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Scott SissonView profile

Professor

• Computational Statistics
• Bayesian Statistics
• Big Data
• +4 more

Professor Scott Sisson is Director of the UNSW Data Science Hub (uDASH) and Professor of Statistics and Data Science at the University of New South Wales, School of Mathematics and Statistics. His research focuses on computational statistics, particularly solving 'intractable' statistical problems through Bayesian methods, big data techniques, simulation algorithms, and extreme value theory with environmental applications. Education includes a PhD in Statistics from Bristol University (2002), MSc in Environmental Statistics from Lancaster University (1997), and BSc in Mathematics and Statistics from Lancaster University (1996). Research interests span: Bayesian statistics and uncertainty quantification Big data analytics and scalable algorithms Machine learning integration with statistical methods Extreme value modeling for climate/environment Computational techniques for intractable problems Recent publications (2022-2025) demonstrate strong emphasis on Bayesian computation, spatiotemporal modeling, and interdisciplinary applications in materials science, oncology, quantum computing, transportation policy, and ecology. Methodological innovations include likelihood-free inference, modular Bayesian analyses, and symbolic data modeling. Awards and honors: 2020 Service Award (Statistical Society of Australia) 2017 ARC Future Fellowship 2015 G. N. Alexander Medal (Engineers Australia) 2011 Moran Medal (Australian Academy of Science) 2010 J.G. Russell Award (Australian Academy of Science) 2010 Queen Elizabeth II Research Fellowship As Director of uDASH, he leads data science initiatives across UNSW. He maintains sustained ARC funding and supervises students in computational statistics, Bayesian methods, extreme value theory, and machine learning. Professional service includes editorial roles for Statistics and Computing and past presidency of Statistical Society of Australia.

Sara HäggView profile

Senior Lecturer

• Epidemiology
• Gerontology
• Medical Genetics and Genomics
• +3 more

Sara Hägg is a Senior Lecturer at the Karolinska Institutet , affiliated with the Department of Medical Epidemiology and Biostatistics . She is also a Docent in molecular epidemiology. PhD in Computational Biology (Linköping University, 2009) MSc in Molecular Biology (Stockholm University, 2003) BSc in Computer Science (Stockholm University, 2003) Her research focuses on human biological aging , including measurement of aging markers (telomere length, epigenetic clocks, frailty index), causal pathway analysis, and identification of geroprotectors for age-related diseases. She utilizes longitudinal twin studies (SATSA, GENDER, HARMONY), UK Biobank, and Swedish cohorts with methods like Mendelian randomization and genome-wide analyses . Recent articles demonstrate trends in epidemiological aging research , with emphasis on cardiovascular aging , neurological disease interactions , metabolic profiling , and epigenetic clocks . Her work often involves multivariable modeling and cross-cohort validation . Leadership roles include Director of LifeGene Core Facility (2024-) and Founding Board Member of the Nordic Aging Society (2023-). She serves on expert groups for the Swedish Twin Registry and Strategic Research Area in Epidemiology and Biostatistics .

Xihong LinView profile

Professor

• Statistical Machine Learning and AI
• Statistical Genetics and Genomics
• Causal Inference and Mediation Analysis
• +4 more

Xihong Lin is a Professor of Statistics at Harvard University and a Professor of Biostatistics at the Harvard T.H. Chan School of Public Health. She is a distinguished academic, holding membership in both the National Academy of Sciences and the National Academy of Medicine. Her research focuses on scalable statistical inference for big data, statistical machine learning, causal inference, and integrative data analysis, with applications in genomics, public health, and precision medicine. Lin’s work addresses challenges in analyzing large-scale genomic and multi-ancestry data, including methods for rare variant association testing, ancestry-adjusted sample analysis, and scalable computing frameworks. Her contributions span biobank studies (e.g., UK Biobank, TOPMed) and clinical applications in lung cancer, cardiovascular health, and smoking cessation. Her scientific awards reflect her leadership in statistical genetics and public health. Key research trends include leveraging single-cell sequencing for functional genomics, developing ensemble machine learning methods for health subtyping, and enhancing polygenic risk prediction across diverse populations. Lin’s methodologies prioritize interpretability and scalability, enabling impactful analyses of complex observational and genomic datasets. Awards: Member, National Academy of Sciences; Member, National Academy of Medicine Her grants and advising efforts focus on interdisciplinary collaborations, bridging statistics, AI, and domain sciences. Lin leads initiatives to improve genomic data management and ethical use of federated data (e.g., FADI framework). She is affiliated with labs advancing statistical genetics and cloud-based workflows (e.g., STAAR workflow).

