Fei Fang is an Associate Professor in the Software and Societal Systems Department at Carnegie Mellon University (CMU) , where she explores the intersection of artificial intelligence and multi-agent systems . Her work integrates machine learning with game theory to address challenges in security , sustainability , and mobility , aligning with the AI for Social Good mission. Ph.D. in Computer Science, University of Southern California (2016) B.Eng. in Electronic Engineering, Tsinghua University (2011) Recent research focuses on reinforcement learning , large language models (LLMs) , and human-AI collaboration . Her team’s work has been recognized with 15+ awards across prestigious venues like IAAI, AAAI, and IJCAI. Notable accolades include the 2023 Allen Newell Award , 2022 Sloan Fellowship , and NSF CAREER Award (2021) . She actively contributes to educational initiatives , including teaching "Demystifying AI for Everyone" at CMU, and has sought part-time teaching assistants for course development. Her research spans 15+ domains , including AI ethics , cyber defense , traffic optimization , and public health .
Tapio Schneider is the Theodore Y. Wu Professor of Environmental Science and Engineering at the California Institute of Technology. His research focuses on atmospheric dynamics across Earth and other planets, climate modeling innovations, and geophysical turbulence analysis. He contributes to the Climate Modeling Alliance (CliMA) and develops advanced computational tools for climate prediction. Albert-Ludwigs-Universität Freiburg (Vordiplom, 1993) Princeton University (M.Sc. 1997, Ph.D. 2001) University of Washington, Seattle (Visiting Graduate Student, 1994-1995) His research spans climate dynamics , atmospheric turbulence , and AI-enhanced climate modeling , addressing challenges in cloud dynamics, extreme weather patterns, and planetary climate systems. Current work emphasizes hybrid machine learning-physical models and computational acceleration for high-resolution simulations. Recent publications highlight trends in AI integration for climate science, with applications in hydrology , cloud microphysics , ocean circulation , snowpack modeling , and climate tipping points . His team develops open-source tools like ClimateMachine for GPU-accelerated simulations. Scientific Recognition: Fellow, American Geophysical Union (2022) Rosenstiel Award (2019) World Economic Forum Young Scientist (2012) David and Lucile Packard Fellow (2005-2010) Alfred P. Sloan Research Fellow (2004-2006) Tapio leads climate dynamics research at Caltech, directs the Linde Center for Global Environmental Science (2011-2012), and serves as Editor for the Journal of Advances in Modeling Earth Systems . His group collaborates with NASA Jet Propulsion Laboratory (2016-2024) and Google Research (2022-present).
Pieter van Goor is a Research Fellow at the Australian National University (ANU), affiliated with the School of Engineering and the Systems Theory and Robotics (STR) group. He holds a PhD in Control Theory (completed 2022) and dual bachelor's degrees (BEng/BSc, 2018). His research focuses on equivariant systems theory, state estimation, and robotics applications. Key contributions include equivariant observer design, Lie group-based control, and geometric data fusion. Education: Bachelor of Engineering (Research & Development) (Honours) in Mechatronics (ANU, 2018) Bachelor of Science in Mathematics (ANU, 2018) PhD in Control Theory (ANU, 2022) Research Interests: Equivariant systems theory, nonlinear control, robotics applications, state estimation on Lie groups, sensor fusion, and geometric control methods. His work emphasizes symmetry exploitation in filter design and observer construction for systems with inherent geometric structures. Grants & Collaborations: Active collaborations include work with Robert Mahony and institutions like the IEEE. Research spans theoretical frameworks (e.g., equivariant filters) and applied systems (e.g., ArduPilot autopilot, event cameras). Labs/Teams: Member of the Systems Theory and Robotics (STR) group at ANU, focusing on advanced control theory and robotics.
