Prof. Sergio Ginebri is an Associate Professor at the Department of Jurisprudence, University of Rome Tre. He holds a PhD in Economics from Sapienza University of Rome and has extensive experience in fiscal policy, pension systems, and public finance research. His work focuses on topics like pension sustainability, social inequality, and the political economy of public debt. Education: PhD in Economics, Sapienza University of Rome (1988–1992) MA in Economics, University of Warwick (1987–1988) Laurea in Statistics and Demography, Sapienza University of Rome (1977–1984) Research Interests: Prof. Ginebri specializes in public policy, fiscal sustainability, and the social impacts of pension reforms. His research explores how demographic trends, wealth distribution, and political dynamics shape economic policies. Recent work includes analyses of longevity risks, pension equity, and fiscal policy integration in the EU. Key Projects: Coordinated studies on pension system sustainability (2002–2018) Developed models for forecasting public pension expenditure Contributed to EU tax policy observatories Awards: Recipient of the national PhD award (1994) and recognition for academic excellence in tax policy research (2001). Teaching: Teaches courses in political economy, public finance, and macroeconomics at undergraduate and graduate levels.
Todd Sorensen is an Associate Teaching Professor at the University of California, Merced (UC Merced), affiliated with the School of Social Sciences, Humanities & Arts and the Economics and Business Management department. He holds fellowships at IZA (Institute of Labor Economics) and GLO (Global Labor Organization). Prior to UC Merced, he served as an Associate Professor at the University of Nevada, Reno (2014–2023) and an Assistant Professor at UC Riverside (2007–2014). His professional journey includes a visiting role as Visiting Associate Professor at UC Merced before his current appointment in 2022. Education: PhD in Economics, University of Arizona, 2007 Research Interests: Todd's work focuses on labor market structures , exploring topics like monopsony power and wage-setting dynamics; immigration , including its demographic and policy implications; and discrimination , particularly in criminal sentencing and labor markets. His research has been published in journals like ILR Review , Labour Economics , and the Journal of Population Economics , with findings cited in the U.S. Economic Report of the President. Scientific Awards: Fellow at IZA (2022–present) Fellow at GLO (2022–present) Advising & Grants: Todd has advised students across various levels but no specific advisees are listed. His grants and funding sources are not detailed in the provided text. He collaborates with interdisciplinary teams and maintains affiliations across UC campuses through his and his spouse’s academic careers. Labs/Teams: Engaged with research networks at IZA and GLO, focusing on labor market policies and migration studies. Active in collaborative projects analyzing immigration trends, labor market competition, and discrimination metrics.
Professor Nikos Tzavidis is a Professor of Statistical Methodology at the University of Southampton . He has held academic positions at institutions including University College London and the University of Manchester, and currently leads research initiatives integrating statistical methodology with geospatial and survey data. His work focuses on Small Area Estimation, official statistics, and poverty mapping, with applications across global development and public policy. Research interests: Small Area Estimation and Official Statistics Outlier Robust Inference Quantile and M-quantile Models Geospatial Data Analysis Poverty Mapping Machine Learning Applications in Statistics Recent research trends emphasize machine learning integration, geospatial data utilization, and advanced quantile modeling for poverty and demographic studies. His publications span journals like the Journal of Official Statistics and the Journal of the Royal Statistical Society . Scientific awards: ISI Fellow (2013) American Statistical Association (2004) Vice Chancellor's Teaching Award (2013) Johann von Spix Professor (2023) He supervises PhD students in social statistics, demography, and geography, and serves on editorial boards for journals including the Journal of Official Statistics . Externally, he has held leadership roles in the International Statistical Institute and contributed to United Nations and World Bank projects.
