Viswanath Nagarajan is an Associate Professor of Industrial & Operations Engineering and Computer Science Engineering (courtesy) at the University of Michigan. His research focuses on combinatorial optimization, approximation algorithms, and stochastic models for routing, scheduling, and location problems. He previously served as an Assistant Professor at the University of Michigan (2014–2020) and a Research Staff Member at IBM T.J. Watson Research Center (2009–2014). He holds a Ph.D. in Algorithms, Combinatorics, and Optimization from Carnegie Mellon University (2004–2009) and a B.Tech. in Computer Science from IIT Bombay (1999–2003). His research explores uncertainty management in optimization, including stochastic models and approximation algorithms for decision-making under uncertainty. He has contributed to adaptive algorithms, submodular optimization, and applications in logistics, network design, and scheduling. Education: Ph.D., Algorithms, Combinatorics, and Optimization (Carnegie Mellon University, 2009) B.Tech., Computer Science and Engineering (IIT Bombay, 2003) Prof. Nagarajan has organized major conferences like IPCO 2019 and served on editorial boards for journals including Operations Research , ACM Computing Surveys , and ACM Transactions on Algorithms . His service includes program committees for SODA, APPROX, and IPCO. He advises Ph.D. students focusing on optimization theory and applications, with advisees securing positions at Yahoo! Research, the University of Chicago, Ford Motor Company, and Georgia Tech.
Stefan Schaltegger is Professor for Sustainability Management at the Centre for Sustainability Management (CSM) at Leuphana University Lüneburg. With over two decades of research experience, he is a globally recognized scholar in sustainability management, corporate sustainability, and environmental accounting. His work bridges academic research with practical business applications, influencing sustainability practices across multiple industries. Professor for Sustainability Management at Leuphana University Lüneburg Director of the Centre for Sustainability Management (CSM) Extensive publication record in top sustainability journals Regular contributor to policy discussions on sustainability Professor Schaltegger's research focuses on sustainability management systems, corporate sustainability strategy, environmental accounting, sustainable business models, and sustainability transitions. His work examines how organizations can effectively integrate sustainability into core operations, emphasizing stakeholder engagement, sustainability performance measurement, and the development of compelling business cases for sustainability. He has pioneered research on sustainability management accounting, rebound effects in sustainability initiatives, and the role of change agents in corporate sustainability transformations. His research spans both theoretical development and practical application, making significant contributions to how businesses understand and implement sustainability. His recent publications reveal an evolving research trajectory with increasing focus on systemic sustainability challenges. Schaltegger has shifted from foundational work on sustainability management accounting toward more complex issues like environmental rebound effects, sustainability transformations, and regenerative business practices. His 2023-2025 publications show growing attention to climate change responses, biodiversity conservation, and the role of service innovation in sustainability transformations. The interdisciplinary nature of his work is evident in publications spanning business strategy, environmental science, accounting, and policy journals. Ranked among the world's top 2% scientists in the Stanford study (2020) Best Young Researcher of the Year at Leuphana University Lüneburg (2010) IFAC PAIB Committee Research award for "article of merit" (2003) Professor Schaltegger actively mentors doctoral students and early-career researchers, though specific student names aren't listed in the provided materials. His research is supported by numerous projects including "TrICo Subproject: Community Sustainable Production," "Account4GreenEco," and "Hochschulen in Gesellschaft – Realexperimente transformativer Lern- und Forschungsprozesse für eine Kultur der Nachhaltigkeit an Hochschulen." His work demonstrates strong industry engagement through collaborations with businesses implementing sustainability practices. The Centre for Sustainability Management (CSM), which Schaltegger leads, serves as a hub for interdisciplinary sustainability research, education, and practice. The CSM coordinates multiple research projects including the Innovation Network aiming at Sustainable Smartphones (INaS) and works closely with industry partners to develop practical sustainability solutions. The center's activities span research, education, and transfer, reflecting Schaltegger's commitment to bridging the gap between academic theory and business practice in sustainability.
