Armin Falk is a Professor of Economics at the University of Bonn, leading the BonnEconLab since 2004 and co-founding the briq Institute on Behavior & Inequality. He holds affiliations with the IZA, DIW, CEPR, and Max-Planck-Institut. His research focuses on behavioral economics, exploring how social preferences like fairness and trust influence decision-making, and investigates early childhood development's role in inequality. Falk has received prestigious awards, including the Leibniz Prize and Yrjö Jahnsson Award. His work combines lab experiments, field studies, and representative data analysis. Education includes a PhD in Economics from the University of Zurich after studies in Economics, Philosophy, and History at the University of Cologne. Key research highlights include global climate action support studies (Nature Climate Change, 2024), socioeconomic disparities in child development (JPE, 2021), and global economic preferences surveys (QJE, 2018). He co-authored the book *Warum es so schwer ist, ein guter Mensch zu sein* to popularize behavioral economics. Awards include recognition for contributions to behavioral and development economics. Mentoring programs like *Balu und Du* aim to reduce educational inequality. His work bridges academic research with public discourse through media contributions and policy engagement.
Olga Saukh is an Associate Professor at the Institute of Technical Informatics, Graz University of Technology (TU Graz), and a Faculty member at the Complexity Science Hub Vienna (CSH). She leads the Embedded Learning and Sensing Systems research group, which operates across both institutions, focusing on the design and deployment of efficient AI-based systems on edge and mobile platforms. Her work bridges deep learning and embedded systems, with applications in environmental monitoring, precision agriculture, and digital health. Ph.D. in Computer Science, University of Bonn (2009) Habilitation in Embedded Systems, TU Graz (2020) Postdoctoral Training, ETH Zurich (2010–2016) B.Sc. in Applied Mathematics, Taras Shevchenko National University of Kyiv (2002) M.Sc. in Applied Computer Science, University of Freiburg (2004) Her research centers on efficient machine learning, particularly model optimization, neural network pruning, and contrastive learning for resource-constrained devices. She is deeply engaged in solving real-world challenges in IoT, sensor networks, and cyber-physical systems. Her work emphasizes data privacy, sustainability, and practical deployment of AI at the edge. The 15 most recent publications highlight a strong trend in efficient deep learning, including model compression, pruning, and transfer learning, applied to diverse domains such as environmental sensing (air quality, pollution tracking), digital agriculture (cattle farming), and embedded AI (sensor calibration, on-demand sensing). Her work frequently appears in top-tier venues like NeurIPS, ICLR, and IEEE/ACM IPSN, reflecting her leadership at the intersection of machine learning and embedded systems. Scientific awards include: CONET Ph.D. Academic Award (2010) Multiple Best Paper Awards at IEEE PerCom, ACM/IEEE IPSN, IEEE ICPADS, IEEE SECON, and UrbCom Spotlight and Oral presentations at ICML and CoLLAs workshops Ph.D. scholarship from IPVS, University of Stuttgart (2004–2005) Prizes in Ukrainian national mathematics competitions (1996–1998) Olga Saukh actively serves on program committees of leading international conferences in machine learning and embedded systems. She has advised multiple students and leads a collaborative research group spanning TU Graz and CSH Vienna. Her group develops practical AI systems for real-world deployment, with a focus on sustainability and privacy. She co-organizes the public EfficientML reading group and has secured recognition through numerous grants and awards. Her future work continues to explore the theoretical and practical challenges of deploying efficient, trustworthy AI in mobile and embedded environments. Her research group, Embedded Learning and Sensing Systems, operates jointly between TU Graz and CSH Vienna, fostering interdisciplinary collaboration across institutions. The team develops AI solutions for edge computing, sensor networks, and cyber-physical systems, with a strong emphasis on environmental sustainability and data privacy. Members work on joint challenges using advanced collaboration tools, reflecting the distributed nature of modern academic research.