Charles Rahal is an Associate Professor in Data Science and Informatics at the University of Oxford, with additional affiliations as an Associate Member of Nuffield College and Researcher at the Gradel Institute, New College. He serves as a Co-Investigator at the ESRC Centre for Care and sits on the Steering Group of Reproducible Research Oxford. His academic career includes previous roles as a Senior Departmental Research Lecturer at the Leverhulme Centre for Demographic Science and a British Academy Postdoctoral Fellow. Dr. Rahal completed his PhD in 2016 and has established himself as a prominent social science methodologist and applied social data scientist with expertise in high-dimensional econometrics. His research spans multiple domains, focusing particularly on unique Big Data origination processes and their relationship to social inequality, mobility, and stratification. He is deeply engaged in machine learning methods, civic technology, spatial and time series econometrics, model uncertainty, and scientometrics. His recent publications reveal a strong trend toward computational social science, with significant contributions to understanding prediction limits, pandemic impacts, healthcare systems, and environmental sustainability. The articles demonstrate his interdisciplinary approach, bridging traditional social science with cutting-edge computational methods, particularly in the analysis of large-scale datasets and development of novel metrics like the InterModel Vigorish for model comparison. Dr. Rahal is actively involved in teaching and mentoring, co-convening courses in Demographic Analysis, Life Course Research, and the Oxford Partner site of the Summer Institute in Computational Social Sciences. He has developed workshops on machine learning, command line interfaces, and LaTeX, reflecting his commitment to methodological training in social science. He leads the Metrics and Models lab and maintains several open-source projects including the GWAS Diversity Monitor and RobustiPy. His editorial roles include Associate Editor-in-Chief at the Journal of Social Computing and Associate Editor at ACM Transactions on Social Computing, highlighting his influence in shaping computational social science methodology.
Professor Alexandra M. Schmidt is a leading academic in Biostatistics at McGill University, holding an endowed University Chair. She specializes in spatial and spatio-temporal modeling, particularly in epidemiology and environmental health. Previously, she served as a Full Professor at the Federal University of Rio de Janeiro (2012–2016). Her research focuses on Bayesian methodologies for analyzing complex processes, including disease spread, environmental hazards, and socio-economic disparities. She has authored influential books such as Spatio-Temporal Methods in Environmental Epidemiology with R (2023) and contributed to over 150 peer-reviewed articles. Key awards include the ISBA Fellowship (2024), ASA Fellowship (2020), and the Abdel El-Shaarawi Award (2008). Education: PhD in Statistics (2001, University of Sheffield, UK), MSc and BSc in Statistics (Federal University of Rio de Janeiro, Brazil). Research interests span Bayesian inference, spatial statistics, and environmental epidemiology. She has advised numerous PhD/MSc students and collaborated on projects linking statistical methods to public health challenges, such as modeling dengue outbreaks and air pollution impacts. Active in academic service, she has chaired major conferences (e.g., 2022 ISBA World Meeting) and serves on editorial boards of top journals like Bayesian Analysis and Canadian Journal of Statistics . Teaching includes advanced courses on generalized linear models, spatial epidemiology, and Bayesian analysis. Her work bridges theoretical statistics with practical applications, addressing global health issues through innovative spatio-temporal modeling techniques.
Ying Lu is an Associate Professor at the Department of Applied Statistics, Social Science, and Humanities within the Steinhardt School of Culture, Education, and Human Development at New York University. She holds dual PhDs in Public Policy and Demography from Princeton University (2005) and in Statistics from the University of North Carolina at Chapel Hill (2009). Before joining NYU, she was an Assistant Professor at the University of Colorado Boulder, affiliated with the Institute of Behavioral Science. Her research focuses on quantitative methodology in social and behavioral sciences, including applications in demography, health, and political behavior, as well as statistical methods like model selection and hypothesis testing for high-dimensional data. Her interdisciplinary work bridges statistical rigor with real-world societal challenges. Recent articles highlight her contributions to areas such as employee outcomes in HRM, gut microbiota-cardiometabolic disease links, and innovative clinical trial designs. She has also explored topics in food science, environmental catalysis, and sustainable development policy. Ying Lu’s academic journey reflects a commitment to advancing statistical methodologies while addressing pressing issues in health, policy, and environmental science. She has advised numerous projects but no specific students are listed in the provided texts.
David Steinsaltz is an Associate Professor of Statistics at the University of Oxford, affiliated with Worcester College. His research focuses on stochastic processes, biodemography, survival analysis, and Bayesian methods, with applications to aging, mortality, and population dynamics. He holds a PhD in probability theory from Harvard University, followed by postdoctoral work at UC Berkeley. His work bridges theoretical probability and applied statistics, addressing questions in demography, ecology, and epidemiology. Education: PhD in Mathematics (Probability Theory), Harvard University (1996); Postdoctoral Research, UC Berkeley (Departments of Demography and Statistics). Research interests include stochastic flows, Markov processes, and statistical methods for longitudinal data. He contributes to interdisciplinary projects, such as earthquake impact modeling and vaccine efficacy analysis. His collaborations span fields like biostatistics, ecology, and machine learning. He advises students on topics including survival analysis and demographic modeling.
