Nancy Margaret Reid is a University Professor of Statistical Sciences at the University of Toronto, holding the Canada Research Chair in Statistical Theory and Applications. She has served as Scientific Director of the Canadian Statistical Sciences Institute (2015–2019) and led the Department of Statistical Sciences as Chair (1997–2002). Her research focuses on theoretical statistics, particularly likelihood inference and foundational aspects of statistical methodology. Reid earned her PhD from Stanford University (1979) under Rupert G. Miller, with Brad Efron and Vernon Johns on her committee. Reid's accolades include Fellowships from the Royal Society, Royal Society of Canada, and National Academy of Sciences, as well as the Guy Medal in Gold (2022) and David R. Cox Award (2023). She has authored influential books like *Theory of the Design of Experiments* and contributed to courses on mathematical statistics and likelihood inference. Active in academic service, she teaches graduate-level courses and has advised numerous students and postdocs in theoretical and applied statistical research.
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.
Demetris Christodoulou is an Associate Professor in Accounting, Governance and Regulation at the University of Sydney. He holds a BEcon from Piraeus University, an MSc(Fin) from the University of York (UK), and a PhD from Athens University of Economics and Business (AUEB). His research focuses on applying data analytics, econometrics, and visualization techniques to financial analysis, equity valuation, life insurance, and financial advice. He co-directs the PEMA research group, specializing in productivity and performance measurement analytics, and previously led the MEAFA research group (2007–2022). He has collaborated extensively with industry partners including Deloitte and Australian insurers, and developed training programs for over 1,000 executives. His work includes open-source contributions to Stata software and the Graph Workflow platform, alongside $662k in workshop-generated funds supporting academic programs. He has advised multiple PhD students and taught at leading universities globally. Education: BEcon in Economics (Econometrics), Piraeus University MSc in Finance, University of York (UK) PhD in Accounting and Financial Analysis, Athens University of Economics and Business His research interests span financial reporting models, life insurance underwriting strategies, and behavioral finance. Recent projects address dishonesty mitigation in insurance disclosures and the adviser effect on customer disclosures. He has published widely in top journals like the Review of Accounting Studies and Stata Journal , and his work was featured in The Australian for insights on insurance fraud reduction. He maintains international collaborations, including visiting roles at Columbia Business School and the University of Cyprus, and serves on advisory boards for organizations like Behaviour.ai. Publications highlight methodological innovations in econometrics and visualization, with 2025's upcoming Stata Journal paper advancing time-series analysis techniques. His grants include partnerships with industry on longitudinal studies of insured lives, aiming to improve risk modeling and public policy insights.
Richard J. Cook is a University Professor and Mathematics Faculty Research Chair in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds cross-appointments at the School of Public Health and Health Systems at the University of Waterloo and the Faculty of Health Sciences at McMaster University. Previously, he held a Tier I Canada Research Chair in Statistical Methods for Health Research from 2005 to 2019. His educational background includes: BSc in Statistics from McMaster University MMath in Mathematics from University of Waterloo PhD in Statistics from University of Waterloo Professor Cook's research focuses on developing and applying statistical methods for public health research. His primary areas of interest include the analysis of life history data, longitudinal data analysis, methods for incomplete data, clinical trial design, and multivariate analysis. His work provides critical methodological frameworks for understanding disease progression and evaluating interventions in complex health settings. He has made significant contributions to the development of multistate models for disease processes and methods for handling interval-censored data. His extensive publication record demonstrates consistent focus on methodological innovations addressing real-world health research challenges. Recent work emphasizes estimand specification in clinical trials, transportability of research findings, and causal inference methods. His research bridges theoretical statistics with practical applications in autoimmune diseases, transfusion medicine, and public health. Professor Cook has received significant professional recognition: Tier I Canada Research Chair in Statistical Methods for Health Research (2005-2019) Mathematics Faculty Research Chair at University of Waterloo His students have earned prestigious awards including multiple Pierre-Robillard Awards, ISCB Student Conference Awards, and ENAR Distinguished Student Paper Awards, with notable achievements like Dr. Shu (Joy) Jiang being named in the Forbes Top 30 Under 30 North America (2023) for Healthcare. Professor Cook has advised numerous graduate students throughout his career, with many going on to successful academic and industry positions. His research has been supported by various grants, and he collaborates extensively with researchers in rheumatology, transfusion medicine, and public health through affiliations with the Centre for Prognosis Studies in Rheumatic Diseases, the International Psoriasis and Arthritis Research Team, and the McMaster Centre for Transfusion Research. He leads a vibrant research team that includes research associates, post-doctoral fellows, and graduate students working on cutting-edge statistical methodology. His research group maintains strong connections with multiple institutions and research centers focused on health outcomes and disease progression.