Rik Crutzen is a Full Professor at the Faculty of Health, Medicine and Life Sciences, Maastricht University, specializing in health promotion and digital interventions. His research focuses on leveraging technology to improve health behaviors across diverse populations, including adolescents, older adults, and immigrant communities. He leads projects in the CAPHRI research group for Promoting Health & Personalised Care. Research trends include: Digital health innovations (e.g., helplines, apps, algorithms) Chronic disease management (post-COVID-19, dementia risk) Health equity in immigrant populations (cervical cancer, maternal care) Behavioral interventions (physical activity, sleep, STI prevention) Systematic reviews and mixed-methods studies He has supervised 22 academic works and contributed to datasets on activity patterns and sleep-activity correlations. His work spans public health policy, health education, and technology integration.
Dr. Zhu Lailai serves as Assistant Professor in the Department of Mechanical Engineering at the National University of Singapore (NUS), appointed in January 2020. His research bridges fundamental fluid mechanics with cutting-edge engineering applications through computational and theoretical approaches. Dr. Zhu holds a PhD from KTH Royal Institute of Technology (Sweden) and completed postdoctoral training at Princeton University. His research program centers on: Low-Reynolds-number fluid-structure interactions and bio-inspired adaptive systems Active matter dynamics (Janus colloids, active droplets, flagella/cilia) Intelligent fluids integrating machine learning for fluid dynamics Microrobotics with reinforcement learning-based chemotactic navigation Non-Newtonian/multiphase flows and microfluidics applications Analysis of his 2017-2025 publications reveals a clear trajectory toward AI-enhanced fluid mechanics, evolving from foundational theoretical models to machine learning integration. Recent work emphasizes foundation models for fluid dynamics prediction and topology-adaptive microrobotic navigation, demonstrating interdisciplinary convergence of physics, AI, and bionics. Scientific Awards: No major scientific awards specified in source materials Advising and Grants: While specific advisees and grants aren't detailed, his active publication record across high-impact journals (Nature Communications, Journal of Fluid Mechanics) indicates ongoing supervised research and likely grant funding through NUS and collaborative projects. Research Group: Dr. Zhu leads a computational/theoretical research team at NUS investigating active and intelligent fluids, with current projects on PCM thermal systems, microrobotic navigation, and active matter phase transitions, collaborating with experimentalists globally.
Emma Brunskill is an Associate Professor of Computer Science at Stanford University, with a courtesy appointment in Education. She holds a PhD in Computer Science from MIT (2009). Her research focuses on reinforcement learning, educational technology, and healthcare applications, aiming to develop AI systems that support human learning and decision-making. Notable projects include AI tutoring systems, policy evaluation methods, and behavior change interventions using large language models. Her work bridges theory and practice, addressing challenges in off-policy evaluation, fairness-aware decision making, and scalable educational tools. Brunskill has contributed to foundational research in reinforcement learning algorithms and their applications in real-world scenarios such as healthcare, education, and human-AI collaboration. She also leads initiatives to improve equity and efficiency in educational technologies through data-driven approaches. Brunskill's research has been supported by grants such as the NSF RI: Small grant for data-efficient reinforcement learning. She actively explores the ethical implications of AI systems, particularly in healthcare and education settings. Her recent work emphasizes leveraging large language models (LLMs) for personalized feedback and simulated training environments, as seen in studies like GPTCoach and LLM-based counselor upskilling.
John Duchi is an Associate Professor at Stanford University, holding positions in the Department of Statistics and the Department of Electrical Engineering (with a courtesy appointment in Computer Science). He is affiliated with the School of Engineering. His research focuses on statistical learning, optimization, information theory, and computation, with an emphasis on balancing computational efficiency, privacy, and robustness. Key interests include developing algorithms for large-scale optimization, privacy-preserving techniques, and tools for evaluating machine learning systems' validity. Education: BS and MS in Computer Science (Stanford University, 2007–2008), MA in Statistics (UC Berkeley, 2012), and PhD in EECS (UC Berkeley, 2014). Research Interests: His work addresses three core areas: (1) optimizing trade-offs between computational resources and statistical performance, (2) creating scalable optimization methods, and (3) quantifying confidence in machine learning systems. Recent publications highlight contributions to privacy in federated learning, robust statistical validation, and uncertainty quantification. Articles: His recent work explores topics like distribution-free M-estimation, private federated learning, and instance-optimal mechanisms for statistical estimation. These studies emphasize privacy, robustness, and scalable solutions for modern data challenges. Advising & Grants: While no advisees are listed, his research has been supported by grants focused on optimization, privacy, and statistical theory.
