Johnny Ducking is an Associate Professor in the Department of Economics at the University of Mississippi, College of Liberal Arts, where he teaches Principles of Microeconomics and Sports Economics. He joined the university in 2024, bringing over a decade of academic experience from North Carolina A&T State University. Education: B.A. in Economics, University of Mississippi (2004) B.A. in Mathematics, University of Mississippi (2004) M.A. in Economics, University of Mississippi (2006) M.S. in Economics, University of Kentucky (2007) Ph.D. in Economics, University of Kentucky (2011) Dr. Ducking's research centers on labor economics in sports, with a focus on how race, gender, and personal characteristics influence labor market outcomes. He analyzes data from major professional leagues including the NFL, NHL, MLB, NBA, and Women’s NCAA Division I Basketball. His work explores the impacts of minimum salaries and systemic biases in player compensation and opportunities. Dr. Ducking is actively involved in academic service, having served as co-editor of a special issue of the Journal of Economics, Race, and Policy on race, gender, and sports, and currently serving on the editorial board of the International Journal of Sport Finance . He advises students in the Economics B.A., B.S., M.A., and Ph.D. programs and contributes to the academic mission of the Department of Economics at Ole Miss.
Emma Pierson is an Assistant Professor of Computer Science at the University of California, Berkeley, affiliated with the Berkeley Artificial Intelligence Research Lab (BAIR) , Computational Precision Health , and the Center for Human-Compatible AI . She focuses on developing data science and machine learning methods to address issues in healthcare equity and social inequality . Her work includes studies on race adjustments in clinical algorithms, migration patterns, and leveraging LLMs for health equity. Education: Ph.D. in Computer Science from Stanford University (2020), Master’s in Statistics from the University of Oxford. Prior roles include Assistant Professor at Cornell Tech, Senior Researcher at Microsoft Research, and data scientist at 23andMe and Coursera. Research Interests: Her research spans fair clinical prediction , sparse autoencoders , health disparities , and algorithmic fairness . Notable projects include the MIGRATE dataset for granular migration analysis and studies on policing disparities. Awards: NSF CAREER Award, Rhodes Scholarship, Hertz Fellowship, MIT Technology Review 35 Innovators Under 35, and Samsung AI Researcher of the Year. She writes a statistics blog ( Obsession with Regression ) and contributes to media outlets like The New York Times and FiveThirtyEight . Labs/Teams: Leads the MIGRATE project, a collaboration to analyze fine-grained migration data. Engages in interdisciplinary work across AI, healthcare, and social science.
Katia Bianchini is a Research Fellow at the Max Planck Institute for Social Anthropology in Halle, Germany, with a focus on the intersection of law and anthropology in refugee and immigration contexts. She holds a PhD in Law (University of York), an LLM in Comparative Laws (University of San Diego), and a Law degree (Università di Pavia). Her work bridges empirical legal analysis with anthropological methodologies to address issues such as statelessness, asylum adjudication, and the rule of law in migration policies. Education PhD in Law, University of York (2011–2016) LLM in Comparative Laws, University of San Diego (1999–2000) Law degree, Università di Pavia (1992–1998) Bianchini’s research interests include refugee law , immigration law , statelessness , EU law , and human rights law . Her current project examines legislation and responses to missing and deceased sea migrants in Italy , analyzing legal frameworks for search, identification, and burial practices, particularly along the Central Mediterranean Route. She emphasizes the importance of legal anthropology to understand gaps between abstract law and its real-world application. The trends in her publications reflect a focus on statelessness determination procedures , asylum law (notably witchcraft-based claims), humanitarian visas , and rule of law challenges in migration contexts. Her work spans journal articles , book chapters , and monographs , often combining legal analysis with empirical data from interviews and fieldwork. Scientific Awards Erasmus scholarship, University of Leuven, Belgium (1997–1998) Grants and Professional Activities include visiting researcher roles at Fordham University and the University of Oxford, as well as editorial work for the Statelessness and Citizenship Review and the CUREDI Database . She has presented extensively on topics like statelessness in the UK , cultural diversity in asylum adjudication , and NGO criminalization in Mediterranean rescue operations .