• Machine Learning
• Health Informatics
• Public Health
• +3 more

Ke Wu is a Professor in the Department of Computer Science and Engineering at the University of Michigan. Their research focuses on the intersection of machine learning, biostatistics, and healthcare technology, with an emphasis on mobile health interventions, causal inference, and Bayesian methods. They lead a small, hands-on research group mentoring PhD students and postdocs. Key interests include developing predictive models for health outcomes, improving treatment effect estimation, and leveraging mobile technology for caregiver support. Their work has addressed critical challenges in clinical decision-making, public health surveillance, and healthcare innovation. Research projects span synthetic data generation for electronic health records, mHealth app development for care partners of traumatic brain injury patients, and algorithmic fairness in reinforcement learning. Ke Wu emphasizes interdisciplinary collaboration and has contributed to global health studies, including analyses of pneumonia etiology in low-resource settings and the PERCH study. Their group's methodologies often integrate wearable sensor data and machine learning to address real-world health challenges. Advising priorities include fostering student independence while maintaining close mentorship, with expectations for consistent research productivity and professional development. Students are encouraged to pursue teaching roles (e.g., GSI positions) and internships aligned with career goals. Funding support for conference participation is available through institutional and external grants. Ke Wu's contributions extend to statistical methodology, including Bayesian latent class models and dynamic risk prediction frameworks. They actively engage in translational research, bridging computational methods with clinical and public health applications, and prioritize open-source software development to advance reproducible research practices.

• Computational Statistics
• Causal Inference
• Machine Learning
• +3 more

Niels Richard Hansen is a Professor at the Department of Mathematical Sciences , University of Copenhagen, leading research at the intersection of Artificial Intelligence and Statistics . He co-founded the Copenhagen Causality Lab and focuses on automating causal explanation discovery from data using Bayesian networks, stochastic processes, predictive models, and machine learning. His work emphasizes creating interpretable and robust AI systems capable of generalizing across domains. His research has produced over 56 publications spanning causal inference , graphical modeling , stochastic processes , and machine learning . Recent work includes: Predictive and causal learning (2018 keynote) High-dimensional regression solutions (2016 lecture) Interdisciplinary applications in actuarial science , environmental statistics , and neuroscience He actively contributes to scientific communication through media appearances and public explanations of statistical concepts, including analyses of: Gaussian correlation inequality proofs Daylight saving time and blood clots Mathematical approaches to lotteries Climate change vs lunar effects

Olatunji JohnsonView profile

Lecturer

• Spatial Statistics
• Epidemiology
• Public Health
• +5 more

Dr Olatunji Johnson is a Lecturer in Statistics at The University of Manchester's Department of Mathematics. His research focuses on spatial and spatio-temporal statistics applied to global public health challenges, including tropical diseases like malaria and neglected tropical diseases (NTDs). He holds a PhD in Statistics and Epidemiology and has developed influential geostatistical methods and R packages (SDALGCP and MBGapp) for disease mapping and surveillance. Education: PhD in Statistics and Epidemiology (supervised by Prof. Peter Diggle). Research interests include model-based geostatistics, real-time health surveillance, and hybrid machine learning approaches for spatial data analysis. He is actively seeking PhD students interested in spatial statistics applications. Key Collaborations: Worked on projects in Kenya, Cameroon, Uganda, and Ethiopia, focusing on helminth control, air pollution impacts, and disease burden analyses. Part of the Statistical Advisory Unit and contributes to the UN Sustainable Development Goals through his work on health equity and disease elimination. Notable Projects: Developed methodologies for efficient survey design in NTD programs, air quality studies in African cities, and spatiotemporal modelling of pandemic risks. His work bridges statistical innovation with actionable public health policy.