Dr. Katerina Marcoulides is an Associate Professor in the Quantitative and Psychometric Methods Program at the University of Minnesota's Department of Psychology. She is affiliated with the Minnesota Population Center and serves as Co-Chair of the Structural Equation Modeling Special Interest Group (SEM SIG) for the American Educational Research Association. Her research focuses on advanced data mining and modeling techniques for complex longitudinal data, particularly applied to developmental processes in economically disadvantaged immigrant children. She holds a PhD in Quantitative Psychology from Arizona State University, an MA from UC Davis, and a BA from UC Santa Barbara. Education: PhD: Quantitative Psychology, Arizona State University MA: Quantitative Psychology, University of California, Davis BA: Psychology (minor in Education), University of California, Santa Barbara Research Interests: Dr. Marcoulides develops and applies statistical methods such as structural equation modeling (SEM), Bayesian synthesis, and data fusion to study developmental and educational processes. Her work emphasizes longitudinal data analysis, item response theory, and multilevel modeling. Recent projects include NIH-funded research on parenting, marginalization, and well-being during the pandemic. Awards: APS Rising Star Award (2021) NIH Grant Award Teaching & Collaboration: She teaches courses on SEM, multilevel modeling, and data analysis at the University of Minnesota. Previously at the University of Florida, she contributed to workshops on educational data mining and served as an APA Advanced Training Institute presenter. Her interdisciplinary collaborations span population studies, health inequities, and workforce research. Labs & Groups: She leads the Data Analytics and Visualization Lab and actively participates in the Minnesota Population Center, integrating computational and statistical innovations with real-world applications.
Professor Roberta Fida is the Head of the Leadership and Management Department at Aston Business School, part of Aston University in the UK. She holds a Professorship in Organisational Behaviour and Organisational Psychology, with prior roles including Professor of Work Psychology at the University of East Anglia and Chair of the Ethics Committee there. A Chartered Occupational Psychologist with the British Psychological Society, her research focuses on unethical workplace behaviors, moral disengagement, and workplace stress, particularly in healthcare contexts. She specializes in advanced data analytics like structural equation modeling and has led major grants including EDI initiatives and studies on workplace aggression policy mapping. Her work bridges academic research with practical applications to foster ethical and inclusive organizational cultures. Education: Laurea (5-year degree in Work and Organizational Psychology) from Sapienza University of Rome (2002) MA in Organizational Training (2004–2005) PhD in Psychology from Sapienza University (2006) Postgraduate Certificate in Higher Education Practice (UEA, 2017) Italian National Qualifications as Full Professor (2017) and Associate Professor (2013) Research Interests: Her core areas include moral disengagement, counterproductive workplace behaviors, stress resilience, healthcare leadership, and EDI. She has published over 60 articles and is listed in Stanford's Top 2% Scientists globally. Awards & Grants: NIHR Work & Health Development Award (2023–2024) for EDI Smart-Dash (£95,855) INAIL grants totaling over €1.1 million for psychosocial risk studies and work-related stress analyses ESRC funding for workplace wellbeing research (£659,540) Research Council of Canada grant for nurse leadership studies ($202,852) Teaching & Supervision: She has taught across undergraduate to postgraduate levels, including modules on organizational psychology, multivariate analysis, and conflict management. She supervises PhD students and has led course design for programs focusing on organizational wellbeing and research methods.