Hanna Halaburda is an Associate Professor of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University, where she joined in 2019. Her research lies at the intersection of economics, technology, and digital platforms, with a strong focus on blockchain, cryptocurrencies, and platform competition. She has published extensively in top academic journals and co-authored the seminal book Beyond Bitcoin: The Economics of Digital Currencies . PhD in Economics, Northwestern University MA in Economics, Warsaw School of Economics MA in Philosophy, Warsaw University Her research interests center on the economic implications of digital transformation. She investigates how blockchain technology reshapes trust, governance, and competition in digital markets. Her work explores token design, consensus mechanisms, smart contracts, and the strategic use of decentralization in platforms. She also studies platform competition under network effects, consumer choice, and omnichannel marketing. A recurring theme is how digital technologies alter traditional economic forces and business models. The most recent articles show a strong trend toward analyzing the governance, security, and economic design of blockchain systems. Her work combines rigorous theoretical modeling with empirical insights, often applying game theory and industrial organization frameworks. Topics include permissioned vs. permissionless blockchains, the role of cryptographic tokens in coordination, and the macroeconomic implications of digital currencies. She also contributes to debates on Web3, AI, and the future of digital platforms. Scientific awards and recognitions include: ISR Best Paper Published in 2022 Runner-Up Lead article in RAND Journal of Economics Best Paper Award at Tokenomics 2023 Best Paper Award at WISE 2023 Best Paper Finalist at WISE 2022 and WISE 2021 Hanna Halaburda has advised and collaborated with numerous researchers and institutions. Her co-authors include leading scholars from Harvard, NYU, and international universities. She has received research recognition through best paper awards and invitations to contribute to high-impact journals and policy discussions. Her work has been supported by academic and policy institutions, including the Bank of Canada, where she previously served as a senior economist. She frequently publishes in both academic and practitioner outlets, including Harvard Business Review and Nature Human Behavior , indicating strong translational impact. She is actively involved in research teams focused on digital assets, blockchain governance, and platform economics. While no formal lab is mentioned, her extensive list of working papers and collaborations suggests leadership in a dynamic research group at NYU Stern. Her recent work on DAOs, public crypto mining firms, and CBDCs indicates ongoing, forward-looking research programs with real-world policy and business implications.
Hiroyuki Iseki is Associate Professor of Urban Studies and Planning at University of Maryland's School of Architecture, Planning and Preservation, and Research Affiliate with the National Center for Smart Growth. His work examines interactions between transportation, land use, equity, and environmental sustainability. Research focuses on: Transit-oriented development impacts on firm location Equity in public transit finance Climate policy implementation in urban planning Active transportation infrastructure analysis Recent publications analyze post-pandemic transit demand shifts, campus multimodal conflicts, and bicycle accessibility modeling. Methodological strengths include spatial econometrics, GIS analysis, and longitudinal data approaches. Work increasingly addresses climate adaptation in transportation planning and EV charging grid impacts. Research contributes to urban policy through WMATA collaborations and Japanese municipal climate planning studies. Current PhD program directorship advances urban planning analytics training.