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).
Armando Rungi is a Professor of Economics at IMT School for Advanced Studies in Lucca, Italy. He teaches econometrics, international economics, and macroeconomics to PhD students. In addition to his academic role, he serves as a research fellow at the Observatory on Foreign Firms in Italy and has consulted for the European Commission, OECD, and UNCTAD on international trade and investment issues. His research focuses on international economics, industrial organization, applied econometrics, and statistical learning. Recent work emphasizes the organization of multinational enterprises, global value chains, labor markets, cyber-resilience of supply chains, and the integration of econometric and machine learning tools for policy evaluation and predictive analysis. His recent publications explore topics such as the impact of trade agreements, multinational enterprises' strategies, and the application of machine learning in predicting firm behaviors and evaluating economic policies. A common theme is the analysis of supply chain resilience, corporate ownership structures, and the effects of globalization on firms' competitiveness and productivity. No scientific awards are mentioned in the provided information. No advisees or grant details are listed in the text. His professional activities include consulting roles and research collaborations. He is affiliated with the Observatory on Foreign Firms in Italy, which evaluates the impact of multinational companies and strategies to attract foreign investment in Italy.
Karen Panetta is a Professor at Tufts University School of Engineering with appointments in Electrical and Computer Engineering, Computer Science, Mechanical Engineering, and Academic Services. She currently serves as Dean of Graduate Education for the School of Engineering and holds the title of Distinguished Professor. Ph.D. in Electrical Engineering, Northeastern University M.S. in Electrical Engineering, Northeastern University B.S. in Computer Engineering, Boston University Dr. Panetta's research focuses on developing efficient algorithms for simulation, modeling, and signal and image processing for security and biomedical applications. Her work brings together artificial intelligence, machine learning, and visual sensing systems to create solutions for robot vision and biomedical imaging. She develops algorithms inspired by the human visual system to enable machines to 'see' like humans, with applications in homeland security, biomedicine, facial recognition, and search and rescue operations. Her research has significant humanitarian applications, addressing global challenges facing women and children. Dr. Panetta has received numerous prestigious awards including induction into the National Academy of Engineering (2023), the Presidential Award for Science and Engineering Education and Mentoring (2011), and the IEEE Award for Distinguished Ethical Practices (2013). She is a fellow of multiple prestigious academies including the National Academy of Inventors, European Academy of Sciences and the Arts, and IEEE. Member, National Academy of Engineering (2023) Presidential Award for Science and Engineering Education and Mentoring (2011) IEEE Award for Distinguished Ethical Practices (2013) Fellow, National Academy of Inventors Fellow, European Academy of Sciences and the Arts Fellow, Asia-Pacific Artificial Intelligence Association As an educator and mentor, Dr. Panetta founded the nationally acclaimed Nerd Girls program to promote engineering to young students, particularly women. She previously served as worldwide director for IEEE Women in Engineering and editor-in-chief of the IEEE Women in Engineering magazine. Her approach to graduate education emphasizes the importance of building strong collaborative relationships between faculty and students, with a focus on proactive communication and documentation of research progress. Dr. Panetta's humanitarian research applies engineering solutions to global challenges, including developing technology to help doctors find cancerous tumors, security screeners find concealed weapons, and law enforcement agencies find criminals and missing children. Her work demonstrates a commitment to 'Doing The Right Thing' by addressing issues affecting populations with limited resources or 'voice' in society.
