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Suzie DunnView profile

Assistant Professor

• Technology and Law
• Gender Equality
• Digital Privacy
• +4 more

Suzie Dunn is an Assistant Professor at Dalhousie University's Schulich School of Law and Acting Director of the Law & Technology Institute. She teaches Contracts, Judicial Decision-Making, Law and Technology, Intellectual Property, and Legal Ethics. Her research focuses on the intersection of gender, equality, technology, and law, particularly technology-facilitated violence, deepfakes, and digital privacy. Education: PhD candidate at the University of Ottawa Faculty of Law, with JD, LLM, and BA degrees. She previously served as a part-time professor at the University of Ottawa, where she taught Contracts and Law of Images, earning a 2021 teaching excellence award. Awards include the Joseph-Armand Bombardier Scholarship for PhD research and the Greenberg Prize for Feminist Research. Professional roles include advising the G7 and UN on gender-based violence in digital contexts, contributing to CIPPIC interventions in landmark Supreme Court cases (R v Jarvis, R v Downes), and serving on advisory boards for organizations like the Women’s Legal Education and Action Fund. She is a Senior Fellow at CIGI, working on global projects against online gender-based violence, and co-authored a UN resolution on digital violence prevention. Her work extends to tech safety toolkits for domestic violence shelters, policy development, and international collaborations. She was called to the Ontario bar in 2016 and actively engages in public discourse on AI ethics and digital rights.

Kamran SedigView profile

Professor

• Human-Computer Interaction
• Information Visualization
• Cognitive Technologies
• +3 more

Dr. Kamran Sedig serves as a Professor in the Department of Computer Science and the Faculty of Information and Media Studies at Western University, where he directs the Insight Lab. His research focuses on designing interactive technologies to enhance human cognitive activities involving data and information, including decision making, problem solving, and learning across domains like healthcare, finance, and scientific discovery. His academic credentials include: Ph.D. in Computer Science (Human-Computer Interaction) from The University of British Columbia under Prof. Maria Klawe, with dissertation nominated for the Governor General’s Gold Medal M.Sc. in Computer Science (Artificial Intelligence) from McGill University under Prof. Renato De Mori B.Sc. in Computer Engineering and Science from Concordia University as Valedictorian with The Most Great Distinction Sedig’s research synthesizes computer science, information science, cognition theory, and game studies to develop frameworks for interactive visual tools (IVTs). He investigates human-data interaction, visual reasoning, and interactivity design to support complex cognitive tasks like medical diagnosis, financial analysis, and scientific exploration. His human-centered approach emphasizes how computational tools and humans form coordinated cognitive systems for optimal task execution. Analysis of his recent publications reveals dominant trends in health informatics applications (drug safety analytics, electronic health records) and foundational work on human-information interaction frameworks. His visual analytics systems consistently bridge theoretical models with practical tools for ontology exploration, document triage, and explainable AI, demonstrating strong interdisciplinary collaboration across medical and computational domains. Key recognitions include: Governor General’s Gold Medal nomination for doctoral research Valedictorian honors at Concordia University As Insight Lab director, Sedig mentors graduate students through courses like Human-Computer Interaction, Information Visualization, and Design of Digital Cognitive Games. His teaching philosophy emphasizes how cognitive technologies mediate human thinking processes in professional and private contexts. While specific grant details aren’t provided, his lab’s sustained output in health analytics and visual interfaces indicates robust research funding. The Insight Lab operates as a collaborative hub for developing and evaluating IVTs, with current projects including VICTORIOUS for document scoping reviews and VISEMURE for multimorbidity analysis. Sedig’s team prioritizes empirical validation of how interaction design affects cognitive load and task efficiency in real-world data-intensive environments.

Elisa KreissView profile

Assistant Professor

• Natural Language Processing
• Psycholinguistics
• Human-Computer Interaction
• +3 more

Elisa Kreiss is an Assistant Professor in the Department of Communication at UCLA, affiliated with the College of Letters & Science. She leads the Coalas Lab (Computation and Language for Society Lab), focusing on advancing understanding of how communicative context shapes language use through natural language processing, psycholinguistics, and human-computer interaction. Her work addresses challenges in image accessibility for visually impaired users, supported by grants from Google, NSF, and Stanford initiatives. Education: Ph.D. in Linguistics, Stanford University (advised by Christopher Potts) Research Interests: Her research bridges AI ethics, image accessibility, and human-centered evaluation of machine learning systems. Key themes include: Generating context-aware image descriptions for accessibility Ethical implications of vision-language models Interpretable AI through causal reasoning Awards: Google Research Awards National Science Foundation Grant Stanford Human-Centered AI Initiative Support Stanford Community Impact Award (2022) Lab & Advocacy: Directs the Coalas Lab, emphasizing inclusive research environments. Advocates for diversity in STEM and accessible technology design.