Nick Abel is an Honorary Associate Professor at the Fenner School of Environment & Society, Australian National University. With a distinguished international career spanning Africa and Australia, he has worked in Zimbabwe, Kenya, Ethiopia, Botswana, Somalia, Swaziland, Zambia, and Australia. His academic credentials include a Ph.D., M.Sc., and B.Sc.(Hons.), reflecting a strong foundation in environmental sciences and complex systems thinking. Abel's research program centers on the critical challenge of human adaptation to climate change, with particular focus on why adaptation efforts are insufficient to prevent environmental and social tipping points. He critically examines Australia's role as the world's third largest exporter of fossil fuels while facing escalating climate impacts, investigating how fossil fuel companies have maintained influence over public policy. His work integrates social-ecological systems theory, resilience thinking, and practical governance frameworks to address climate adaptation challenges. His publication record demonstrates a clear evolution from foundational work in rangeland management and complex adaptive systems toward urgent climate policy interventions. Early research established theoretical frameworks for understanding resilience in dryland ecosystems, while recent work focuses on transformation pathways, Indigenous engagement, and community-based adaptation strategies. A consistent thread throughout his career is the integration of diverse knowledge systems and the development of practical approaches for environmental governance in rapidly changing conditions. Abel has registered to supervise research students at ANU and participates in significant collaborative projects, including the current 'Partnering with local communities in regional Australia to increase resilience to flood events' (2022-2025). His high-impact publications, including the highly cited 'Resilience management in social-ecological systems' (1,120 Scopus citations) and 'From Metaphor to Measurement: Resilience of What to What?' (2,596 Scopus citations), demonstrate his foundational contributions to the field. Through his work with various research groups and projects, Abel has helped establish frameworks for understanding social-ecological systems and climate adaptation. His contributions to the Fenner School include advancing methodologies for integrating scientific and Indigenous knowledge systems, developing practical approaches to environmental governance, and critically examining the political economy of climate policy in Australia.
Hamsa Bastani is an Associate Professor of Operations, Information and Decisions at the Wharton School, University of Pennsylvania, with a secondary appointment in Statistics and Data Science. She co-directs the Wharton Healthcare Analytics Lab and serves as an Associate Editor for Operations Research, M&SOM and OR Letters. Her academic journey began with summa cum laude graduation from Harvard in 2012 with an A.M. in physics and A.B. in physics and mathematics. She completed her PhD in Stanford's Electrical Engineering department under Mohsen Bayati, followed by a Herman Goldstine postdoctoral fellowship at IBM Research. Professor Bastani's research focuses on developing novel machine learning algorithms for data-driven decision-making, with applications spanning healthcare operations, social good, and revenue management. Her work demonstrates particular expertise in sequential decision-making (bandits, reinforcement learning), learning from auxiliary data sources (transfer learning, meta-learning), and designing effective human-AI interfaces (interpretability, fairness). She has made significant contributions to understanding how AI systems affect and augment human behavior, with the goal of designing AI tools that help humans thrive. Her publications reveal a strong trend toward high-impact applications of machine learning in critical societal domains. A significant portion of her recent work focuses on healthcare applications, including optimizing health supply chains in low- and middle-income countries, designing clinical trial protocols, and creating targeted public health interventions. Another major theme examines the complex relationship between humans and AI systems, particularly how AI affects learning outcomes and decision-making processes. Her work frequently bridges theoretical advances with practical implementation, as evidenced by country-scale deployments in Greece and Sierra Leone. Wagner Prize for Excellence in Operations Research Practice (2021) Pierskalla Award for Best Paper in Healthcare (2021, 2019, 2016) Behavioral OM Best Paper Award (2021) Public Sector in OR Best Paper Award (2024) INFORMS Data Mining Best Paper Award (2022) Wharton Teaching Excellence Award (2019, 2020, 2021) Professor Bastani has advised numerous PhD students who have gone on to prominent positions, including Pia Ramchandani (Director of Responsible AI at PwC), Arielle Anderer (Assistant Professor at Cornell Johnson), and Kan Xu (Assistant Professor at ASU Carey). Her research has been supported by collaborations with national governments, including the Greek government where she co-designed Eva, the national-scale reinforcement learning system for targeted COVID-19 testing, and the Government of Sierra Leone where she improved patient access to essential medicines by nearly 20% via decision-aware learning. She has also conducted the first large field study deploying generative AI tutors in high school math classes. She leads the Wharton Healthcare Analytics Lab and serves on the Steering Committee for the Penn Center for Health Incentives and Behavioral Economics and on the statistics advisory committee for the AHA Food is Medicine Initiative. Outside academia, she serves on the Workday AI Advisory Board, demonstrating her commitment to translating academic research into practical applications.