Dr. Zhaohai Li Professor of Statistics at George Washington University, specializing in statistical methodologies for genetic epidemiology and clinical biostatistics. His research focuses on meta-analysis techniques, empirical Bayes methods, and population-based study designs. He has contributed extensively to improving statistical approaches in clinical trials and addressing challenges in genetic association studies. Education: Ph.D. in Statistics, Columbia University, 1989 Research Interests: His work addresses critical issues in modern biostatistics including: Population stratification in genetic studies Hardy-Weinberg equilibrium testing Optimal experimental design for case-control studies Handling missing data in genetic linkage analysis Development of robust statistical tests for complex survey data Publications Overview: Dr. Li's recent work emphasizes methodological advancements in: Bayesian approaches to population genetics Meta-analytic frameworks for combining study results Statistical solutions for multi-stage clinical trials Algorithmic improvements for genome-wide association analyses Professional Contributions: His articles consistently address practical challenges in biomedical research, bridging theoretical statistics with real-world genetic and clinical applications.
Amelia Acker is an Associate Professor and Graduate Advisor/Director of Masters Studies at the University of Texas at Austin's School of Information. Her work focuses on the emergence of new information objects in wireless networks, digital preservation, and cultural memory. She holds a PhD with award-winning research on SMS standardization and mobile communication infrastructure. Prior to UT Austin, she served as an Assistant Professor at the University of Pittsburgh's iSchool and worked as an archivist/librarian in Southern California. Her research is funded by NSF and IMLS grants, and has been published in journals like JASIST and Archival Science. She teaches courses on metadata, information studies, and cultural heritage informatics. Her current projects address digital traces in mobile computing and data justice issues. She previously worked with artist John Baldessari as an arts cataloger and has extensive experience in library preservation practices. Key contributions include analyses of Venmo social payments, Palantir's surveillance systems, and API-driven social media archives. She emphasizes interdisciplinary approaches to sociotechnical systems and data ethics in emerging technologies.
Toby Davies is an Associate Professor in Criminal Justice Data Analytics at the University of Leeds, School of Law. His work focuses on quantitative criminology, spatial analysis, and computational methods to inform crime prevention. He holds a Mathematics degree from the University of Oxford (2008) and a PhD from University College London (UCL), followed by postdoctoral research on the EPSRC-funded Crime, Policing and Citizenship project. Before joining Leeds in 2023, he was at UCL’s Department of Security & Crime Science. His research spans interdisciplinary topics including urban form and crime, crime modeling, social networks, and cybercrime. He has collaborated with police agencies (West Yorkshire, Thames Valley, West Midlands Police) and governmental bodies (UK Home Office, London Mayor’s Office). A strong advocate for Open Science, he co-founded JDI Open to promote open practices in crime science. His recent work emphasizes policy interventions like phasing out pointed kitchen knives to reduce knife crime, leveraging data-driven approaches. He publishes widely in criminology, physics, network science, and general science journals, and has guest-edited special issues. His applied research aims to bridge theory and practice, developing tools deployed operationally in policing. Current interests include financial crime dynamics, social contagion of crime, and street network configurations’ impact on crime patterns.
Yasser Arafat Payne is a Professor of Sociology & Criminal Justice at the University of Delaware's College of Arts & Sciences, with a joint appointment as an associate professor in the Department of Africana Studies. His academic work focuses on street ethnographic research examining policing and reentry, economic well-being and educational inequality, and gun violence with street-identified Black Americans through the innovative Street Participatory Action Research (Street PAR) framework. Payne earned his Ph.D. and M.Phil. from the City University of New York Graduate Center, an M.A. from Seton Hall University, and a B.A. from Wagner College. His educational background has informed his distinctive approach to studying marginalized communities through the lens of those who live within them. Payne's research program challenges dominant arguments in the literature by asserting that all of the streets of Black and Brown America are resilient. His work focuses on racial identity, street identity, school violence, physical violence, Gangster Rap music and culture, and the methodology of street participatory action research. He has developed Street PAR as a framework that includes three essential features: research orientation, intervention for Street PAR members, and a vehicle for action and activism in local communities. His recent publications reveal a consistent focus on violence exposure, police-community relations, reentry challenges, and resilience among street-identified Black populations. Payne's work spans multiple disciplines including criminology, sociology, public health, and urban studies, with particular attention to the intersection of structural inequality and individual experience. Payne actively engages with media on issues of policing and structural violence, as evidenced by his July 2024 commentary on stop-and-frisk policies for NBC News and his August 2023 presentation to the Wilmington Reparations Task Force. He co-authored the book 'Murder Town, USA: Homicide, Structural Violence and Activism in Wilmington' (Rutgers University Press, 2023), which has become a key resource for understanding gun violence in urban communities. As an educator, Payne teaches specialized courses on street ethnography, Gangster Rap Music and Culture, Racial Stratification, and core courses on Race, Class & Gender and Race, Gender & Poverty. His teaching philosophy centers on bringing the lived experiences of street-identified populations into academic discourse through innovative pedagogical approaches. Payne leads a research lab focused on Street PAR methodology, training community members to document experiences of street-identified Black populations through surveys, interviews, and dual interviews. His approach emphasizes community ownership of research and the transformative potential of participatory methodologies in addressing structural violence.