Stefano GalelliView profile

Associate Professor

• Environmental and Water Resources Systems Engineering
• Water Systems
• Energy and the Environment
• +6 more

Stefano Galelli is a tenured Associate Professor in the School of Civil and Environmental Engineering at Cornell University, where he leads the Critical Infrastructure Systems Lab. He also holds an adjunct position as a Research Scientist at the Lamont-Doherty Earth Observatory, Columbia University. His career spans roles in Singapore, including a Postdoctoral Research Fellow at NUS (2011–2013) and faculty at the Singapore University of Technology and Design (2013–2023). Dr. Galelli earned his B.Sc. (2004), M.Sc. (2007), and Ph.D. (2011) in Environmental and Land Planning Engineering and Information Technology from Politecnico di Milano, Italy. His research focuses on the interactions between critical infrastructure systems and natural environments, emphasizing adaptive management solutions for water-energy systems. Techniques include process-based modeling, climatology, statistical learning, control theory, and optimization. He explores topics like hydro-climatic variability impacts, dam re-operation for environmental flows, and cyber-physical security in infrastructure. His contributions to journals such as Nature Sustainability, Earth’s Future, and Environmental Modelling & Software have earned him multiple awards, including the Early Career Research Excellence Award (2014) and SUTD Excellence in Research Award (2017). He has served as an editor for several journals and is recognized for advancing interdisciplinary approaches to water-energy nexus challenges. Teaching highlights include foundational mathematics courses and advanced topics in data analytics, optimization, and water-energy management. He is developing new courses on data-driven control of coupled human-natural systems and risk management for interconnected systems.

Michael FullerView profile

Lecturer

• Science and Religion
• Theological Ethics
• Theology of Music and Literature
• +3 more

Rev Dr Michael Fuller is a Lecturer in Science and Religion at the University of Edinburgh's School of Divinity. He holds degrees in Chemistry (MA and DPhil from Oxford) and Theology (BA from Cambridge), with additional ordination in the Scottish Episcopal Church. His work bridges science-religion dialogue, theological analysis of literature and music, and ethical considerations of emerging technologies like big data. Qualifications include: MA Chemistry (Oxford, 1989) DPhil Chemistry (Oxford, 1989) BA Theology (Cambridge, 1991) Fellow of the Higher Education Academy (2010) Fellow of the International Society for Science and Religion (2016) Research interests emphasize interdisciplinary dialogue between scientific disciplines and religious thought, particularly exploring human uniqueness, ethics of technology, and theological interpretation in music and literature. Recent work examines big data's ethical implications and Wagnerian redemption narratives. Key affiliations include: Vice-President for Publications, European Society for the Study of Science and Theology International Advisory Board member for Zagreb University's CePOZiR Centre Former Chair of the Science and Religion Forum Publications include over ten edited volumes and numerous articles on science-religion interface topics. His work often addresses public intellectualism, advocating for constructive dialogue between scientific and religious communities.

Hilary GreavesView profile

Professor

• Ethics
• Formal Epistemology
• Philosophy of Physics
• +3 more

Hilary Greaves is a Professor of Philosophy at the University of Oxford and Supernumerary Fellow at Merton College. She holds visiting appointments at the Institute for Futures Studies (Stockholm) and the University of Michigan. Her research focuses on ethics, formal epistemology, and philosophy of physics, with particular emphasis on consequentialism, population ethics, effective altruism, and AI ethics. She previously directed the Global Priorities Institute (2018–2022). Education: PhD in Philosophy (Rutgers University, 2008); BA in Physics and Philosophy (Oxford, 2003, First Class Honors). Awards include the Leverhulme Research Fellowship (2018–2020) and the British Academy Rising Star Engagement Award (2015–2017). Research interests span moral philosophy, including aggregation debates, existential risk analysis, and interdisciplinary work with economics. She has published extensively in journals like Philosophy and Phenomenological Research and Mind , and contributed to edited volumes on consequentialism and population ethics. Teaching includes graduate courses on effective altruism, population ethics, and undergraduate modules in moral philosophy and philosophy of physics. She has supervised eight DPhil theses and organized workshops on global priorities research and population ethics. Grants include a £1.2M Leverhulme Trust project on population ethics (2014–2018) and collaborations with the Oxford Martin School on human rights for future generations. Her lab, the Global Priorities Institute, focuses on longtermist ethical frameworks and policy applications.