LEONG Tze Yun is a Professor in the Department of Computer Science at the School of Computing, National University of Singapore (NUS). She holds S.B., S.M., and Ph.D. degrees in Computer Science from the Massachusetts Institute of Technology (MIT). Her academic career spans both research and industry experience, with significant contributions to the fields of artificial intelligence and health informatics. Dr. Leong's educational background includes: Ph.D. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.M. in Electrical Engineering & Computer Science, Massachusetts Institute of Technology S.B. in Computer Science & Engineering, Massachusetts Institute of Technology Her primary research interests focus on responsible AI, dynamic decision-making, neurocognitive modeling, reinforcement learning, artificial general intelligence, and biomedical and health informatics. Her work bridges the gap between theoretical AI development and practical healthcare applications, with an emphasis on ethical considerations and human-centered design. She directs the Medical Computing Laboratory at NUS, a multidisciplinary research program exploring human-aware decision modeling in complex environments. Analysis of her recent publications reveals a strong trend toward responsible AI development, with significant contributions to reinforcement learning techniques, causal inference methods, and applications of AI in healthcare. Her work increasingly integrates ethical considerations with technical AI development, particularly evident in her 2024 publications on medical AI and human values. Her scientific recognition includes: Fellow of the American College of Medical Informatics (ACMI) Founding Fellow of the International Academy of Health Sciences Informatics (IAHSI) Member of Eta Kappa Nu (Honor Society for Electrical Engineers) Dr. Leong has supervised numerous doctoral and master's students throughout her career, many of whom have gone on to prominent positions at institutions like Google, Netflix, Mayo Clinic, and academic institutions worldwide. Her advisory work extends to significant policy development, including contributions to WHO guidance on ethics and governance of AI for health. She currently serves on the World Health Organization (WHO) Expert Group on Ethics and Governance of AI for Health, the World Economic Forum (WEF) AI Governance Alliance, and the Advisory Council on AI in Uzbekistan. Her laboratory work focuses on developing adaptive systems that evolve with changing technical functionalities, system infrastructures, usage patterns, and operational contexts, with applications spanning prediction and decision analytics, human-aware robotics, game artificial intelligence, personalized education, and assistive care for elderly with neurocognitive disorders.
Frank Corsetti is a Professor of Earth Sciences at the University of Southern California (USC), leading the Corsetti Lab. His research focuses on the co-evolution of Earth and its biosphere, particularly during extreme events like the 'Snowball Earth' glaciation and mass extinctions. He holds a Ph.D. in Geological Sciences from UC Santa Barbara (1998) and a B.S. in Geology from UC Davis (1989). Research Interests: Corsetti studies geobiological processes such as stromatolite formation, microbial carbonate systems, and the environmental impacts of mass extinctions. His lab investigates biosignatures, diagenetic processes, and astrobiological applications. Current projects include the end-Triassic mass extinction and microbial interactions in extreme environments. Advising & Grants: He mentors a team of PhD students (including Reena Joubert, Alison Cribb, and others) and has secured funding from NASA for projects like stromatolite carbonate formation studies. His work bridges paleontology, geochemistry, and microbiology, contributing to understanding Earth’s long-term climate and life history. Labs/Teams: The Corsetti Lab at USC explores stromatolites, microbial mats, and ancient Earth systems. Collaborations involve institutions like JPL and international universities.
Barbara Caputo is a Full Professor at Politecnico di Torino, leading the VANDAL Laboratory and directing the AI@PoliTo Interdepartmental Lab. She holds a double affiliation with the Italian Institute of Technology (IIT) and has held roles at Idiap-EPFL and Sapienza University. Her research focuses on AI, computer vision, domain adaptation, and federated learning. She contributes to national AI policy, including the Italian Strategy on AI and the National PhD on AI for Industry 4.0. She is an ERC Laureate and ELLIS Fellow, co-founding ELLIS society. Her work spans visual place recognition, action recognition, and cross-domain learning. Education: PhD in Computer Science from KTH Royal Institute of Technology (2005). Major roles include Rector’s Advisor on AI at PoliTo, Board Member of ELLIS, and coordinator of the AI & Industry 4.0 vertical in the National PhD program. Awards include ERC Laureate (2017), ELLIS Fellow (2019), and Inspiring Fifty Italy (2018). Her research emphasizes federated learning, domain adaptation, and AI ethics. Recent articles explore domain generalization, resource-efficient federated models, and AI-environment interactions. She collaborates with institutions like MUR, CNR, and the European Commission on AI policy and tech initiatives.