Gary King is the Albert J. Weatherhead III University Professor at Harvard University and Director of the Institute for Quantitative Social Science. He is based in the Department of Government within Harvard's Faculty of Arts and Sciences. One of only 22 University Professors at Harvard, this represents the institution's most distinguished faculty position. King received his B.A. from SUNY New Paltz in 1980 and his Ph.D. from the University of Wisconsin-Madison in 1984. His academic journey has led him to become one of the most influential scholars in political methodology and quantitative social science. Professor King's research spans numerous areas of methodological innovation in the social sciences. His work focuses on developing and applying empirical methods across various domains. Key research interests include: Ecological Inference - developing methods to infer individual behavior from group-level data Automated Text Analysis - creating techniques for extracting knowledge from massive text collections Causal Inference - methods for detecting and reducing model dependence in causal effect estimation Missing Data and Measurement Error - statistical approaches to handle incomplete or imperfect data Survey Research - developing methods for more accurate cross-cultural survey comparisons Unifying Statistical Analysis - integrating diverse methodological approaches into coherent frameworks King's recent publications demonstrate a continued focus on methodological innovation with practical applications. His work spans political science, public health, and data science, with particular emphasis on privacy-preserving data analysis, maternal health metrics, survey methodology, and media effects. A notable trend is the increasing interdisciplinary nature of his research, bridging political methodology with public health, computer science, and demography. His work on census data privacy, maternal mortality disparities, and media influence represents cutting-edge applications of social science methodology to critical societal issues. His scientific achievements have been recognized with numerous prestigious awards: Fellow of the National Academy of Sciences (2010) Fellow of the American Statistical Association (2009) Fellow of the American Academy of Arts and Sciences (1998) Guggenheim Foundation Fellow (1994-1995) Career Achievement Award (2010) Warren Miller Prize (2008) Multiple awards for research software and methodology King has mentored numerous students and postdocs, many of whom now hold faculty positions at leading universities. His research has been supported by major funding agencies including the National Science Foundation, Centers for Disease Control and Prevention, World Health Organization, and National Institute of Aging. He has collaborated with over seventy scholars on research publications and served on numerous editorial boards and professional organization councils. His work on the Mexican universal health insurance program represents one of the largest randomized health policy experiments to date, demonstrating his commitment to rigorous evaluation of real-world policy interventions. As Director of the Institute for Quantitative Social Science, King leads a vibrant research community focused on methodological innovation. His work has practical applications in diverse areas including legislative redistricting (used by the U.S. Supreme Court), health policy evaluation (including the largest randomized health policy experiment to date in Mexico), Chinese censorship analysis (revealing government fabrication of 450 million social media comments annually), and automated text analysis (through Crimson Hexagon, a company he co-founded).
Ramina Sotoudeh is an Assistant Professor of Sociology at Yale University with a secondary appointment in Statistics & Data Science. Her research bridges sociogenomics, the sociology of culture, and social inequality, focusing on how genetic and social environments interact to shape human behavior. Education : BA in Social Research and Public Policy from NYU Abu Dhabi, PhD in Sociology from Princeton University Postdoctoral Experience : Fellow at Nuffield College, University of Oxford Ramina’s work in sociogenomics examines how institutional, relational, and genetic contexts influence health outcomes, such as smoking behavior and peer interactions. Her sociology of culture projects use relational methods to explore cultural frameworks underlying attitudes toward science, religion, politics, and marriage. She also investigates health disparities and inequality through interdisciplinary lenses. Her most recent publications analyze genomic population structure, behavioral plasticity, and computational approaches to algorithm selection. Earlier works focus on cultural attitudes, behavioral diffusion in networks, and genetic correlations with education and longevity. These studies span journals like American Sociological Review , PNAS , and Demography .
Christof Bigler is an Adjunct Professor at ETH Zurich's Department of Environmental Systems Science, where he also serves as Deputy Group Head of the Professorship of Forest Ecology and Senior Scientist. He leads the ETH Tree-Ring Laboratory and teaches courses in dendroecology, applied statistics, and forest ecology. His research focuses on tree mortality, climate impacts on forests, disturbance ecology, and phenology, with a strong emphasis on quantitative methods and long-term data analysis. Bigler holds a PhD in Forest Ecology from ETH Zurich (2003), awarded the ETH Medal for his thesis on tree mortality modeling. He has conducted postdoctoral research in Switzerland and the University of Colorado Boulder, exploring disturbance interactions and dendroecology. He has supervised numerous PhD and master's students, contributing to over 150 publications in top journals like Nature Climate Change and Ecology . His work integrates dendrochronological techniques with statistical modeling to understand forest responses to climate change, including drought effects, tree competition, and mortality patterns. He serves on editorial boards for journals like Agricultural and Forest Meteorology and reviews proposals for national and international science foundations. His research also includes managing forest reserves and studying natural forest dynamics in the Alps and beyond. Education: PhD in Forest Ecology, ETH Zurich (2003) Postdoctoral Research: University of Colorado Boulder (2004–2005) Diploma in Biology, ETH Zurich (1998) Key Awards: ETH Medal for PhD thesis (2003) Professional Roles: Member of the Tree-Ring Society, Editor for Frontiers in Forests , and Commission for Phenology and Seasonality (KPS). Bigler's lab and fieldwork emphasize long-term datasets, including analyses of tree-ring records from Swiss National Park and other reserves. His research highlights the critical role of historical climate data in predicting future forest resilience and adaptation strategies.
