Paul Gustafson is a Professor and Department Head in the Department of Statistics at the University of British Columbia (UBC). His research focuses on Bayesian analysis, partial identification, causal inference, measurement error, and evidence synthesis, with applications in epidemiology, public health, and biostatistics. He has authored two books: Bayesian Inference in Partially Identified Models (2015) and Measurement Error and Misclassification in Statistics and Epidemiology (2003). As an inaugural Statistics Editor of Epidemiology , he also serves as an Associate Editor of Biometrics . Gustafson has led the STRATOS initiative to strengthen analytic thinking in observational studies, particularly addressing measurement error and misclassification. His work emphasizes methodological advancements in handling complex data challenges, with contributions to understanding bias in epidemic curves and improving statistical practices in observational research.
Professor Jang Yoon is a faculty member in the Department of Computer Engineering at Sejong University, South Korea. He currently holds the position of Daeyang Distinguished Professor and leads the Data Visualization Lab. His academic journey includes postdoctoral research at the Swiss National Supercomputing Center (2007-2009), ETH Zurich (2009-2011), and Purdue University (2011-2012). His educational background includes a Bachelor's degree from Seoul National University in Electrical Engineering (2000), and Master's and Doctoral degrees from Purdue University in Electrical and Computer Engineering (2002 and 2007). His academic progression at Sejong University shows his appointment as Assistant Professor (2012-2016), Associate Professor (2016-2022), and Professor (2022-present). Professor Jang's research spans multiple domains within data science and visualization, with primary focus on data visualization, visual analytics, and their applications in various domains. His work bridges theoretical computer science with practical applications in traffic analysis, healthcare, and smart city infrastructure. He has developed innovative techniques for spatiotemporal data visualization, volume rendering, and causal analysis in complex datasets. His recent publications (2023-2025) demonstrate a strong focus on integrating deep learning with visualization techniques, particularly in traffic analysis, volume rendering, and large language model interpretability. His work shows a clear trajectory toward combining causal inference with visual analytics, applying these methods to urban traffic systems, structural health monitoring, and public relations analysis. Professor Jang has served in numerous leadership roles in major visualization conferences including IEEE VIS, IEEE PacificVis (as General Chair in 2023), EuroVis, and HCI Korea conferences. His service contributions include program committee memberships and chair positions across multiple prestigious conferences in the visualization field. His laboratory work focuses on practical applications of visualization techniques with numerous patents registered in Korea. His research has resulted in multiple practical systems for traffic analysis, VR sickness detection, data quality improvement, and eye-tracking applications. The lab maintains strong industry connections through applied research projects addressing real-world problems.
Lingzhou Xue is a Professor of Statistics at The Pennsylvania State University, affiliated with the Eberly College of Science. He holds dual roles as a faculty member and the Associate Director of the National Institute of Statistical Sciences (NISS). His research focuses on high-dimensional statistics, nonparametric methods, statistical learning, and optimization, with applications in biomedical, environmental, and social sciences. He leads the SLDM (Statistical Learning and Data Mining) Lab and MDS (Microbiome Data Science) Lab. Education: B.Sc. in Statistics from Peking University (2008), Ph.D. in Statistics from the University of Minnesota (2012), postdoctoral training at Princeton University (2012–2013). Professional roles include Associate Editorships at the Journal of the American Statistical Association, Annals of Applied Statistics, and ACM Transactions on Probabilistic Machine Learning. Research Interests: Federated learning, causal inference, graphical models, reinforcement learning, optimal transport, and large-scale optimization. Recent work emphasizes theoretical guarantees for sparse PCA and federated Q-learning algorithms. Awards: IMS Fellow (2024), ASA Fellow (2023), COPSS Emerging Leader Award (2021), and Bernoulli Society New Researcher Award (2019). He has mentored 17 Ph.D. students and 3 postdocs, with four students securing tenure-track faculty positions. Service: Organized multiple NISS writing workshops, co-chaired the Ingram Olkin Statistics Serving Society Forum on Gun Violence, and contributed to the ASA whitepaper 'Discovery with Data' (2014).