• Wine Marketing
• Customer Experiences
• Value Creation
• +7 more

Barry Babin is the Phil B. Hardin Professor of Marketing and Chair of the Department of Marketing, Analytics and Professional Sales at the University of Mississippi (Ole Miss) School of Business Administration. He also serves as Executive Director of the Academy of Marketing Science®. Education: B.S. in Industrial Engineering, Louisiana State University (1982) M.B.A. in Business, University of Central Florida (1987) Ph.D. in Business Administration, Louisiana State University (1991) Barry's research focuses on customer experience design , wine marketing , value creation , and marketing analytics . He has published over 200 works, including books like Multivariate Data Analysis and CB: A Consumer Value Framework , with an h-index of 88. His meta-analyses and sensory marketing studies have significantly influenced service and retail marketing practices. Recent publications highlight trends in augmented reality marketing , robotic shopping assistants , and sustainable packaging . His work spans consumer behavior , salesforce management , and digital innovation . Awards: AMS Harold W. Berkman Distinguished Service Award Barry has directed over 20 dissertations/theses and consults on graduate program structures. He is a sought-after speaker in France and globally on marketing research topics.

Gokhan AydinView profile

Senior Lecturer

• Online Consumer Behavior
• Digital Marketing
• Mobile Marketing
• +5 more

Dr. Gokhan Aydin serves as a Senior Lecturer at the University of Brighton's School of Business and Law, affiliated with the Digital Innovation and Transformation Research Excellence Group and Centre for Regenerative Medicine and Devices. His academic profile reflects expertise in digital consumer behavior and quantitative research methodologies within business contexts. Education: PhD from Istanbul Technical University (2009) with thesis on "Effect of consumer based brand equity on firms' financial performance" His research centers on online consumer behavior , digital marketing (mobile/social media), digitalization (mobile applications/payments), and brand equity . Methodologically, he employs cross-sectional data with multivariate analyses including structural equation modeling (SEM) and machine learning clustering algorithms, frequently applying frameworks like UTAUT and PLS-SEM. Recent publications (2023-2025) reveal strong thematic convergence in sustainability-driven consumer behavior, Gen Z technology adoption, and virtual human interactions. Key trends include green fashion purchasing motivations, mobile health app usage barriers, and anthropomorphism effects in digital environments, demonstrating consistent application of advanced statistical modeling across diverse digital marketing contexts. Scientific Awards: None documented Dr. Aydin has supervised at least one student and maintains active academic service through 29 recorded activities including peer-review for the Journal of Global Fashion Marketing, Health Expectations, Australian Journal of Management, and Journal of Business Analytics. His May 2025 invited talk addressed "Personal Health, Digital Transformation and Healthy aging: Mobile Devices and Applications". He contributes to the Digital Innovation and Transformation Research Excellence Group and Centre for Regenerative Medicine and Devices, focusing on digital transformation impacts across business and healthcare sectors through collaborative research initiatives.

• Econometrics
• Applied econometrics
• Energy economics
• +1 more

Adonis Yatchew is a Professor in the Department of Economics at the University of Toronto , where he has held multiple roles including Vice President for Publications at the International Association for Energy Economics and Editor-in-Chief Emeritus of The Energy Journal (2006-2023). His research bridges econometric methodology with critical energy and environmental policy challenges. Ph.D., Harvard University (1980) M.A., University of Toronto (1975) B.A., University of Toronto (1974) Yatchew's research focuses on Econometrics and Energy Economics , particularly nonparametric regression techniques , energy market regulation , and carbon policy frameworks . His work on scalability in energy industries and empirical analysis of electricity distribution productivity has shaped modern regulatory approaches. Recent publications reveal a strong focus on energy transition dynamics , with technical contributions to nonparametric estimation and carbon pricing mechanisms . Notable works include analyses of Alberta's electricity futures market and Ontario's feed-in-tariff programs. Outstanding Contributions to the Profession , International Association for Energy Economics (2018) Senior Fellow , US Association for Energy Economics (2014) Teaching Award , University of Toronto (1987) As a teacher, Yatchew offers courses on Energy and the Environment and Energy and Regulation , exploring geopolitical impacts on energy markets and optimal government intervention strategies. His econometrics courses cover advanced topics like bootstrap inference and nonparametric methods.