Elizabeth Bruch is an Associate Professor of Sociology and Complex Systems at the University of Michigan, serving as Associate Director of the Institute for Data and AI in Society. She holds External Faculty status at the Santa Fe Institute and is affiliated with the Center for Population Studies. With a Ph.D. from UCLA and an M.S. in Statistics, her research integrates choice modeling, network science, and agent-based simulations to study individual decisions in social environments. Key areas include residential segregation, dating markets, and higher education. Education: Ph.D. and M.S. in Sociology/Statistics (UCLA), B.A. in Sociology (Reed College) Affiliations: Santa Fe Institute, Institute for Advanced Study Berlin Her work has been published in Science , PNAS , and American Journal of Sociology , earning awards like the ASA Methodology Innovation Prize and the Merton Prize. Her upcoming book Date Like a Local (Princeton, 2026) explores urban influences on romantic behavior. Bruch’s research addresses societal challenges through computational methods, including pandemic modeling during the 2020 crisis and algorithmic analysis of dating markets. She serves on Santa Fe Institute’s Science Steering Committee and collaborates across disciplines to advance complexity science.
Guanglei Hong is a Professor at the University of Chicago, holding tenure in the Comparative Human Development Department and the Committee on Education. She chairs the University-wide Committee on Quantitative Methods in Social, Behavioral, and Health Sciences and the Committee on Education. Her research focuses on causal inference methodologies for evaluating educational and social policies, particularly mediation and moderation effects in multi-level longitudinal studies. She developed the RMPW and MMWS methods, widely used in causal mediation analysis. Hong holds a Master’s in Applied Statistics and a Ph.D. in Education from the University of Michigan. Education: Ph.D. in Education, University of Michigan, 2004 Master's in Applied Statistics, University of Michigan, 2002 Research Interests: Hong’s work centers on causal moderation and mediation, spillover effects, and sensitivity analysis in policy evaluation. She applies these methods to assess impacts of educational programs, contextual changes, and socioeconomic factors on child/youth development. Her monograph *Causality in a Social World* (2015) is a foundational text in the field. Awards: John Simon Guggenheim Fellowship (2021–2022) William T. Grant Scholar Award (2009–2014) NAE/Spencer Postdoctoral Fellowship (2006–2007) AERA Mary Catherine Ellwein Dissertation Award (2005) Teaching & Training: Hong teaches advanced quantitative methods courses, including causal inference and mediation analysis. She leads the NSF-funded SIARM for STEM institute, training researchers in computational methods for education research. Notable courses include *Advanced Topics in Causal Inference* and *Mediation, Moderation, and Spillover Effects*. Grants & Leadership: Hong leads major grants from NSF, IES, and private foundations. Her current projects include methodological advancements for multisite trials and sensitivity analysis in mediation. She has co-authored over 60 peer-reviewed articles and edited volumes, and serves on editorial boards of leading journals. Labs/Teams: Hong directs the Quantitative Methods Group at the University of Chicago, fostering interdisciplinary collaborations in causal inference and policy evaluation. Her work integrates statistical innovation with real-world applications in education and health sciences.
Deborah Balk is a Professor at the Marxe School of Public and International Affairs at Baruch College, part of the City University of New York (CUNY). She also serves as Director of the CUNY Institute for Demographic Research and holds appointments in the CUNY Graduate Center's Sociology and Economics programs, as well as the CUNY School of Public Health's Epidemiology Program. Her expertise lies in spatial demography, integrating earth and social science data to address policy challenges related to urbanization, climate change, and population dynamics. Dr. Balk has led significant roles in climate assessments, including Co-Chair of the New York City Panel on Climate Change’s 4th Assessment (2019–2024) and membership in the U.S. National Climate Assessment’s 6th Health Chapter (2025). She holds a PhD in Demography from UC Berkeley and degrees from the University of Michigan (MPP and AB in International Relations). Her research focuses on urbanization, migration, poverty, health, and environmental interactions, particularly climate adaptation and equity. Notable projects include analyzing population vulnerability in coastal zones and developing spatial demographic tools for global health and policy. Awards include the Andrew Carnegie Fellowship (2016–2018) and the William and Flora Hewlett Foundation Fellowship (1991). Dr. Balk has secured grants exceeding $6 million from NSF, NASA, and others, supporting work on urbanization, climate justice, and demographic data integration. She advises multiple institutions, including the U.S. Census Bureau and National Academy of Sciences. Her teaching spans spatial demography, urban policy, and statistical methods, reflecting her commitment to bridging demographic science and real-world applications.