Dr. Stephanie Spahr is a Research Group Leader at the Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB) in Berlin, Germany, where she leads the Organic Contaminants research group within the Department of Ecohydrology and Biogeochemistry. Previously, she served as a Junior Research Group Leader at the University of Tübingen's Center for Applied Geoscience (2019-2021) and as a Postdoctoral Researcher at Stanford University's Department of Civil and Environmental Engineering (2016-2019). Dr. Spahr earned her PhD in Environmental Chemistry from the Swiss Federal Institute of Technology Lausanne (EPFL) and the Swiss Federal Institute of Aquatic Science and Technology (Eawag) in 2016. Her doctoral research focused on the formation of N-nitrosodimethylamine during water disinfection with chloramine. She completed her MSc in Geoecology at the University of Tübingen in 2012, with thesis work on carbon and nitrogen isotope analysis of benzotriazoles conducted at Eawag, and her BSc in Geoecology/Ecosystem Management at the same institution in 2010. Dr. Spahr's research focuses on trace organic contaminants in aquatic systems, with particular expertise in transformation processes of contaminants in natural and engineered systems, advanced oxidation processes for water treatment, urban blue-green infrastructure, and compound-specific isotope analysis. Her work bridges environmental chemistry, engineering, and ecology to address water quality challenges in urban and natural water systems. She employs advanced analytical techniques to track contaminant sources and transformation pathways, with a strong emphasis on practical applications for water treatment and environmental protection. Her recent publications demonstrate a strong focus on biochar-based water treatment technologies, particularly for stormwater management. She investigates how biochar amendments can remove trace organic contaminants from urban runoff, with recent work examining persulfate activation mechanisms, the role of chloride in reactive species formation, and the performance of engineered media filters under dynamic conditions. Her research also extends to understanding contaminant transport in rivers, the ecological impacts of pollutants, and developing analytical methods for environmental monitoring. The interdisciplinary nature of her work connects chemical processes with ecological outcomes. Outstanding Review Paper Award 2023 in Environmental Science: Water Research & Technology Selected for the Falling Walls Female Science Talents Intensive Track 2023 Selected mentee in the Leibniz Mentoring Programme 2022-2023 Best poster award (1st prize) at the Wasser 2022 of the Water Chemistry Society Selected fellow in the Postdoc Academy for Transformational Leadership 2020-2022 (Robert Bosch Stiftung) Selected fellow in the Athene Program for early female career researchers at the University of Tübingen, 2020-2021 As a Research Group Leader, Dr. Spahr supervises multiple research projects including 'POllution in UrbaN ponds, eco-evolutionary Dynamics, and Ecosystem Resilience (POUNDER)', 'Dynamic hyporheic zone', 'NYMPHE', and the 'Incident-related special investigation programme for the environmental disaster in the Oder River'. She serves on the Executive Board of the German Water Chemistry Society and heads its Expert Committee on 'Oxidative Processes'. Her collaborative work spans numerous institutions across Germany and internationally, addressing critical water quality challenges through interdisciplinary approaches. Dr. Spahr leads the Organic Contaminants research group at IGB Berlin, which focuses on understanding the fate and treatment of organic pollutants in water systems. Her team employs advanced analytical techniques including compound-specific isotope analysis to track contaminant sources and transformation pathways. The group collaborates extensively with other departments at IGB and with international partners on projects addressing urban water challenges and ecological impacts of pollution. Current research emphasizes innovative water treatment technologies, particularly biochar-based systems for stormwater management, and investigating the complex interactions between contaminants, aquatic ecosystems, and human activities.