Zhibo PangView profile

Adjunct Professor

• Embodied Intelligence
• Artificial Intelligence/ML
• Robotics
• +7 more

Zhibo Pang is an Adjunct Professor at KTH Royal Institute of Technology's Department of Intelligent Systems (EECS) and Senior Principal Scientist at ABB Corporate Research Sweden. His work focuses on digital transformation in industry and healthcare, spanning robotics, AI, control systems, and wireless communication. He leads projects in embodied intelligence, Industry 4.0, and Healthcare 4.0, with 23 granted patents and over 120 journal papers. Education: PhD in Electronic and Computer Systems (KTH, 2013), MBA in Innovation & Growth (University of Turku, 2012). Key Roles: IEEE Technical Committee Chair, Editor of 6 IEEE journals, ABB Inventor of the Year (2016, 2018, 2021). Research Interests: Robotics safety, wireless automation, federated learning, digital twins, and IoT security. Recent Projects: Cloud-fog automation frameworks, robot skin systems for healthcare, and latency-aware industrial control. His work bridges academia and industry through cross-functional collaborations.

• Machine Learning
• Generative Artificial Intelligence
• Computational Models of Human Behavior
• +3 more

Mirco Musolesi is a Full Professor of Computer Science at both University College London (UCL) and the University of Bologna. He leads the Machine Intelligence Lab at UCL, part of the UCL Centre for Artificial Intelligence. His research focuses on Machine Learning, Generative AI, and computational models of human behavior, with applications in ubiquitous systems and societal impacts of AI. Education: PhD in Computer Science from UCL (2007) and Laurea in Electronic Engineering from the University of Bologna (2002). Previous roles include positions at the University of Birmingham, Dartmouth College, and the Alan Turing Institute. Research spans multi-agent systems, reinforcement learning, and AI ethics. Notable awards include ACM UbiComp 10-Year Impact Award (2020/2024) and the NetExplorateur/UNESCO Top 100 Innovations (2011). His work on EmotionSense and CenceMe applications has been recognized with Test-of-Time awards. Recent publications (2024-2025) address moral alignment in AI agents, multi-agent environmental policy simulations, and creativity in LLMs. His labs explore AI-driven solutions for urban systems and ethical decision-making frameworks.

Sudha RamView profile

Professor

• Big Data Analytics
• Business Intelligence
• Large Scale Network Science
• +12 more

Sudha Ram is the Anheuser-Busch Endowed Professor of MIS, Entrepreneurship & Innovation at the Eller College of Management, University of Arizona. She holds joint faculty appointments as Professor of Computer Science and is a member of the BIO5 Institute and the Institute for the Environment. She is also the Director of INSITE: Center for Business Intelligence and Analytics, a leading research center in data-driven decision-making. Her research focuses on Big Data Analytics , Business Intelligence , Large Scale Network Science , and Machine Learning , with applications in healthcare, smart cities, environmental policy, and social media. She has pioneered methods in explainable AI, conceptual modeling, and multimodal data fusion, integrating statistical, ontological, and machine learning approaches. Recent publications demonstrate a strong trend in healthcare analytics (e.g., asthma, diabetes, fracture prediction), explainable AI (ROLEX, argumentation-based models), and urban/smart systems (mobility, wearables, environmental impact). Her work consistently appears in top-tier journals and conferences, reflecting sustained scholarly impact. AIS Fellow (2018) INFORMS ISS Distinguished Fellow IBM Faculty Award Peter Chen Award Best Paper Award, IEEE Smart Cities (2016) Best Paper Award, ACM Digital Health (2016) Woman of Impact Award, University of Arizona (2023) Dr. Ram has secured over $70 million in research funding from agencies like NSF, NASA, CIA, and corporations including IBM, Intel, and SAP. She has mentored numerous students and leads a multidisciplinary research team at INSITE. She has held editorial leadership roles in Information Systems Research , Journal of AIS , and is founding co-editor of the Journal of Business Analytics . She directs the INSITE Center, which fosters collaboration across business, computer science, and health domains, enabling large-scale data synthesis and knowledge discovery. The center supports projects in healthcare innovation, smart cities, and environmental policy analytics.