Petros Koumoutsakos is the Herbert S. Winokur, Jr. Professor of Computing in Science and Engineering at Harvard University's School of Engineering and Applied Sciences (SEAS), where he also serves as Area Chair for Applied Mathematics. His research integrates machine learning with computational science to advance understanding of complex systems, including fluid dynamics, turbulence modeling, and biomedical applications. He leads the CSE Lab, focusing on high-performance computing and interdisciplinary collaborations such as a recent study with Citadel Securities and Google Cloud to simulate heart disease in cloud environments. Key research interests include reinforcement learning for turbulence closures, generative models for PDE solutions, and physics-informed AI for biomedical imaging and wildfire prediction. He was awarded the PRACE HPC Excellence Award (2023) for contributions to high-performance computing. His work bridges computational methods with real-world applications, emphasizing interpretability and scalability in multiscale systems. Grants & Collaborations: Leadership in multi-institutional projects, including turbulence modeling via reinforcement learning and cloud-based HPC studies. Labs/Teams: Director of the CSE Lab, advancing AI, computational fluid dynamics, and biomedical simulations.
Robert Jenssen is a Professor in the Machine Learning Research Group at UiT The Arctic University of Norway and serves as the Director of Visual Intelligence , an 8-year Research Council of Norway-funded SFI center. His research focuses on solving societal challenges in healthcare, marine mapping, energy, and Earth observation through collaborations with industry and public stakeholders. Director, Visual Intelligence (SFI) Center Professor, UiT Adjunct Professor, Pioneer Centre for AI (University of Copenhagen) and Norwegian Computing Center His methodological expertise spans neural networks, information-theoretic learning, self-learning, and explainable AI (XAI). Recent work emphasizes multimodal learning, uncertainty estimation, and medical image analysis. Scientific awards include: Best Paper, Pattern Recognition Letters (2024) Dissertation Award, Norwegian AI Society (2023) Best Paper, Color and Visual Computing Symposium (2022) IEEE GRS Society Letters Prize (2013) Prize for Young Researchers, University of Tromsø (2007) He contributes to international leadership as a member of the Scientific Advisory Board (SAB) for the Max Planck Institute for Intelligent Systems, France's SequoIA AI Excellence Cluster, and Denmark's DIREC center.
James K. Russell is a Full Professor in Earth, Ocean and Atmospheric Sciences at The University of British Columbia, where he has been faculty since 1999. He directs the Volcanology and Petrology Laboratory and maintains an active research program in volcanology and petrology with significant international collaborations including appointments at LMU Munich, University of Torino, and Monash University. His educational background includes a Ph.D. (1984) and M.Sc. (1980) from the University of Calgary, and a B.Sc.(H) (1976) from the University of Manitoba. He previously worked at Cominco Exploration Ltd in Vernon, British Columbia before joining UBC. Russell's research focuses on understanding magmatic processes through multiple approaches. His primary interests include glaciovolcanism as a tool for paleoclimate reconstruction, predictive modeling of magma transport properties, and high-temperature rock mechanics experiments. His work bridges theoretical modeling with field observations, particularly in the Canadian Cordillera. He has developed influential models for magma viscosity that are widely cited in the volcanology community. His publication record shows consistent high-impact research over several decades, with recent work focusing on ultramafic melt viscosity, kimberlite transport mechanisms, and glaciovolcanic interactions. The research demonstrates a strong emphasis on quantitative approaches combining thermodynamics, rheology, and field-based observations to solve fundamental problems in volcanology. Scientific Awards: 2024 Research Award of the Alexander von Humboldt Foundation, LMU, Munich, Germany 2022 DAAD Research Stay for Visiting Academics Scholarship (3 months) 2019 Visiting Professorship, University Roma Tre 2019 Visiting Fellow at Centre for Advanced Studies, University of Munich 2016 Fellow of Mineralogical Society of America 2013 NSERC Discovery Accelerator Supplement Award 2010 Career Achievement Medal, Volcanology Division, Geological Association of Canada 2008 Peacock Medal, Mineralogical Association of Canada Russell has served in numerous editorial and professional capacities, including as Co-Editor-in-Chief for Journal of Volcanology & Geothermal Research (2016-2022), Associate Editor for multiple prominent journals, and as evaluator for research grants including ERC Consolidator grants and NSERC competitions. He has provided media consultation for National Geographic and Nova/Pioneer Productions. He maintains active laboratory facilities for volcanology and petrology research at UBC, with equipment for high-temperature experiments and magma rheology studies. His research group collaborates internationally with institutions in Germany, Italy, and Australia, reflecting his extensive network built through sabbatical visits and visiting professorships.