Katja Hose is a Full Professor of Data Management at TU Wien's DBAI research unit, heading the Data Management and Knowledge-Driven AI Lab. She previously held a Poul Due Jensen Foundation Professorship at Aalborg University. Her research focuses on data and knowledge engineering, including graph databases, knowledge graphs, querying, analytics, and machine learning, with interdisciplinary applications in bioscience, healthcare, and environmental assessment. Education: PhD in Computer Science (Ilmenau University of Technology, 2009), Postdoc at Max Planck Institute for Informatics (2009–2012). Academic roles include Program Co-Chair for ISWC 2024 and EDBT 2023, and editorial board membership at VLDBJ and TGDK. She leads projects like TARGET (health virtual twins) and ARMADA (data management). Research Interests: Knowledge Graphs, Semantic Web, Big Data, Machine Learning, Data Integration, and Provenance Systems. Key contributions include SHACL shape extraction, conversational data analytics, and environmental knowledge graphs. Awards include the 2025 Distinguished Meta-Reviewer Award and 2024 Manfred Paul Award. Advising and Grants: Supervised students including E. Pürmayr (Diploma Thesis 2025). Active in EU projects (TARGET, ARMADA) and grant coordination. Labs/Teams: DMKI Lab at TU Wien, collaborating with interdisciplinary teams in healthcare and environmental science.
Dr. Nanpeng Yu is a Full Professor at the University of California, Riverside , serving as Vice Chair and Graduate Advisor in the Department of Electrical and Computer Engineering . He maintains cooperative faculty affiliations with the Department of Computer Science and Department of Statistics , and directs the Energy, Economics, and Environment Research Center at UCR. Education : B.S. in Electrical Engineering from Tsinghua University (2006), M.S. in Electrical Engineering and Economics, and Ph.D. from Iowa State University (2010) Prior Industry Experience : Senior Power System Planner and Project Manager at Southern California Edison (2011-2014) Dr. Yu’s research bridges smart grid technologies , data-driven optimization , and machine learning for energy systems. Key areas include voltage control , renewable energy integration , transportation electrification , and grid resilience . His recent work focuses on physics-informed graph learning for unit commitment problems and adversarial purification in power system classifiers. The AI-Energy Nexus Laboratory under Dr. Yu has produced impactful publications in Applied Energy , IEEE Transactions on Power Systems , and Transportation Research series, covering topics like heavy-duty EV charging infrastructure and dynamic distribution network reconfiguration . Lab members have won the American-Made Digitizing Utilities Grand Prize ($300,000) from the U.S. DOE. Leadership Roles : Chair, IEEE Power and Energy Society Distribution System Operation and Planning Subcommittee Chair, IEEE Power and Energy Society Working Group on Data-Driven Modeling for Power Distribution Networks Associate Editor, IEEE Transactions on Smart Grid and IEEE Power Engineering Letters Scientific Awards : Regents Faculty Fellowship Regents Faculty Development Award Multiple IEEE Best Paper Awards
Sergio Moreno Gil is a Professor in the Department of Economics and Business Management at Universidad de Las Palmas de Gran Canaria (ULPGC), Spain. He is affiliated with the GIR TIDES research group (Economy, Environment, Sustainability and Tourism) and the Institute of Tourism and Sustainable Economic Development. His academic work focuses on tourism economics, marketing, and sustainability within the Social and Legal Sciences branch. Dr. Moreno Gil's research interests span across multiple dimensions of contemporary tourism. His primary focus areas include sustainable tourism development, destination marketing, sharing economy platforms in tourism, and consumer behavior analysis. He has made significant contributions to understanding tourism in the Canary Islands, particularly through his work with the Canary Islands Tourism Observatory. His research integrates traditional tourism studies with emerging fields like neurotourism and biometric analysis of tourist experiences. His publication record shows a clear trend toward addressing contemporary challenges in tourism. Recent works focus on sustainability in mass tourism destinations, the impact of sharing economy platforms, gastronomic tourism marketing, and collaborative tourism networks. His research demonstrates a strong methodological diversity, employing bibliometric analysis, social network analysis, and empirical studies of tourist behavior. Dr. Moreno Gil has been actively involved in the Canary Islands Tourism Observatory, contributing to annual reports on tourism sustainability in the region. His collaborative work spans across the Ibero-American region, with numerous co-authorships reflecting international research networks in tourism studies.