Monica LamView profile

Professor

• Artificial Intelligence
• Privacy-Preserving Technologies
• Compiler Design
• +4 more

Monica Lam is the Kleiner Perkins, Mayfield, Sequoia Capital Professor in Stanford University's School of Engineering and holds a courtesy professorship in Electrical Engineering. She leads the Stanford Open Virtual Assistant Laboratory and has pioneered work in virtual assistants, privacy protection, and compiler design. Her research includes the Almond virtual assistant, privacy-preserving IoT systems, and the ThingTalk programming language. She co-authored the seminal 'dragon book' on compilers and co-founded Tensilica (now part of Cadence). Education: Bachelor of Science (Honors), Computer Science, University of British Columbia, 1980 Master of Science, Computer Science, Carnegie Mellon University, 1982 Doctor of Philosophy (PhD), Computer Science, Carnegie Mellon University, 1987 Research Interests: Dr. Lam's work focuses on conversational AI with privacy guarantees, compiler optimization for parallel computing, and open-source virtual assistant ecosystems. She is a leader in decentralized systems, having developed frameworks like SociaLite for large-scale graph analysis and Musubi for mobile social networking without centralized platforms. Article Trends: Recent publications emphasize multimodal interactions, multilingual dialogue systems, and LLM-driven applications in areas like question answering, persuasive chatbots, and adaptive assistants. Her work bridges foundational AI research (e.g., semantic parsing) with real-world deployments (e.g., privacy-compliant IoT). Awards & Honors: Member of the National Academy of Engineering ACM Fellow Popular Science's Best of What's New Award (Security, 2019) Advising & Grants: Lam oversees the Open Virtual Assistant Initiative, a collaborative project to build open-source semantic models. Her NSF CNS grant (CNS Core) focuses on federated privacy systems. She has advised over 50 students in AI and systems research, though specific names are not listed in the provided texts. Labs & Teams: Directs the Stanford Open Virtual Assistant Laboratory, collaborates with the Stanford NLP group, and maintains ties to industry through former startup Tensilica's legacy in embedded processors.

Kari IngstadView profile

Professor

• Healthcare Management
• Sociology
• Health Services Organization
• +4 more

Kari Ingstad is a Professor of Sociology at the Faculty of Nursing and Health Sciences, Nord University, and currently serves as Vice-Dean for Research. Her work bridges academia, clinical practice, and policy, focusing on healthcare organization, leadership, and innovation in Norway's health sector. She holds a PhD in Sociology from NTNU (2011) and has extensive clinical experience in municipal and specialist healthcare. Education: PhD in Sociology, NTNU, 2011 Nursing background from municipal and specialist healthcare sectors Research Interests: Workforce planning and staffing strategies in healthcare Impact of shift scheduling on employee well-being and patient safety Integration of artificial intelligence in healthcare operations Cross-sectoral collaboration and innovation in health services Recent Research Trends: Her 2024 publications emphasize strategic staffing solutions, AI-driven shift planning, and long-shift work-life balance. Key projects include the OptiCare-AI initiative (Norwegian Research Council-funded), exploring AI applications for optimizing healthcare resource allocation. Leadership & Grants: Leads the Acute Care, Innovation and Patient Safety (AKIP) research group Principal Investigator on OptiCare-AI (2023–present) Labs/Teams: Active collaborations with the AKIP group and national/international networks on healthcare workforce issues.

Necmiye OzayView profile

Associate Professor

• Control Systems
• Optimization
• Formal Methods
• +5 more

Necmiye Ozay is an Associate Professor in Robotics and Electrical and Computer Engineering at the University of Michigan. Her research focuses on control systems, formal methods, and cyber-physical systems, with applications in autonomy, system identification, and verification. She leads a diverse research group encompassing PhD, MS, and undergraduate students, as well as postdoctoral researchers. Her work bridges theory and practice, addressing challenges in safety-critical systems design and data-driven control. Her educational background includes a PhD in Electrical and Computer Engineering from Northeastern University and a Master’s from Penn State. She has received significant funding from NSF, ONR, and industry partners, supporting projects like the CLEVR-AI initiative and Scenic ecosystem development. Her research has been recognized through awards and collaborations at institutions like MIT, Berkeley, and Johns Hopkins. Key research areas include model-based control synthesis, robust system identification, and anomaly detection in cyber-physical systems. Notable contributions include methods for correct-by-construction control, hybrid system analysis, and learning-based approaches for autonomous systems. She actively contributes to conferences such as HSCC and CDC, and her lab’s work impacts automotive safety, energy systems, and robotics. Ozay’s advising spans over 50 students and postdocs, many of whom hold academic or industry roles. Her lab maintains active collaborations across disciplines, emphasizing interdisciplinary solutions to real-world control challenges.