Raffaele Ferrari is the Cecil & Ida Green Professor of Oceanography and Co-Director of the MIT Lorenz Center within the Department of Earth, Atmospheric, and Planetary Sciences (EAPS). He holds a BS/MS from Università di Torino and a PhD from Politecnico di Torino and Scripps Institution of Oceanography. His research focuses on ocean dynamics, climate modeling, and paleoclimatology, with emphasis on turbulence, carbon cycling, and climate system interactions. Key projects include the Climate Modeling Alliance (CliMA) and the Bottom Boundary Layer observational program. He has been recognized with awards such as the Harald Sverdrup Lecture (2022) and the Nicholas P. Fofonoff Award (2007). His work integrates high-resolution modeling, observational data, and theoretical frameworks to advance climate science. Education: BS/MS (Università di Torino), PhD (Politecnico di Torino/Scripps) Affiliations: MIT Lorenz Center, CliMA collaboration (Caltech/JPL), MIT-WHOI Joint Program Research spans ocean turbulence, paleoclimate dynamics (e.g., Last Glacial Maximum, Eocene), and climate model development using AI/ML. He advises numerous students across these themes, contributing to breakthroughs in submesoscale dynamics and ocean energetics.
Michael Jong Kim is an Associate Professor at the Sauder School of Business, University of British Columbia, specializing in the Division of Operations and Logistics. His research focuses on dynamic programming, statistical learning, robust optimization, and the exploration vs exploitation trade-offs in sequential decision-making processes. BASc, M.Math, and PhD from the University of Toronto His work spans topics in stochastic optimization, supply chain dynamics, and information dissemination in uncertain environments. Publications highlight contributions to Bayesian inventory control, semi-Markovian system control, and variance regularization in optimization models. Dr. Kim teaches advanced business analytics courses, including Descriptive and Predictive Business Analytics and Advanced Predictive Business Analytics (MBAN) during the 2024-2025 academic year. He can be reached at mike.kim@sauder.ubc.ca or by phone at +1 604.822.8682.
Mark Crowley is an Associate Professor in the Department of Electrical and Computer Engineering at the University of Waterloo, with a cross-appointment in the Cheriton School of Computer Science. He is a member of the Waterloo Artificial Intelligence Institute (WAII) and the Waterloo Institute for Complexity and Innovation (WICI), and serves as National Secretary of the Canadian Artificial Intelligence Association (CAIAC). His educational background includes a Ph.D. and M.Sc. in Computer Science from the University of British Columbia, where he worked in the Laboratory for Computational Intelligence, and a B.A. in Computer Science from York University. He completed a postdoctoral fellowship at Oregon State University working with Tom Dietterich's machine learning group. Crowley's research focuses on developing dependable and transparent algorithms to augment human decision-making in complex domains with multiple agents, spatial structure, or uncertainty. His work spans Reinforcement Learning , Deep Learning , Ensemble Methods , and Manifold Learning . He frequently collaborates with researchers in applied fields including Computational Sustainability, Sustainable Forest Management, Autonomous Driving, Medical Imaging, and Material Design. His research is motivated by both theoretical opportunities and real-world challenges such as forest fire management, automotive applications, and medical imaging. His recent publications demonstrate a strong focus on addressing challenges in reinforcement learning, particularly around observation costs, multi-agent systems, and causal representation learning. His work on ChemGymRL provides a significant contribution to digital chemistry and material design through reinforcement learning frameworks. The textbook Elements of Dimensionality Reduction and Manifold Learning represents a major contribution to the theoretical foundations of machine learning. Crowley actively supervises graduate students, with recent thesis completions including Shayan Shirahmadi Gale Bagi (PhD, Feb 2025) and Oleksandra Nahorna (MASc, Dec 2024). His lab, UWECEML (Waterloo ECE Machine Learning Lab), focuses on developing new algorithms at the intersection of Machine Learning, Optimization, and Probabilistic Modeling. He teaches courses including ECE 457C (Reinforcement Learning), ECE 657A (Data & Knowledge Modelling & Analysis), and ECE 457B (Fundamentals of Computational Intelligence). His blog Computationally Thinking explores AI, machine learning, and the societal impact of these technologies.