Ashley Cordes (Coquille/KōKwel) serves as an Assistant Professor of Indigenous Media in Environmental Studies and Data Science at the University of Oregon's College of Arts and Sciences. She is also a recent American Council of Learned Societies Fellow whose research bridges Indigenous science and technology studies, digital media, and environmental/place-based studies. Her educational background includes a PhD in Media Studies with an outside area in Native American Studies from the University of Oregon (2019), an MA in Communication from Hawaii Pacific University (2012), and a BA in Communication with a Journalism Certificate from Loyola Marymount University (2010). Cordes' research centers on how Indigenous culture and technology producers leverage digital media, emerging technologies, and discourse to advance Tribal sovereignty, cultural revitalization, and the resurgence of Indigenous knowledge systems. Her work specifically examines AI, blockchain, and cryptocurrency through Indigenous epistemologies, challenging dominant narratives about technological progress and financial systems. She has published in journals such as Cultural Studies >Critical Methodologies , Journal of International and Intercultural Communication , and Feminist Media Studies , and is the author of Indigenous Currencies: Leaving Some for the Rest in the Digital Age (MIT Press). Her recent publications reveal a consistent focus on decolonial approaches to technology, with particular attention to Indigenous data sovereignty, environmental justice, and alternative economic systems. The trajectory of her work shows increasing engagement with AI ethics from Indigenous perspectives, culminating in her contributions to the Indigenous Protocol and Artificial Intelligence position paper. American Council of Learned Societies Fellow Contributor to Indigenous Protocols and Artificial Intelligence Working Group Cordes actively mentors doctoral students in Indigenous digital media and participates in multiple research collectives including Abundant Intelligences, CHoRUS Network (focusing on ethical data gathering in Indigenous contexts), Indigenous Protocols and Artificial Intelligence, and the Climate Resilience Taskforce. Her community-engaged work includes the Storying on the Coquille River project, which addresses climate change impacts on salmon populations through digital humanities approaches. She maintains strong connections to her Coquille Nation community, serving on the Climate Resilience Taskforce and as Chair of the Culture and Education Committee, ensuring her academic work remains grounded in community needs and Indigenous knowledge systems.
Matthew Santa serves as Professor of Music Theory and Chair of the Music Theory and Composition Area at Texas Tech University School of Music, with prior teaching appointments at Queens College and Hunter College. His leadership shapes curriculum development and academic initiatives within the department. Academic credentials include advanced degrees from Louisiana State University and The City University of New York, establishing foundational expertise in theoretical frameworks and analytical methodologies. Research focuses on post-tonal analysis , diatonic set theory , and parsimonious voice leading , bridging complex theoretical concepts with practical pedagogy. His work integrates popular music analysis and metrical studies, emphasizing accessibility for diverse learners through innovative teaching resources. Publications demonstrate evolving scholarly trends from late-20th century set theory toward contemporary applications in musical form and rhythm. Recent textbooks synthesize Rothstein, Krebs, and Mirka's theories into unified analytical approaches for undergraduate and graduate education. Scientific recognition includes: MTSNYS Young Scholar Award (1998) Mentorship encompasses graduate and undergraduate students across composition and theory disciplines, though specific advisees aren't documented in source materials. No grant funding details appear in available records. Collaborative projects include the Flute/Theory Workout series with Lisa Garner Santa and Thomas Hughes, blending performance technique with theoretical concepts through MIDI accompaniment systems.