Felix Elwert is the Vilas Distinguished Achievement Professor of Sociology and Romnes Professor of Sociology at the University of Wisconsin-Madison. He holds an affiliate professorship in Population Health Sciences. His expertise spans causal inference methods, social stratification, demography, and health inequality. Elwert earned his Ph.D. in Sociology and M.A. in Statistics from Harvard University (2006-2007). He has held roles including Karl W. Deutsch Professor and Acting Director at the WZB Berlin Social Center (2014-2016). Research focuses on quasi-experimental approaches, peer effects, and social determinants of health. Notable awards include the 2013 ASA Causality in Statistics Education Award and 2018 ASA Leo Goodman Award. He teaches advanced courses like Causal Inference and Graphical Causal Models, and leads the Sociological Methods & Research journal as Editor-in-Chief. Key affiliations include the Center for Demography and Ecology, Center for Demography of Health and Aging, and Institute for Diversity Science. His work bridges sociology and biostatistics, with experimental studies on education, health disparities, and social networks. Education : Harvard University (Ph.D. Sociology 2007; M.A. Statistics 2006) Grants/Research : Funded studies on ADHD medication effects, lupus disparities, and neighborhood impacts Labs : Involvement with the Inequality Lab and interdisciplinary teams globally
Kevin He is an Associate Professor in the Department of Biostatistics at the University of Michigan School of Public Health. He serves as Associate Director of the Kidney Epidemiology and Cost Center (KECC) and leads statistical innovations in survival analysis, healthcare provider profiling, and data integration for biomedical applications. PhD in Biostatistics (University of Michigan, 2012) MS in Biostatistics (University of Michigan, 2008) BS in Statistics (Queen’s University, 2006) MS in Epidemiology (Queen’s University, 2004) BM in Clinical Medicine (Dalian Medical University, 2002) His research focuses on survival analysis for large-scale datasets, machine learning for healthcare provider profiling, and statistical genetics in organ transplantation and chronic disease. He develops data integration frameworks for polygenic risk scores and statistical optimization algorithms for time-varying effects in national registries. The 15 most recent articles highlight his work in survival modeling with time-varying coefficients, federated learning for privacy-preserving data integration, and genomic applications in inflammatory diseases. His methodological contributions span Kronecker product algorithms , proximal optimization , and penalized partial likelihood . He mentors a team including software developers and graduate researchers working on deep learning , frailty models , and distributed computing . His lab maintains the surtvep R package for scalable survival analysis.
Erik Sverdrup is a Senior Lecturer in the Department of Econometrics & Business Statistics at Monash University. His research focuses on causal inference, statistical computing, and data science, with applications spanning mental health analytics, econometrics, and machine learning. Recent work has emphasized treatment effect heterogeneity, survival analysis, and algorithm development for complex healthcare and financial datasets. Research Interests: Causal inference, treatment effect modeling, statistical computing, mental health analytics, financial risk analysis. Key Collaborations: Involved in multi-institutional studies with applications to psychiatric research, veterans' health, and global disability assessment. Email Contact: Erik.Sverdrup@monash.edu
Rui Miao serves as an Assistant Professor in the Department of Mathematical Sciences within the School of Natural Sciences and Mathematics at The University of Texas at Dallas. Previously, he worked as a Mathematical Statistician at the Office of Biostatistics Research at NIH/NHLBI and completed postdoctoral training under supervision of Dr. Annie Qu and Dr. Babak Shahbaba. His academic foundation includes a PhD in Statistics from The George Washington University under Dr. Xiaoke Zhang's mentorship. Education: PhD in Statistics, The George Washington University Dr. Miao's research program bridges advanced statistical methodology with critical healthcare applications. His work focuses on Reinforcement Learning for personalized treatment policies, Causal Inference methods addressing unmeasured confounding, Health AI applications, Functional Data Analysis techniques, and computational approaches to Few-shot Learning . His methodological innovations particularly address challenges in heterogeneous medical data and complex decision frameworks. Analyzing his publication trajectory reveals a strong emphasis on developing statistical frameworks for personalized medicine, with increasing interdisciplinary collaboration in cardiology, immunology, and neuroscience. His work spans theoretical statistics in journals like Annals of Statistics and Journal of the American Statistical Association to applied medical research in Science Advances and Journal of the American College of Cardiology . Scientific Recognition: 2021 ICSA Student Paper Award for work on wavelet-based independence testing Dr. Miao actively contributes to the academic community through invited talks at institutions including National Cancer Institute, Duke University, and NIH, presenting on reinforcement learning under heterogeneity and functional data analysis methods. His teaching portfolio at UT Dallas includes STAT 5304 Introduction to Human Health Research, building on previous teaching experience at The George Washington University covering statistical theory and applied courses.
