023-2025) reveals a strong focus on fairness in AI systems, with particular attention to individual fairness metrics and nuanced evaluation frameworks. She has made significant contributions to fact verification systems, especially in multimodal contexts involving charts and tabular data. Her work increasingly integrates argumentation theory with natural language processing to create explainable AI systems that can justify their decisions through structured reasoning. Dr. Cocarascu leads an EPSRC-funded project titled 'A framework for evaluating and explaining the robustness of NLP models' (2024-2027) and is actively involved in the Natural Language Processing Group at KCL. Her research fingerprint shows strong activity in argumentation (100%), decision-making (74%), artificial intelligence (50%), explainable AI (39%), and bias mitigation (35%). She has contributed to numerous high-impact publications in top venues including ACL, EMNLP, AAAI, and the Journal of Artificial Intelligence Research, with a particular focus on making AI systems more transparent, accountable, and aligned with human values. Her work intersects with UN Sustainable Development Goals, particularly those related to reducing inequality and building resilient infrastructure.
Associate Professor Kiat Boon, Daniel SENG is a leading academic at the National University of Singapore (NUS), specializing in technology law and infocommunications law. He currently serves as Director of the LLM (Intellectual Property & Technology Law) program and IT Coordinator. Previously, he held roles as Director of Research at the Singapore Academy of Law (2001–2003), partner and head of technology practice at Rajah & Tann, and non-residential fellow at Stanford Law School’s Center for Legal Informatics (CodeX). His education includes a JSD and JSM from Stanford University, BCL (Oxford, Rupert Cross Prize recipient, 1994), and LLB (NUS with first-class honors). His research employs machine learning, NLP, and big data to analyze digital law challenges, particularly in copyright takedown notices and AI governance. Research interests span information technology law, empirical legal studies, quantitative research, and AI’s intersection with legal reasoning. He contributes to global legal reform via advisory roles at the World Intellectual Property Organization (WIPO), focusing on copyright exceptions, music licensing, and intermediary liability. Key awards include the 1994 Rupert Cross Prize. His work bridges law and technology through publications on digital evidence, metaverse legal frameworks, and AI’s impact on private law. He is actively involved in shaping Singapore’s legal infrastructure through government committees and academic initiatives.