Prof. Dr. Rudi Zagst is a Professor of Mathematical Finance at the Technical University of Munich (TUM), where he serves as Head of the Department of Mathematical Finance within the TUM School of Computation, Information and Technology. He has held this position since 2001 and is actively involved in teaching, research, and academic leadership. In 2003, he was appointed as a second member of the Faculty of Economics, and since 2004, he has served as Deputy Chairman of the joint elite degree program 'Finance & Information Management' of the University of Augsburg and TUM. Prof. Zagst earned his doctorate in business mathematics from the University of Ulm, where he later completed his habilitation in 2000. His academic journey began with a professional career at HypoVereinsbank AG, where he served as Head of Product Development in Institutional Investment Management before becoming Managing Director of RiskLab GmbH in 1997. His research focuses primarily on financial engineering, risk management, and asset management, with particular emphasis on portfolio optimization, mathematical finance, and quantitative risk management. His work bridges theoretical finance with practical applications, often incorporating advanced mathematical techniques to solve complex financial problems. Recent publications demonstrate his continued interest in GARCH models, portfolio optimization under various constraints, and the application of machine learning techniques to financial problems. Analysis of his recent publications (2024-2025) reveals a strong focus on portfolio optimization under complex market conditions, particularly using GARCH models to capture volatility dynamics. His work increasingly incorporates machine learning techniques (as seen in the credit spread analysis paper) while maintaining rigorous mathematical foundations. Many papers explore the intersection of theoretical finance with practical investment strategies, reflecting his commitment to bridging academic research with real-world financial applications. Professor of the Year 2007 (awarded by Unicum Profession magazine) Prof. Zagst has supervised numerous bachelor's, master's, and doctoral theses through TUM's Finance and Actuarial Science research group. His collaborative work with industry partners through the TUM CAIR Labs and RiskFactory demonstrates strong connections between academic research and practical financial applications. He has received research funding through various industry partnerships with major financial institutions including Allianz, Munich Re, and ERGO Group AG. Prof. Zagst leads the Research Group Finance and Actuarial Science at TUM, which includes Professors Matthias Scherer, Aleksey Min, and Christoph Knochenhauer. The group maintains strong industry connections through the TUM CAIR Labs initiative, collaborating with over 25 financial institutions including Allianz, Munich Re, Deloitte, PwC, and KPMG. Their RiskFactory laboratory serves as a bridge between academic research and practical financial risk management applications in the industry.
Prof. Dr. Kai Cieliebak is a Professor of Mathematics at the University of Augsburg, where he holds the Chair of Analysis and Geometry within the Institute of Mathematics under the Faculty of Mathematics, Natural Sciences, and Materials Engineering. He has been at Augsburg University since 2012, following a professorship at Ludwig-Maximilians-Universität München from 2001-2012. His research group includes several researchers and postdocs working on symplectic geometry and related fields. Dr. Cieliebak earned his Diplom in mathematics summa cum laude from Ruhruniversität Bochum in 1992, with thesis on "Pseudo-holomorphe Kurven und periodische Orbits auf Cotangential Bündeln" under advisor H. Hofer. He completed his PhD in mathematics at ETH Zürich in 1996, with thesis "Symplectic boundaries: closed characteristics and action spectra," also advised by H. Hofer. His academic journey included positions at Harvard University, Stanford University, and research at IBM Zürich before his professorships in Munich and Augsburg. Prof. Cieliebak's research focuses on symplectic and contact geometry , with significant contributions to understanding symplectic manifolds, Lagrangian and Legendrian knots, Stein manifolds, and string topology. His work in Hamiltonian dynamics explores variational methods, periodic orbits, and celestial mechanics problems, particularly the restricted three-body problem. In global analysis , he investigates solution spaces of elliptic PDEs and symplectic field theory. His approach often bridges differential geometry, topology, and dynamical systems, with applications to mathematical physics. Over the past decade, Prof. Cieliebak's publications reveal a consistent focus on symplectic homology, Floer theory, and their applications to geometric problems. His work shows increasing integration of algebraic structures with geometric methods, particularly in cyclic homology and string topology. Recent research demonstrates strong collaboration with Urs Frauenfelder on celestial mechanics problems, applying symplectic techniques to the restricted three-body problem and related orbital dynamics. Prof. Cieliebak has secured significant research funding throughout his career, including multiple DFG grants under project codes CI 45/1 through CI 45/12, NSF grants, and participation in European Science Foundation networking programs. His most notable grants include "Foundations of Symplectic Field Theory" (2009-2015) and the current "Rabinowitz Floer Homology" project (since 2023), both in collaboration with U. Frauenfelder. He has mentored numerous researchers and maintains an active research group at Augsburg University, including postdocs and collaborators working on symplectic geometry problems. His team includes researchers such as Dr. Filip Broćić, Zhen Gao, Dr. Hanna Häußler, Emilia Konrad, Shuaipeng Liu, Dominik Meidert, Dr. Airi Takeuchi, Dr. Evgeny Volkov, Milan Zerbin, and PD Dr. Lei Zhao. Prof. Cieliebak has also organized numerous workshops on symplectic geometry, including the annual "Symplectic Field Theory" workshop series.