Stephanie Denison is an Associate Professor and Associate Chair of Undergraduate Affairs at the University of Waterloo. Her research focuses on developmental psychology, particularly how children develop reasoning abilities related to probability, counterfactual thinking, and social cognition. Her work explores topics such as causal attribution, sunk cost fallacy, social network inference, and decision-making strategies in children. She has published extensively on probabilistic reasoning in infants and preschoolers, demonstrating how young children use statistical information to form beliefs about the world. Her recent studies investigate children's understanding of counterfactual scenarios (e.g., 'What if I had done something differently?'), their ability to infer emotions based on probabilistic outcomes, and their reasoning about social relationships through mutual connections. Denison also examines how children integrate physical constraints and emotional factors into their decision-making processes. Her contributions span cognitive development, philosophy of mind, and educational psychology, with a strong emphasis on bridging theoretical models with empirical findings from developmental research.
Jeremy Teigen is a Professor of Political Science in the School of Humanities and Global Studies at Ramapo College of New Jersey, where he has been a faculty member since 2005. His academic work centers on American politics, with a focus on elections, voter behavior, political participation, and the political engagement of military veterans. His research interests include: Elections and Electoral Behavior Political Participation Role of Military Veterans in Politics Voter Behavior and Political Psychology Political Methodology and Experimental Design Political Communication and Campaigns Teigen’s scholarly publications span top political science journals and an influential book. His recent work analyzes the impact of military service on political behavior, demographic shifts in voting patterns, and the role of identity in presidential elections. He frequently employs experimental and quantitative methods in his research. Notable publications include his 2018 book Why Veterans Run: Military Service in American Presidential Elections, 1789–2016 , and articles in Electoral Studies , Political Research Quarterly , and Armed Forces & Society . His research demonstrates a consistent focus on the intersection of military experience and democratic participation. He teaches courses in American government, political methodology, campaigns and elections, and military-political dynamics. His office is located in B-211, and he is accessible during regular office hours. He holds a Ph.D. from the University of Texas and a B.A. from the University of Wisconsin-Madison.
Sjoukje van Deuren is an Assistant Professor in Criminology at the Faculty of Law, Vrije Universiteit Amsterdam (VU), and is affiliated with the Netherlands Institute for the Study of Crime and Law Enforcement (NSCR). Her academic work bridges empirical research and policy relevance in the domains of organized crime, domestic violence, and juvenile delinquency. Assistant Professor, Faculty of Law, Vrije Universiteit Amsterdam Researcher, Netherlands Institute for the Study of Crime and Law Enforcement (NSCR) Member, A-LAB and Empirical and Normative Studies Project Researcher, EPIC: Explaining, Preventing, and Intervening in Organized Crime Involvement (2024–2028) Her research focuses on the structure and behavior of outlaw motorcycle gangs, particularly the Hells Angels, examining entry mechanisms, co-offending patterns, social proximity, and judicial responses. She also investigates the dynamics of domestic violence, especially during the COVID-19 pandemic, and the interplay between criminal behavior and personal relationships. Her methodological approach is predominantly empirical, using large-scale datasets and qualitative case studies. The most recent articles show a strong trend in analyzing organized crime through both quantitative and qualitative lenses, with a focus on Dutch outlaw motorcycle gangs. Her work frequently explores how formal club hierarchies relate to criminal behavior, the geographical and social clustering of co-offenders, and the societal impact of crime policies. A secondary but consistent theme is domestic violence, particularly how reporting and prevalence shifted during public health crises. She teaches the course Transnational Organized Crime and has previously served as a lecturer at VU. She has been involved in multiple research projects, most notably the ongoing EPIC project, which examines pathways into organized crime and intervention strategies. While no formal advisees are listed, her role as a PhD supervisor and project researcher suggests mentoring responsibilities. Her collaborative work spans institutions and disciplines, often co-authored with leading criminologists such as E. Kleemans, A. Blokland, and R. Roks. She has contributed to policy-relevant reports on domestic violence and organized crime, indicating strong engagement with societal impact.