Mohammed AledhariView profile

Assistant Professor

• Artificial Intelligence
• Machine Learning
• Data Science
• +3 more

Mohammed Aledhari is an Assistant Professor at the University of North Texas, specializing in cybersecurity, machine learning, and data science. His research focuses on applications in computational medicine, bioinformatics, and autonomous systems. He holds a Ph.D. from Western Michigan University and degrees from the University of Basrah and the University of Anbar. His research interests include social cybersecurity techniques, federated learning in IoT, and AI-driven solutions for healthcare and transportation. Recent work explores blockchain-enabled digital twins, DDoS attack detection, and equitable ASD diagnostics using machine learning. His publications span cybersecurity frameworks, autonomous vehicle communication protocols, and biomedical IoT innovations. Notable contributions include optimizing intrusion detection in IoMT networks and developing interpretable machine learning models for healthcare. While no formal awards or grants are listed, his work emphasizes interdisciplinary applications of AI in healthcare, transportation, and energy markets. His email is Mohammed.Aledhari@unt.edu .

Ellie PavlickView profile

Assistant Professor

• Language Acquisition
• Grounded Language Learning
• Neural Language Models
• +7 more

Ellie Pavlick is an Assistant Professor of Computer Science and Linguistics at Brown University, where she leads the Language Understanding and Representation (LUNAR) Lab . Her work bridges computational linguistics and cognitive science, focusing on grounded language learning and the structural dynamics of neural language models. PhD from University of Pennsylvania (2017), specializing in paraphrasing and lexical semantics Collaborates with Brown's Robotics, Visual Computing labs, and Cognitive, Linguistic, and Psychological Sciences department Research interests center on cognitively-inspired approaches to language acquisition, analyzing how LLMs and humans encode abstract reasoning and compositional structures. Her lab explores: Grounded language learning in multimodal systems Emergence of compositional behavior in neural networks Interpretability frameworks for black-box AI Key publication areas include: Transformer mechanisms and cognitive modeling Formality and complexity in language systems Relational reasoning and multimodal integration Ellie teaches courses on computational linguistics and human-machine language processing, with a focus on synergies between AI and psychological science.

Bryon AragamView profile

Associate Professor

• Causality
• Statistical Machine Learning
• Probabilistic Modeling
• +6 more

Bryon Aragam is an Associate Professor of Econometrics and Statistics and Robert H. Topel Faculty Scholar at the University of Chicago Booth School of Business. His research focuses on the intersection of causality, statistical machine learning, and probabilistic modeling, with particular emphasis on applications to artificial intelligence systems including large language models like ChatGPT and generative models like DALL-E. Dr. Aragam completed his PhD in Statistics and a Masters in Applied Mathematics at UCLA, where he was an NSF graduate research fellow. Prior to joining the University of Chicago, he was a project scientist and postdoctoral researcher in the Machine Learning Department at Carnegie Mellon University. Research Focus: Causal structure learning in probabilistic generative models Key Areas: Causal machine learning, deep generative models, latent variable models, statistical learning theory Applications: AI interpretability, ethics, and fairness in artificial intelligence systems Teaching: Business Statistics, Econometrics and Statistics Colloquium His recent publications demonstrate a strong theoretical foundation combined with practical applications, particularly in understanding and improving AI systems. His work spans causal discovery, graphical models, deep learning, and latent variable modeling, with particular attention to the theoretical properties of these methods and their applications to real-world AI challenges. The research shows a progression toward increasingly complex problems in causal representation learning and AI interpretability. Scientific Awards: Robert H. Topel Faculty Scholar NSF Graduate Research Fellow Dr. Aragam's work has been published in top statistics and machine learning venues including the Annals of Statistics, Neural Information Processing Systems (NeurIPS), the International Conference on Machine Learning (ICML), and the Journal of Machine Learning Research (JMLR). His research group publishes broadly across both statistical and machine learning communities, demonstrating the interdisciplinary nature of his work at the intersection of statistics, machine learning, and causal inference. As a data science consultant for technology and marketing firms, Dr. Aragam has applied his expertise to problems in survey design, customer retention, logistics, and ranking, bridging the gap between theoretical research and practical applications.