Nicolò Cesa-Bianchi is a Professor of Computer Science at the University of Milan, where he serves as head of the Computer Science programs. He is also associated with the Department of Electronics, Information and Bioengineering (DEIB) at Politecnico di Milano. Cesa-Bianchi holds significant leadership roles including Board member, Fellow and co-director of the Milan unit of the European Laboratory for Learning and Intelligent Systems (ELLIS), and membership in the prestigious Accademia Nazionale dei Lincei. He is also involved with The European Lighthouse on Secure and Safe AI (ELSA), The European Lighthouse of AI for Sustainability (ELIAS), and The FAIR foundation. Professor Cesa-Bianchi's research focuses on the theoretical foundations of machine learning, with special emphasis on sequential decision making and online learning algorithms. His work spans multiple areas including multi-armed bandit problems, regret analysis, prediction with expert advice, and learning on graphs. He has made significant contributions to understanding the theoretical limits of learning algorithms and developing efficient methods for various learning scenarios. His research has important applications in online markets, social networks, and bioinformatics. His monographs 'Prediction, Learning, and Games' and 'Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems' are considered seminal works in the field. His recent publications demonstrate continued leadership in advancing the theoretical understanding of machine learning, with 2024-2025 papers covering cooperative online learning, multitask learning, fair trade mechanisms, and refined analyses of bandit algorithms. The research shows increasing focus on practical economic applications while maintaining strong theoretical foundations. Google Research Award Xerox Foundation UAC Award Member of the Accademia Nazionale dei Lincei ELLIS Fellow Cesa-Bianchi has been deeply involved in academic service, having served as action editor for the Machine Learning Journal, IEEE Transactions on Information Theory, and the Journal of Machine Learning Research. He currently serves as associate editor for the Journal of Information and Inference and TheoretiCS. He has held leadership positions including President of the Association for Computational Learning and member of the steering committee for the EC-funded Network of Excellence PASCAL2. He was program chair of the 13th Annual Conference on Computational Learning Theory and the 13th International Conference on Algorithmic Learning Theory. He leads the Laboratory for AI and Learning Algorithms (ALGA) at the University of Milan, which focuses on theoretical and applied research in machine learning. His international collaborations are extensive, with visiting positions at UC Santa Cruz, Graz Technical University, Ecole Normale Supérieure in Paris, Google, and Microsoft Research. As an educator, he teaches advanced courses including Reinforcement Learning and Statistical Methods for Machine Learning, and has supervised numerous students through the years.
Stephen Marshall is Professor of Urban Morphology and Urban Design at The Bartlett School of Planning, University College London. He also served as Visiting Professor at the Department of Architecture and Urban Studies, Politecnico di Milano, Italy from 2019 to 2021. With over twenty-five years of experience in the built environment fields, initially in consultancy and subsequently in academia, Professor Marshall has established himself as a leading expert in urban morphology and design. His educational background includes a Doctor of Philosophy from University College London (2001), a Postgraduate Diploma from Edinburgh College of Art (1995), a Master of Science from the University of Leeds (1989), and a Bachelor of Engineering from the University of Glasgow (1988). Professor Marshall's principal research focuses on urban morphology and street layout, examining their relationships with urban formative processes including urban design, coding and planning. His work bridges urban design theory with practical applications, exploring how cities evolve through complex interactions of physical form, social processes, and planning interventions. He has written or edited several influential books including 'Streets and Patterns' (2005), 'Cities, Design and Evolution' (2009), and 'Urban Coding and Planning' (2011). His recent publications reveal a growing interest in applying complexity science to urban morphology, with particular attention to biological analogies for understanding self-organizing cities. He has pioneered research on digital participation methods in urban planning, exploring how online platforms can enhance public engagement in urban space design. His work consistently bridges theoretical urban morphology with practical applications for contemporary urban challenges like pandemic adaptation and sustainable transport. Professor Marshall has served as Chair of the Editorial Board of Urban Design and Planning from its launch to 2012, and is now co-editor of Built Environment journal. His editorial work has significantly shaped scholarly discourse in urban planning and design. He leads several significant research initiatives including the Incubators of Public Spaces project, which explores digital platforms for co-creating urban spaces, and the Self-Organising Built Environment project, which investigates biological analogies in urbanism. These projects reflect his interdisciplinary approach to understanding and shaping urban environments, connecting with Sustainable Development Goal 11 (Sustainable Cities and Communities).