Pui-Wa LeiView profile

Professor

• Multivariate Statistical Analyses
• Structural Equation Modeling
• Item Response Theory
• +3 more

Dr. Pui-Wa Lei is a Professor in the Department of Educational Psychology, Counseling, and Special Education at Pennsylvania State University. Her research focuses on methodological issues in multivariate statistical analyses, structural equation modeling (SEM), and applications of item response theory (IRT). She teaches courses in statistics and measurement theories. Dr. Lei received her Ph.D. in Educational Measurement and Statistics from The University of Iowa (2001), following an M.S. (1997) and dual B.S./B.A. in Applied Psychology and Psychology from Bemidji State University (1994). Her work is supported by the Eunice Kennedy Shriver National Institute of Child Health and Human Development (P2CHD041025) and the Pennsylvania State University's Social Science Research Institute (SSRI). She contributes to the Population Research Institute (PRI), which highlights her engagement in interdisciplinary research initiatives.

Sharon L.R. KardiaView profile

Professor

• Genetic Epidemiology
• Cardiovascular Disease
• Gene-Environment Interactions
• +3 more

Sharon L.R. Kardia is the Millicent W. Higgins Collegiate Professor of Epidemiology and Associate Dean for Education at the University of Michigan School of Public Health (SPH). She holds a PhD in Human Genetics (1991) and MA in Statistics (1990), both from the University of Michigan. Her research focuses on genetic epidemiology of chronic diseases, particularly cardiovascular conditions, emphasizing gene-environment and gene-gene interactions. She directs the Public Health Genetics Program and co-directs the Life Sciences and Society Program and the Michigan Center for Genomics and Public Health. Education: PhD in Human Genetics (University of Michigan, 1991); MA in Statistics (University of Michigan, 1990). Postdoctoral training in systems biology at the Department of Microbiology and Immunology. Research Interests: Genetic epidemiology of cardiovascular diseases, chronic disease prevention, genomic/epigenomic analyses in large cohorts, and translational public health genetics. Notable Projects: Co-leads initiatives integrating genomics with public health practice, including the Michigan Center for Genomics and Public Health. Her work spans population studies, analytical method development, and interdisciplinary program leadership. Labs/Teams: Public Health Genetics Program, Life Sciences and Society Program. Teaches courses on AI in public health (PUBHLTH555) and integrative learning experiences for MPH students (PUBHLTH680).

Bjørge Hermann HansenView profile

Research Fellow

• Physical Activity Epidemiology
• Measurement of Physical Activity
• Accelerometry
• +3 more

Bjørge Hermann Hansen is a postdoctoral researcher at the Department of Sport Medicine, Norwegian School of Sport Sciences (NIH), actively engaged in national physical activity surveillance and the International Children's Accelerometer Database (ICAD). His work bridges epidemiology, statistics, and device-based measurement to investigate activity patterns across diverse populations including children, cancer survivors, and clinical cohorts like Duchenne Muscular Dystrophy patients. Previously referenced in 2020-2023 materials as a University of Agder professor, NIH's December 2023 employee page confirms his current research role at Norway's premier sport science institution. Hansen's research focuses on physical activity epidemiology and measurement validity, specializing in accelerometer data analysis. He investigates intensity distribution, sedentary behavior, and their health associations across lifespan stages. His methodological expertise includes harmonizing multi-cohort data, validating self-report tools against objective measures, and developing protocols for reliable activity monitoring. Current projects address environmental determinants like family car ownership and school policies on screen time. Recent publications reveal a dominant trend: large-scale meta-analyses using pooled international data (ICAD) to establish dose-response relationships between activity patterns and mortality. He also examines secular trends in Norwegian youth activity, methodological challenges in measurement, and the impact of clinical conditions on movement behaviors. Key journals include The Lancet, British Journal of Sports Medicine, and PLOS ONE. Scientific awards: None explicitly mentioned in source materials. Hansen teaches Statistics, Epidemiology, and Scientific Methods at NIH, translating complex concepts through podcasts like Fysioformidlingen. While no specific grants or student advising records are provided, his leadership in national surveillance projects (e.g., Kan3 reports for Folkehelseinstituttet) indicates active grant-funded research. He advocates for evidence-based policies such as school mobile phone bans to increase recess activity. He collaborates with the ICAD consortium and Norwegian Institute of Public Health on population monitoring. His work with the Kan3 project involves nationwide data collection on adult physical activity and fitness. Recent Folkehelseinstituttet reports (2023-2024) demonstrate his role in national health surveillance systems.