Prof. Peter van der Heijden is a Professor of Statistics for the Social and Behavioural Sciences at Utrecht University's Department of Methodology and Statistics. He also holds a professorship in Social Statistics at the University of Southampton. His roles include chairing the Ethical Review Board and the Committee for Policy on Integrity at Utrecht's Faculty of Social and Behavioural Sciences. He chairs the Advisory Council on Methodology and Quality of Statistics Netherlands and serves on the Executive Board of the European Statistical Advisory Committee (ESAC). Since 2017, he has led Utrecht's Applied Data Science focus area, focusing on human-centered AI and data-driven solutions. His research emphasizes population size estimation, fraud detection, and categorical data analysis, with applications for Dutch ministries and international bodies like the UN. He has pioneered methods for estimating human trafficking victims and optimizing healthcare treatments using multilevel models and neural networks. Key projects include the AI for Health initiative with Utrecht Medical Center and Wageningen University. His work bridges statistical rigor with societal impact, addressing challenges in criminal justice, public health, and policy-making through innovative methodologies. Universities: Utrecht University (Primary), University of Southampton Key Committees: European Statistical Advisory Committee, UN Human Trafficking Monitoring Research Themes: Multiple Systems Estimation, Data Science for Social Issues Research interests span statistical methods for complex societal problems, including: Register linkage and fraud detection Machine learning applications in healthcare Human trafficking prevalence estimation His publications (2019-2023) highlight advancements in multilevel modeling, randomized response techniques, and AI-driven clinical data classification. He has advised on policy frameworks for official statistics and contributed to global initiatives like the UN Sustainable Development Goals (Target 16.2). Grants and collaborations include projects with Dutch ministries, the EU, and international organizations. Current initiatives involve optimizing Hepatitis C treatment networks and improving criminal recidivism prediction models. His leadership in interdisciplinary teams ensures methodological innovation addresses real-world challenges.
Eric Wickel, Ph.D., is a Professor and John C. Oxley Endowed Chair of Kinesiology & Rehabilitative Sciences at The University of Tulsa's Oxley College of Health and Natural Sciences. His research focuses on physical behavior assessment using wearable devices and self-report tools, with applications to cardiovascular disease risk factors, sedentary behavior, and youth health outcomes. Education: Ph.D., Iowa State University (2006) M.S., University of Wyoming B.S., University of Wyoming His work examines associations between physical activity, sedentary behavior, and sleep using advanced methods like thigh-worn accelerometers, funded by TSET and Oklahoma Center for the Advancement of Science and Technology. Current research explores emergent assessment tools for daily behavior analysis in health promotion. Notable trends in his publications (2019–2009) include: Physical behavior surveillance in early childhood interventions Methodological advancements in wearable technology validation Developmental patterns of sedentary behavior and activity compliance Maturity-related differences in physical activity among youth After-school period behavioral dynamics Cardiovascular risk factor analysis through longitudinal studies Scientific recognition includes: American Heart Association Institutional Research Advancement Award (2025) Oxley College Dean’s Excellent Faculty Award (2025) TSET Seed Grant (2023) Oklahoma Center for the Advancement of Science and Technology Award (2023) Multiple Oxley College Outstanding Research/Teaching Awards (2020, 2016, 2009) Wickel contributes to community health through Tulsa County Community Health Improvement Plan (CHIP) and serves as faculty affiliate of the TSET Health Promotion Research Center.