Clio Andris is an Associate Professor at Georgia Tech, jointly appointed in the School of City and Regional Planning and the School of Interactive Computing. She directs the Friendly Cities Lab, focusing on mathematical models of social networks applied to urban planning, transportation, and geography. Her work integrates spatial analysis with visualization, emphasizing interdisciplinary collaboration. Education and Career: Andris earned a PhD in Urban Information Systems from MIT (2011), where she was an NDSEG Fellow. She held postdoctoral positions at Singapore-MIT Alliance for Research and Technology and the Santa Fe Institute. Prior to Georgia Tech, she was a faculty member at Penn State’s Department of Geography, affiliated with the GeoVISTA Center. Research Focus: Her research bridges social networks, geovisualization, and urban informatics. Key areas include spatial social network analysis, GIS applications for urban policy, and the impact of digital tools on civic engagement. She has developed innovative visual analytics tools like SNoMaN and ROBIN to democratize spatial data exploration. Awards: She received the NSF CAREER Award (2021) and NDSEG Fellowship (2011). Her lab is affiliated with the Center for Spatial Planning Analytics and Visualization (CSPAV) and the Information Visualization Lab. Labs and Collaborations: The Friendly Cities Lab focuses on socially just urban design through computational methods. Her work addresses issues like food security networks, pandemic impacts on biodiversity, and community mapping for activism. Grants and Outreach: Her NSF-funded projects emphasize public good applications, such as real-time pandemic risk communication and educational tools for migration data. She actively collaborates with non-profits and policymakers to translate research into actionable urban strategies.
Alena Thiel is an Associate Professor at the Centre of African Studies within the Faculty of Social Sciences at the University of Copenhagen. Her research examines digital technologies' impact on state formation and knowledge systems across Africa, with primary field sites in Ghana, Senegal, Botswana, and Kenya. Her core research areas include: State knowledge systems (biometric ID, census histories, DHIS2 health records) Digital governance (addressing systems, payment solutions, disaster risk datafication) African/feminist AI development and implementation Anthropology of the state through Science and Technology Studies (STS) frameworks Recent publications (2024-2025) reveal concentrated analysis of health data infrastructures in Ghana, trust dynamics in Africa's global networks, and critical sociological examinations of digital identification. Her work consistently bridges anthropological theory with practical governance challenges in African contexts. As an active member of the Centre of African Studies, she maintains interdisciplinary collaborations across multiple African countries and European institutions, with particular emphasis on Ghanaian research partnerships and editorial leadership for Africa Spectrum journal.