Elliot Tucker-DrobView profile

Professor

• Individual Differences
• Behavioral Genetics
• Psychiatric Genetics
• +5 more

Elliot Tucker-Drob is a Professor in the Department of Psychology at the University of Texas at Austin, affiliated with the College of Liberal Arts and the Population Research Center. He holds a Ph.D. from the University of Virginia. His research focuses on individual differences in psychological development, particularly genetic and environmental influences on cognition, personality, and academic achievement. Key areas include behavioral and psychiatric genetics, cognitive aging, and social inequality's impact on development. Teaches Individual Differences (PSY 345) regularly since 2020. Directs the Lifespan Development Lab and leads the Texas Twin Project. His work explores how genes and experiences interact across the lifespan, with recent studies examining polygenic risk scores, epigenetic markers, and structural brain connectivity. He emphasizes translating genomic findings into understanding disparities in education and mental health. Publications highlight themes like genetic contributions to educational attainment, psychiatric disorder correlations, and developmental trajectories. He collaborates internationally, leveraging large datasets for multivariate genomic analyses.

• Multilevel Modeling
• Single-Case Experimental Design (SCED)
• Bayesian Analysis
• +5 more

EUNKYENG BAEK is an Associate Professor in the Department of Educational Psychology at Texas A&M University. She specializes in multilevel modeling (MLM) for analyzing educational and psychological data, with a focus on longitudinal and time-series data such as single-case experimental design (SCED) data. Her research bridges methodological advancements and applied studies, addressing gaps in data analysis techniques for SCED and educational datasets. **Education**: Ph.D., Educational Research, Measurement and Statistics, University of South Florida (2015) M.A., Psychometrics, Korea University (2006) B.A., Psychology, Sungshin Women University (2003) **Research Interests**: Dr. Baek’s work centers on Bayesian analysis, meta-analysis of single-case studies, and the integration of machine learning techniques in educational contexts. She explores statistical methods to handle autocorrelation, heterogeneity, and variance in SCED datasets, contributing to robust methodologies for synthesizing research findings. **Grants & Leadership**: She has led or co-led multiple grants, including projects on developing effect size toolkits for SCED meta-analyses and studying online learning readiness. She serves as an Action Editor for Behavior Research Methods and holds leadership roles in academic organizations like EREL. **Teaching**: She teaches courses in educational measurement, statistical analysis, and SCED data techniques, such as EPSY622 and EPSY661.

Glen McGeeView profile

Assistant Professor

• Biostatistics
• Epidemiology
• Environmental Health
• +3 more

Glen McGee is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds a PhD in Biostatistics from Harvard University and a BScH in Mathematics from Queen's University. His research focuses on developing statistical tools for epidemiology, environmental health, and health policy, with a particular emphasis on environmental mixture analysis, cluster-correlated data modeling, and outcome-dependent sampling methodologies. Education : PhD in Biostatistics, Harvard University BScH in Mathematics, Queen's University Research Interests : McGee advances methodologies for analyzing complex environmental mixtures and their health impacts, including incorporation of biological knowledge into statistical frameworks. His work addresses challenges in multigenerational studies, informative cluster sizes, and measurement error correction in case-crossover designs. Key application areas include hospital profiling, exposure misclassification, and longitudinal health data analysis. Research Trends : His publications emphasize Bayesian methods for mixture modeling, innovative sampling strategies for clustered data, and causal inference techniques. Notable contributions include frameworks for integrating biological pathways into environmental health analyses and developing efficient sampling approaches for healthcare performance evaluation. Grants & Labs : McGee collaborates on projects involving CMS data applications and maintains GitHub repositories like hospODS for hospital profiling methodology implementation. His work bridges statistical theory with practical public health applications, particularly in environmental epidemiology and healthcare analytics.