Hans-Peter Y. Qvist is an Associate Professor on a promotion track to full Professor at Aalborg University's Department of Sociology and Social Work, within the Faculty of Social Sciences and Humanities. His research focuses on civic associations, interethnic relations, and the socio-economic impacts of volunteering. He leads projects such as 'Association Participation among Immigrant Children: A Vehicle for Integration' and 'MNcontact: Measuring Intense Migrant-Native Contact.' He is a Sapere Aude research leader (2024–2028), funded by the Independent Research Fund Denmark. His work contributes to UN SDGs related to social integration and labor market inclusion. Qvist teaches courses in quantitative methods and sociology, including 'Advanced Quantitative Method: Causal Inference in Sociology' and coordinates 'Methods in Quantitative Research.' He supervises student projects and serves as an external examiner nationwide. His affiliations include SocMap (Sociological Mapping Research Group) and MIX (Center for Displacement, Migration, and Integration). His research explores how association participation influences labor markets and health, and examines interethnic social bonds. Notable publications include analyses of immigrant integration, volunteering's mental health impacts, and ethnic union formation. Awards include 'Dommer: Årets Ungdomsforening 2016' and the Talent Programme for Young Researchers (2016–2018).
Dr. Kelsey West serves as an Assistant Professor in the Department of Psychology at the University of Alabama, where she directs the Bama Baby Lab. Her research investigates infant learning during the first three years of life, focusing on how skills in one domain (such as motor development) influence learning across multiple areas including language and social communication. She studies neurotypical infants, autistic infants, and infants with language delays using laboratory experiments and naturalistic home observations. Her educational background includes: PhD in Psychology from the University of Pittsburgh Postdoctoral fellowship at New York University Dr. West's research explores critical questions about developmental interconnectivity: How infants actively construct their learning environments How new skills (e.g., walking or pointing) create ripple effects across developmental domains How delays in one area impact overall developmental trajectories Patterns in infant-caregiver interactions and natural behavior Her work emphasizes autism spectrum disorder, language acquisition, and motor development through the lens of developmental cascades. Methodologically, she integrates real-time behavioral coding with natural language analysis to understand caregiver-infant dynamics. Analysis of Dr. West's publication record reveals consistent focus on motor-communication relationships, particularly during walking transition. Her research frequently examines infant siblings of children with autism as a population at heightened developmental risk, employing longitudinal designs to map cascading effects across domains. Naturalistic observation methods feature prominently in her approach to capturing authentic developmental processes. Dr. West is currently accepting graduate students into the Bama Baby Lab. While specific grant details aren't provided, her active research program investigating infant development across diverse populations indicates ongoing funding support. The Bama Baby Lab, located in Gordon Palmer Hall, Room 162C, employs innovative 'day-in-the-life' video recording techniques to document infants' natural learning environments. The lab collaborates with the Center for Innovative Research in Autism and focuses on translating developmental science into practical early intervention strategies for infants showing developmental variations.