Dr. Francis L. Huang is a Professor in the Department of Educational, School, and Counseling Psychology at the University of Missouri-Columbia 's College of Education and Human Development. An applied quantitative methodologist , he teaches courses in program evaluation, multilevel modeling, and data management while researching school climate, bullying prevention, and large-scale educational data analysis. PhD in Research, Statistics, and Evaluation (University of Virginia) MA in Instructional Technology and Media (Teachers College, Columbia University) BS in Legal Management (Ateneo de Manila University) His methodological expertise spans clustered data analysis , plausible values modeling , and robust standard error estimation . He develops tools like the MLMusingR package for multilevel modeling in education research. Recent work focuses on 2025 grant-funded studies about data weighting in international assessments and 2024 open-access replication frameworks for nonexperimental datasets. He advocates for rigorous causal inference and equitable discipline policy analysis through projects like the National Center for Rural School Mental Health (funded by Institute of Education Sciences). Dr. Huang contributes to Missouri Prevention Science Institute as Methodology Co-Director and collaborates with interdisciplinary teams on school violence prevention and behavioral threat assessment systems. His 2023-2025 publications demonstrate technical innovations in three-level cluster-robust errors and missing data handling for large-scale assessments.
Susan Murray , ScD, is a Professor in the Department of Biostatistics at the University of Michigan School of Public Health. She has made significant contributions to survival analysis methodology, particularly in lung allocation systems and quality-of-life-adjusted survival models. Education: ScD in Biostatistics (Harvard, 1994), MS in Biostatistics (Harvard, 1992), BA in Statistics and English (Rice, 1990) Roles: Biostatistical Editor for American Journal of Respiratory and Critical Care Medicine , member of Cystic Fibrosis Foundation's Data and Safety Monitoring Board Her research focuses on nonparametric survival analysis with informative censoring, group sequential methods for censored data, and quality-of-life-adjusted survival models. She pioneered Lung Allocation Score development and dependent censoring methodology. Recent collaborative work with pulmonary researchers has advanced CT parametric response mapping for COPD progression, machine learning applications in proteomics, and spatially localized lung disease analysis. Her publications show strong emphasis on survival modeling , censored data handling, and transplantation outcomes . Scientific Awards: University of Michigan School of Public Health Excellence in Teaching Award (2003) On Job/On Campus Master's Program Teacher of the Year (2001) Professor Murray maintains active collaborations with University of Michigan Medical School pulmonary researchers and national institutions. She has served on editorial boards for Biometrics and Lifetime Data Analysis , and currently contributes to the American Journal of Respiratory and Critical Care Medicine as Biostatistical Editor.
Fredrik Johansson is an Associate Professor in the Department of Data Science and AI at Chalmers University of Technology. His research focuses on developing machine learning methods for healthcare applications, causal inference, and handling imperfect data. He leads multiple funded projects including WASP AI/MLX and research on causal machine learning for healthcare applications. Johansson's core research interests include: Machine learning for clinical decision support and healthcare analytics Causal inference methods for observational data Handling missing values and data quality issues Interpretable and robust ML models Domain adaptation and transfer learning Reinforcement learning for treatment policies His recent publications demonstrate strong focus on clinical ML applications (dermatology, rheumatology, Alzheimer's) and methodological work on causal inference. Frequent themes include handling missing data, model interpretability, and healthcare policy optimization. Collaborative work spans multiple medical domains using registry data, proteomics, and medical imaging. He leads significant research projects including: Kausalitet och sidoinformation för effektiv maskininlärning (VR-funded) Maskininlärning för kausal inferens från observationsdata (Wallenberg) Förutsättningar för inlärning av överförbara koncept (Wallenberg) Fattigdomsfällor i Afrika (Formas-funded)