Teemu Roos is a Professor at the Department of Computer Science , University of Helsinki , and a Principal Investigator for the Complex Systems Computation Group under the Helsinki Institute for Information Technology. He serves as a Supervisor for the Doctoral Programme in Computer Science and leads multiple research initiatives, including Distributed AI in Supercomputing , AI & Kids , and Generation AI . Dr. Roos also holds a Docent title in Computer Science. His research spans Artificial Intelligence , Machine Learning , and Data Science , with a focus on AI education , graph neural networks , Bayesian modeling , and health informatics . He has pioneered tools like Elements of AI , a free online course now translated into 22 EU languages, and explores the ethical implications of AI-generated content in authorship and inventorship. The 15 most recent publications highlight applications in environmental forecasting (e.g., Mediterranean Sea via graph-based deep learning), healthcare (e.g., skin cancer detection with transfer learning), and social media analysis (e.g., explainable AI platforms for K-12 education). Methodologically, his work advances clustering algorithms , dimensionality reduction , and approximate nearest neighbor search . Scientific Awards: Cor Baayen Award (2009) Nokia Foundation Recognition Award (2019) Best Paper Honorable Mention Award (2013) ICT Influencer of the Year 2019 (Vuoden TiVi-vaikuttaja 2019) World Summit AI's Top-50 Innovators in 2020 Dr. Roos has supervised 2 doctoral students and contributed to 163 academic activities , including invited talks at MIT, University of Cambridge, and the Finnish Institute in Rome. He has secured funding from the Academy of Finland and the Strategic Research Council, focusing on projects like Fast AI-assisted Space Environment Prediction and Urban Exerciser .
Debdeep Pati is a Professor in the Department of Statistics at the University of Wisconsin-Madison, affiliated with the School of Computer, Data & Information Sciences. His research focuses on Bayesian methods, high-dimensional data analysis, machine learning, and computational statistics, with applications in health data and network analysis. He has contributed to approximate Bayesian computation, graphical models, and fair algorithms. Key research interests include Bayes theory in high dimensions, hierarchical modeling, efficient Bayesian computation, and real-time tracking algorithms. His work bridges theoretical advancements with practical applications in areas like electronic health records and nuclear physics constraints. Recent work emphasizes Wasserstein-guided nonparametric Bayes, fair clustering algorithms, and variational inference in singular models. He has developed software for covariate-dependent Gaussian graphical modeling, published in ACM Transactions on Mathematical Software . Grants: NSF proposal on Wasserstein-guided nonparametric Bayes, NIH R01/R21 grants on periodontal disease and diabetes comorbidity. Advising: No named advisees listed but actively supervising research in Bayesian computation and high-dimensional statistics. Awards: 2024 JASA reproducibility award for 'Covariate-Assisted Bayesian Graph Learning.' He is an Associate Editor for Journal of Computational and Graphical Statistics and has organized workshops at Banff International Research Station (BIRS) and the Institute for Mathematics and its Applications (IMSI).
Vikas Singh is a Professor in the Department of Biostatistics at the University of Wisconsin-Madison, with appointments in Computer Sciences and Statistics. He also serves as a part-time Faculty Researcher at Google DeepMind. His research focuses on image analysis, machine learning, and medical imaging applications, particularly in neuroimaging and Alzheimer's disease studies. Singh holds a Ph.D. in Computer Science from SUNY Buffalo and has taught courses such as BMI/CS 767 (Medical Image Analysis) and CS 766 (Computer Vision). Affiliations: UW Computer Vision Group, Wisconsin Alzheimer's Disease Research Center (W-ADRC), Machine Learning@UW. Research: Develops algorithms for medical image analysis, including tools for neuroimaging and longitudinal biomarker studies. Grants: Collaborates on grants related to Alzheimer's progression modeling and imaging techniques. His work emphasizes interdisciplinary applications, bridging statistics, geometry, and optimization to solve real-world problems in healthcare and engineering.