Marcia C. Linn is the Evelyn Lois Corey Professor of Instructional Science in the Berkeley School of Education at the University of California, Berkeley. She serves as Chair of the Graduate Group in Science and Mathematics Education (SESAME) and has made significant contributions to the field of science education for over five decades. Dr. Linn is a member of the National Academy of Education and a Fellow of multiple prestigious organizations including the American Association for the Advancement of Science (AAAS), the American Psychological Association (APA), the Association for Psychological Science (APS), the American Educational Research Association (AERA), and the International Society of the Learning Sciences (ISLS). Dr. Linn earned her B.A. in Psychology and Statistics (1965), M.A. in Educational Psychology (1967), and Ph.D. in Educational Psychology (1970) from Stanford University, where she worked under Lee Cronbach. Her early career included working with Jean Piaget at the Institute Jean Jacques Rousseau in Geneva, Switzerland (1967-68), serving as a Fulbright Professor at the Weizmann Institute of Science in Israel (1983), and conducting research at University College in London. She has been a fellow at the Center for Advanced Study in Behavioral Sciences three times and a Writing Resident at the Rockefeller Foundation Bellagio Center twice. Dr. Linn's research focuses on how students learn science and how technology can be used to improve science education. She developed the Knowledge Integration framework, which has become widely used in science education. Her work explores the intersection of cognitive science and educational practice, with particular attention to how students develop understanding of complex scientific concepts. She has pioneered the use of technology in science education, developing the Web-based Inquiry Science Environment (WISE) and directing the NSF-funded Technology-Enhanced Learning in Science (TELS) center. Dr. Linn's recent publications demonstrate a clear trajectory toward integrating artificial intelligence with science education. Her work increasingly focuses on how AI can support knowledge integration, facilitate science learning opportunities, and promote equitable educational experiences. She examines how technology can help students develop deeper understandings of scientific concepts through inquiry-based learning while addressing issues of social justice in science education. Scientific Awards and Honors National Association for Research in Science Teaching Award for Lifelong Distinguished Contributions to Science Education American Educational Research Association Willystine Goodsell Award Council of Scientific Society Presidents first award for Excellence in Educational Research Fulbright Professor (1983) Apple Wheels for the Mind grant (1985) National Institute of Education grant (1983) Throughout her career, Dr. Linn has secured significant funding for educational research, including multiple National Science Foundation grants. She directed the NSF-funded Technology-Enhanced Learning in Science (TELS) center and has led numerous projects investigating the cognitive consequences of computer environments for learning. She has advised countless students and researchers in the field of science education, shaping the next generation of educational researchers and practitioners. Dr. Linn directs the Web-based Inquiry Science Environment (WISE) project and has been instrumental in developing technology-enhanced learning environments for science education. Her laboratory has been at the forefront of creating and testing innovative learning technologies that support students in developing deep understanding of scientific concepts through inquiry-based approaches.