Scott D. Landes is a Professor in the Sociology Department at Syracuse University's Maxwell School of Citizenship and Public Affairs. He holds affiliations with the Aging Studies Institute, Lerner Center for Public Health Promotion, and Center for Aging and Policy Studies. Landes earned his Ph.D. in Sociology from the University of Florida in 2014. His research focuses on disability mortality disparities, veteran health outcomes, and the intersection of disability with medical and social theory. Education: Ph.D. in Sociology, University of Florida, 2014 Research Interests: Landes investigates mortality trends for disabled adults and veterans, disability theory integration with life course frameworks, and data equity in disability measurement. His work addresses systemic undercounting of disability in federal surveys and explores racial/ethnic disparities in lifespan outcomes for individuals with intellectual/developmental disabilities (IDD). Recent Research Trends: His publications analyze pandemic impacts on IDD populations, combat veteran longevity, and diagnostic overshadowing in mortality reporting. Recent work critiques Washington Group disability measures' limitations and advocates for improved data collection methods. Awards: 2025 Excellence in Graduate Education Faculty Recognition Award O’Hanley Faculty Scholar (Syracuse University) NIH-funded research projects on aging policy and mortality disparities Advising & Grants: Recipient of multiple National Institutes of Health grants. Advises on disability data equity initiatives and participates in interdisciplinary teams addressing veteran health disparities. Supervises graduate research on IDD lifespan trajectories and pandemic vulnerability. Labs/Teams: Core member of Syracuse’s Aging Studies Institute and Lerner Center for Public Health Promotion. Collaborates with the Center for Aging and Policy Studies on federal health data projects. Engages with national disability advocacy groups through research briefs and policy consultations.
Karin Allor Pfeiffer is a Professor in the Department of Kinesiology at Michigan State University (MSU) and Director of the Institute for the Study of Youth Sports. She holds additional membership in the Center for Physical Activity and Health. With a Ph.D. from MSU, her research focuses on physical activity measurement methodologies and population health interventions, particularly among children and adolescents. Her work addresses obesity prevention, environmental design impacts on activity levels, and sociocultural factors influencing youth sport participation. Education: Ph.D. in Kinesiology from Michigan State University Her research interests emphasize: - Quantitative methods for physical activity assessment - Schoolyard redesign strategies and their health impacts - Cardiometabolic risk factors in pediatric populations - Longitudinal tracking of physical fitness and health outcomes Recent work explores accelerometer fragmentation metrics, GPS-linked activity tracking, and disparities in sedentary behavior across demographic groups. She has pioneered interdisciplinary approaches integrating spatial analysis, wearable technology, and policy evaluation. Key contributions include developing the Observational System for Recording Physical Activity in Children and advancing consensus methods for accelerometer data interpretation. Her studies frequently highlight socioeconomic and environmental determinants of health behaviors. Dr. Pfeiffer collaborates with public health agencies and urban planners to translate research into actionable policies. Her lab focuses on scalable interventions for underserved communities, leveraging community-engaged methods to address greenspace accessibility and safety concerns.
Gabriel Birzu is an Assistant Professor in the Department of Physics at the University of Florida. He develops quantitative models of microbial ecology and evolution using statistical physics approaches. His research investigates fine-scale diversity in microbial communities, examining how spatial processes shape evolutionary trajectories. Recent work analyzes hybridization barriers in cyanobacteria and genealogical patterns during range expansions. Birzu's interdisciplinary approach combines theory, computation, and data analysis to understand microbial diversification mechanisms and community responses to environmental perturbations.
Dr. Jeewanie Jayasinghe Arachchige is a Lecturer in the Department of Computer Science at Vrije Universiteit Amsterdam, Faculty of Science. She teaches undergraduate courses including Bachelor Project Computer Science, Professional Development, and Software Engineering Processes for the academic year 2024–2025. Her research focuses on process mining , healthcare informatics , and data security . She applies process mining to analyze healthcare pathways and subpopulation treatment variations, develops explainable AI frameworks for predictive analytics, and examines data governance in emerging architectures like Data Lakehouses. Her work intersects legal informatics, particularly formalizing Sri Lankan civil court processes using ontology engineering. Recent publications highlight trends in balancing simplicity and complexity in process modeling, Industry 4.0 healthcare applications, and cybersecurity in model-driven web development. She has contributed to over 20 peer-reviewed articles since 2006, spanning topics from service-oriented architectures to value network analysis. Her teaching and research emphasize practical applications of IT in healthcare, legal systems, and enterprise environments. No ancillary activities are currently recorded.