Naisyin Wang is a Professor of Statistics at the University of Michigan, where she has been since 2009. Previously, she served as a faculty member in Statistics and Toxicology at Texas A&M University from 1992. She holds a Ph.D. in Statistics from Cornell University (1992), an M.A. in Statistics from Ohio State University (1987), and a B.S. in Mathematics from National Tsing-Hua University, Taiwan (1986). Her research focuses on longitudinal and functional data analysis, measurement error models, semiparametric methods, and applications in biological and medical fields, particularly genomics and metabolomics. Key contributions include methodologies for handling missing data, mixed effects models, and clustering techniques. Education: Ph.D. in Statistics, Cornell University (1992) M.A. in Statistics, Ohio State University (1987) B.S. in Mathematics, National Tsing-Hua University (1986) Dr. Wang’s honors include the College of Science Distinguished Alumni Award (2012), the Distinguished Achievement Award in Research (2003), and fellowships from the AAAS, ASA, and IMS. She has held leadership roles, including Co-editor of Statistica Sinica (2011–2014) and President of the International Chinese Statistical Association (2010). Her teaching includes courses such as Applied Statistics (STATS 500), Linear Models (STATS 600), and Special Topics in Applied Statistics (STATS 700). She has advised numerous students and contributed to research on cancer genomics, dietary interventions, and statistical methodology.
Professor Luke Prendergast is the Deputy Dean of the School of Computing, Engineering & Mathematical Sciences (SCEMS) at La Trobe University (LTU) and holds a Professorship in the Department of Mathematics and Statistics. He previously served as Head of Department (2014–2020) and led LTU's Statistics Consulting Platform. His research focuses on robust statistics, meta-analysis, dimension reduction, and applied statistics, leading the DRAMA research group. Collaborations span fields like endocrinology, disability studies, and respiratory health. He actively contributes to research grants, including projects on Prader-Willi syndrome and exercise for disability populations. Professor Prendergast's recent work emphasizes statistical software development (e.g., the rquest package) and applications in biostatistics, such as metabolomics analysis and health intervention fidelity. His articles address topics like quantile-based hypothesis testing, geospatial accessibility for disability care, and motivational interviewing efficacy. Professional roles include NHMRC grant review panels, editorial boards for Nutrients and Respirology , and leadership in the Statistical Society of Australia (SSA Vic). His teaching includes courses in meta-analysis, linear models, and data-based critical thinking. Grants funded projects on exercise programs for cerebral palsy populations and community-university partnerships for disability inclusion. Luke's work bridges statistical theory with real-world health challenges, emphasizing robust methodologies and interdisciplinary collaboration.
Professor Arcot Sowmya is a distinguished academic at the University of New South Wales, serving as Professor in the School of Computer Science and Engineering. With a strong background in both computer science and mathematics, she has established herself as a leading researcher in machine learning and computer vision applications, particularly in medical imaging and diagnostics. Dr. Sowmya earned her PhD in Computer Science from the Indian Institute of Technology, Bombay, along with an MTech in Computer Science, MSc in Mathematics, and BSc in Mathematics from the same institution. Her academic journey has positioned her at the intersection of theoretical computer science and practical medical applications. Her research interests span multiple domains with a primary focus on Machine Learning for Computer Vision . She has made significant contributions to learning object models, feature extraction, segmentation, and recognition techniques. Her work extends into medical image analysis, computer-aided diagnostics, high-resolution remote sensing, and biomedical informatics. More recently, she has applied similar techniques to social sciences domains, developing improved forecasting models for genocide and politicide. Her earlier work also includes contributions to real-time, concurrent, and embedded systems. Analyzing her recent publications reveals a strong trend toward medical applications of computer vision and deep learning. Her work spans from OCT-based glaucoma diagnosis to tumor segmentation, lung disease detection, and breast cancer prognosis. She has successfully bridged computer science with clinical medicine, developing practical tools for disease diagnosis and prediction that incorporate explainable AI approaches. Professor Sowmya's collaborative approach is evident in her extensive publication record across multiple journals and conferences. She has worked with researchers from diverse fields including ophthalmology, oncology, neurology, and public health, demonstrating the interdisciplinary nature of her research. Her laboratory work focuses on developing robust deep learning architectures for medical image analysis, with particular attention to segmentation networks, transformer models, and multimodal data fusion techniques. Her team has developed specialized networks for lung segmentation, tumor detection, and disease classification that address specific challenges in medical imaging.