Martin D F Wong serves as the Edward C. Jordan Professor of Electrical and Computer Engineering and Executive Associate Dean for the College of Engineering at the University of Illinois at Urbana-Champaign. He is affiliated with the Coordinated Science Laboratory and has been instrumental in advancing electronic design automation research. His educational background includes: B.Sc. in Mathematics, University of Toronto (1979) MS in Mathematics, University of Illinois at Urbana-Champaign (1981) Ph.D. in Computer Science, University of Illinois at Urbana-Champaign (1987) Wong's research centers on combinatorial optimization and algorithm design for VLSI systems, with particular expertise in lithography-aware physical design, field-programmable systems, and electronic packaging. His work bridges theoretical algorithms with practical semiconductor manufacturing challenges as feature sizes shrink below 20 nanometers. Current research focuses on integrating chip design with next-generation lithography technologies including triple-patterning, self-aligned double patterning, directed self-assembly, and extreme ultraviolet processes. His publication record shows consistent contributions to electronic design automation, with emphasis on manufacturing-aware physical design algorithms and circuit optimization techniques. Recent work addresses the critical interface between circuit layout and lithography processes as semiconductor technology advances to 14nm and beyond. Scientific recognition includes: Fellow of IEEE and ACM 2000 IEEE Donald O. Peterson Best Paper Award Multiple Best Paper Awards at DAC, ICCD, and ICCAD conferences IBM Faculty Awards (2000, 2004) NSF Research Initiation Award Wong has secured significant research funding including a $450,000 NSF grant for lithography-aware physical design and has supervised over 49 PhD students. His work continues the legacy of integrated circuit innovation at Illinois, building on foundational contributions like Jack Kilby's integrated circuit invention. Current research initiatives focus on optimizing chip design for next-generation manufacturing processes where optical interference challenges require co-design of layout and fabrication. He leads research within the Coordinated Science Laboratory, focusing on electronic design automation algorithms that address the growing complexity of semiconductor manufacturing at nanometer scales.
Brett Sanders is a Professor in the Department of Civil and Environmental Engineering at the Samueli School of Engineering, University of California, Irvine. His research focuses on developing innovative algorithms for flow and transport in river and coastal systems and integrating information technologies to create more accurate and efficient simulation tools for flood hazard assessment. His primary research interests include: Flooding and erosion hazards, particularly coastal flooding and urban flooding Surface water quality Low impact development impacts on hydrology Dam-break flooding Aerial and terrestrial lidar scanning Geographical information systems High performance computing for simulation tools Social dimensions of flood risk and adaptation behaviors Dr. Sanders' recent publications (2024-2025) reveal a comprehensive research program addressing both technical and social aspects of flood risk. His work spans computational hydrodynamics, flood hazard mapping, infrastructure vulnerability assessment, and the socioeconomic dimensions of flood risk. He has made significant contributions to understanding multi-grid modeling of urban flooding, post-fire flood hazards, satellite-based monitoring of land motion, and social inequalities in flood exposure. His research demonstrates how flood dynamics are more complex than simple bath-tub filling models suggest, with important implications for urban planning and climate adaptation. Dr. Sanders has received recognition as a Chancellor's Professor at UC Irvine, indicating distinguished scholarly achievement. His educational background includes: Ph.D. in Civil Engineering from the University of Michigan (1997) M.S. in Civil Engineering from the University of Michigan (1994) B.S. in Civil Engineering from the University of California, Berkeley (1993)
Anders Krogh is a Professor at the Department of Computer Science, University of Copenhagen, and also holds a position at the Department of Public Health in the Section for Health Data Science and AI. He serves as the head of the Center for Health Data Science (HeaDS) in the Faculty of Health and Medical Sciences. Previously, he was affiliated with the Department of Biology at the University of Copenhagen until 2020. Dr. Krogh earned his PhD in theoretical physics but transitioned into machine learning and bioinformatics during his doctoral studies. His research spans both theoretical foundations and practical applications in these fields. He is particularly renowned for his pioneering work on hidden Markov models for biological sequences, which has had significant impact in computational biology. In recent years, Krogh's research has focused on deep generative models applied to gene expression data and other biomedical applications. His work bridges computer science with healthcare, developing AI-driven approaches for precision medicine, cancer diagnostics, and analysis of complex biological systems. His current research integrates machine learning with quantum computing applications in biomolecular modeling. Analysis of his recent publications reveals a strong trend toward applying artificial intelligence to healthcare challenges, particularly in rare diseases, cancer diagnostics, and personalized medicine. His work increasingly incorporates federated learning approaches to address privacy concerns while enabling collaborative research across institutions. There's also a growing emphasis on quantum computing applications in biomolecular modeling and drug discovery. As head of the Center for Health Data Science, Krogh leads interdisciplinary research efforts that bring together computer scientists, medical researchers, and clinicians. His team develops novel computational frameworks like MOSAIC for multimodal analysis of rare cancers and multiDGD for multi-omics data integration. These tools are designed to translate AI innovations into clinical practice while addressing the unique challenges of medical data.