Ulrik Beierholm is an Associate Professor in the Department of Psychology at Durham University, with affiliations in the Biophysical Sciences Institute and the Durham Research Methods Centre. He previously held positions at the University of Birmingham’s Centre for Computational Neuroscience and Cognitive Robotics. His research lies at the intersection of psychology, neuroscience, and machine learning. His research focuses on how the human brain processes uncertainty in perception, decision-making, and learning. He employs Bayesian inference and reinforcement learning models to understand human behavior, validated through psychophysics , fMRI , and pharmacological methods. Key areas include multisensory integration , causal inference , perceptual clustering , and behavioral vigor . His work often explores developmental and aging effects on perception. His recent publications reveal a strong trend in modeling multisensory perception under uncertainty, with increasing focus on open-source tools (e.g., BCI Toolbox) and educational outreach (e.g., Neuromatch Academy). Themes include reliability-weighted cue integration, dopamine’s role in motivation, and developmental changes in perceptual strategies. Facebook Faculty Award - Virtual Reality (2016) Leverhulme Trust Grant (£250k) for 'Learning to perceive and act under uncertainty' (2017) Tubingen-Durham Joint Seedcorn Fund for 'The effects of mood on effort allocation during uncertainty' (2019) Dr. Beierholm has supervised PhD students and research assistants such as Nathanael Larigaldie, Denise Foresteire, and Laura Bird. He has secured competitive grants from the Leverhulme Trust and joint university funds, supporting projects on uncertainty, effort allocation, and multisensory perception. His collaborative network spans institutions in the UK, Germany, and the US. He co-organizes research workshops including the Probabilistic Brain Workshop and Computational Models of Social Interaction , and is involved in the Biophysical Sciences Institute Executive Board . His lab conducts behavioral experiments on multisensory integration, equipped with a soundproof room, projector, and 17-speaker array.
Valentina Tocchioni serves as Associate Professor in Social Statistics at the University of Florence's Department of Statistics, Computer Science, and Applications 'G. Parenti' (DiSIA). Her academic career spans teaching roles across Bachelor's, Master's, and PhD programs in Statistics, Humanities, and Sustainable Tourism. PhD in Applied Statistics (University of Florence, 2016) Post-doctoral researcher at University of Florence (2016-2019) Visiting fellow at University of Southampton (2017-2019) Italian National Scientific Qualification as Associate Professor (2022-2034) Her research centers on socio-demographic dynamics with emphasis on family formation , labor market uncertainty , and educational trajectories . Key methodologies include survival models, causal inference, and sequence analysis. Current investigations examine childlessness patterns, PhD career pathways, and housing dynamics' impact on fertility decisions across European contexts. Recent publications reveal strong focus on Italian demographic trends through analyses of school-to-university transitions, vaccination disparities in non-intact families, and socio-economic determinants in ART treatment. Her work frequently employs longitudinal datasets and comparative frameworks spanning Italy, UK, and Poland. Demographic Research Editor’s Choice Award 2016 Principal Investigator for PRIN2022 KinHealth project on kinship networks Co-PI for PEER-UP project on educational inequalities She actively mentors PhD candidates in event-history analysis and social demography while leading multiple national research initiatives funded by MUR and EU programs. Her teaching integrates advanced statistical methods with real-world demographic applications across diverse student cohorts. Current projects include examining ethnic bullying in schools and aging dynamics through the Age-IT consortium.
Amiremad Ghassami is an Assistant Professor in the Department of Mathematics and Statistics at Boston University. His research focuses on causal inference and discovery, statistical learning theory, and semiparametric statistics. He develops methodologies to address challenges in causal effect estimation, policy optimization, and causal graph identification in complex real-world data scenarios such as unobserved confounders and measurement errors. His work integrates modern statistical techniques, machine learning, and information theory. Dr. Ghassami holds a PhD in Data Science and Communications from the University of Illinois at Urbana-Champaign (2020), advised by Prof. Negar Kiyavash, and completed postdoctoral research at Johns Hopkins University under Professors Ilya Shpitser and Eric Tchetgen Tchetgen. He co-organizes the Statistics and Probability Seminars at Boston University. Research Interests: Causal Inference and Discovery Statistical Learning Theory Semiparametric Statistics Probabilistic Graphical Models His recent publications emphasize causal data fusion, mediation analysis, and brain connectivity studies using fMRI. He addresses methodological challenges in handling unobserved variables and nonignorable missing data through innovative data fusion strategies and debiased estimation techniques. Dr. Ghassami has contributed to causal structure learning algorithms for cyclic and acyclic models, with applications to neuroscience and policy evaluation. His work bridges theoretical foundations and practical data-driven solutions for modern causal analysis problems.