Dr. Jannah Baker is a Research Fellow at the Sydney School of Public Health, University of Sydney. She holds a PhD in Statistics specializing in Bayesian spatiotemporal modelling of chronic diseases, alongside dual postgraduate diplomas in Public Health and Statistics. Her clinical background includes five years as a practicing physician. Her research focuses on cancer prevention (breast, endometrial, melanoma), spatiotemporal disease modeling, health economics, and clinical trial design. She has led projects on sepsis detection systems, diabetes management, and surgical outcomes analysis. Notable contributions include a seminal systematic review on fertility-sparing endometrial cancer treatments referenced in international guidelines. Jannah has attracted $6M+ in collaborative funding and oversees grants such as the 2023 NHMRC-funded ROADMAP trial and a Department of Health-funded study on pediatric sepsis management. Her work spans 50+ peer-reviewed articles across journals like Journal of Medical Internet Research and PLOS One . Professional activities include clinical trials statistics, health economics analysis, and implementation science lectures. She has mentored multiple research teams and maintains an ORCID profile (0000-0002-2208-6584).
Marc G. Berman is a Professor and Chair of the Department of Psychology at the University of Chicago. His research focuses on understanding how environmental factors interact with human cognition, emotion, and behavior, particularly through the lens of environmental neuroscience. He leads the Environmental Neuroscience Lab (ENL), investigating how natural and urban environments influence brain function, memory, attention, and mental health. Dr. Berman holds a B.S.E. in Industrial and Operations Engineering from the University of Michigan and a Ph.D. in Psychology and Engineering from the same institution. His postdoctoral training was at the Rotman Research Institute in Toronto. Prior to Chicago, he was an Assistant Professor at the University of South Carolina. His research interests include the cognitive and affective benefits of natural environments, brain network efficiency, and the neurobiological underpinnings of self-control and emotion regulation. Recent work explores how urban design elements (e.g., greenspace, street activity) relate to crime rates and mental health outcomes, leveraging big data from social media and geospatial tools. Key contributions include demonstrating that natural environments improve memory and attention by ~20%, and that city characteristics like population diversity and segregation correlate with implicit racial biases. His lab employs fMRI, neuroimaging, computational modeling, and ecological data to quantify brain-environment interactions. Dr. Berman collaborates across disciplines, integrating neuroscience, psychology, urban planning, and data science to inform evidence-based environmental design for public health. Current projects include analyzing social media data to map gang networks and investigating how heat and greenspace influence emotional states in urban populations.
Sylvia Richardson is an MRC Investigator at the MRC Biostatistics Unit and holds a Research Professorship at the University of Cambridge, where she served as Director of the Biostatistics Unit from 2012 to 2021. She is affiliated with the Cambridge Mathematics of Information in Healthcare Hub (CMIH) at the Centre for Mathematical Sciences. Her work bridges advanced statistical methodology with critical healthcare applications, particularly in the analysis of complex biomedical data. Richardson's research spans multiple domains of biostatistics with a strong emphasis on Bayesian approaches. Her work has significantly advanced spatial modeling and disease mapping techniques, developed sophisticated methods for handling measurement error in epidemiological studies, and pioneered mixture and clustering models for integrative analysis of heterogeneous data sources. Her research addresses fundamental challenges in analyzing longitudinal health data, multimorbidity patterns, and complex disease trajectories. Her publication record demonstrates consistent methodological innovation applied to pressing healthcare challenges. Recent work focuses on traumatic brain injury outcomes, multimorbidity progression, genomic analysis, and statistical approaches to pandemic data. The articles reveal a strong pattern of methodological development driven by real-world healthcare challenges, with particular attention to longitudinal analysis, Bayesian computation, and integrative modeling approaches that can handle diverse and complex data structures. While specific awards are not detailed in the available information, Richardson's leadership as Director of the MRC Biostatistics Unit for nearly a decade and her continued Research Professorship reflect significant recognition of her contributions to the field. Her work with major international consortia like CENTER-TBI demonstrates her role in large-scale collaborative research efforts addressing critical health challenges. Richardson's research has substantial implications for healthcare policy and practice, particularly in understanding disease progression, developing predictive models for patient outcomes, and creating methodological frameworks that can integrate diverse data sources to generate meaningful clinical insights. Her work continues to influence both statistical methodology and healthcare applications through ongoing research and leadership in the field.