Professor Gabrielle Lynch is a leading scholar in Comparative Politics and African Studies at the University of Warwick. She holds the academic rank of Professor and serves as PAIS Impact Director, Comparative Politics cluster lead, and Warwick UCU Equalities Officer. Her research focuses on ethnic identities, elections, transitional justice, and social media's role in African politics. Lynch earned her BA, MPhil, and DPhil from the University of Oxford. She has conducted extensive fieldwork in Kenya, Ghana, and Uganda, supported by grants from the ESRC, Warwick Policy Support Fund, and others. Her awards include the ESRC Award for Outstanding International Impact (2019) and the Market Research Society’s President’s Medal. Lynch is editor of African Affairs and a member of the African Studies Association of the UK and British Institute in Eastern Africa. Lynch’s work bridges academia and public engagement, including columns in The Saturday Nation and The East African . She founded the African Judiciaries Research Network and co-authored influential books like Performances of Injustices and The Moral Economy of Elections in Africa . Her research emphasizes democratization challenges, electoral integrity, and the intersection of technology and politics in Africa.
Prof. Richard L. Peters is a Professor and Head of the Chair of Tree Growth and Wood Physiology at the Technische Universität München (TUM) since 2024. His research focuses on tree physiology, wood formation, and climate-forest interactions. He holds a doctorate summa cum laude from the University of Basel (2018) and has conducted postdoctoral research at the Swiss Federal Institute for Forest Science (WSL) and Ghent University. His work integrates forest ecology, dendrochronology, and ecophysiology to address climate change impacts on forests. Education: B.Sc./M.Sc. in Biology at Utrecht University, Ph.D. from University of Basel (2018). Key career steps include a Swiss National Science Foundation (SNSF) fellowship at Ghent University and coordination of the Swiss Canopy Crane II project (2021). Research Interests: Tree water-use strategies, drought tolerance mechanisms, carbon allocation dynamics, and the physiological basis of tree growth. He leads interdisciplinary projects to monitor forest responses to environmental stress using tools like the TreeNet network. Awards: Early Postdoc Mobility Fellowship (SNSF, 2019), Doctorate summa cum laude (2018). Contributions: Authored >180 publications, co-developed the datacleanr R package for ecological data processing, and pioneered methods linking tree-ring data with climate models. His work emphasizes the need for better observational data to improve vegetation models.
Lars Ulander is a Professor at Chalmers University of Technology specializing in radar remote sensing. His research focuses on synthetic aperture radar (SAR) signal processing, particularly for applications in forest biomass mapping and ground imaging using VHF/UHF-band systems. He is a key proposer for ESA's BIOMASS satellite mission (launching 2025) and leads the BorealScat project, utilizing a 50-meter tower-based tomographic radar to study boreal forest dynamics. His work spans radar system development, SAR tomography techniques, and environmental monitoring of forests and sea surface currents. Current research areas include vegetation water content estimation, bistatic radar configurations, and optimization of SAR data processing algorithms for multi-temporal analysis. Recent publications demonstrate expertise in P-band/L-band SAR for biomass retrieval, passive radar systems, and interferometric techniques. His articles investigate radar backscatter sensitivity to forest structure, moisture parameters, and seasonal changes, while contributing to mission design frameworks like SLAINTE and SESAME.
