Edward Kroc is an Associate Professor in the Department of Educational and Counselling Psychology, and Special Education at the University of British Columbia (UBC), Faculty of Education. He joined UBC in 2018 as part of the Measurement, Evaluation, and Research Methodology (MERM) program. His work bridges statistical theory, psychometrics, and ecological applications. Ph.D., Mathematics, University of British Columbia (2015) M.Sc., Applied Mathematics in Statistics, DePaul University (2007) B.Sc., Mathematics, DePaul University (2006) Kroc's research focuses on generalized measurement error, causal inference, and spatio-temporal modeling, with applications to urban ecology (e.g., gull populations) and psychometric validity. His methodological work challenges traditional assumptions in educational measurement and regression analysis. Recent publications highlight his interdisciplinary approach, combining statistics, ecology, and psychology. Key trends include the development of robust measurement frameworks, Bayesian methods for ecological data, and methodological critiques of reliability estimation techniques. Edward Kroc actively supervises graduate students in the Measurement, Evaluation and Research Methodology program and collaborates on interdisciplinary research projects and grants.
Dr. Tara Baldacchino is a Senior Lecturer in the School of Electrical and Electronic Engineering at the University of Sheffield, specializing in systems engineering and inclusive pedagogy. With a PhD from the University of Sheffield (2011), she bridges technical and educational research through her work in curriculum design and employability integration. Her affiliations include the Automatic Control and Systems Engineering (ACSE) department and the Dynamical Systems and Systems Engineering research group. PhD (University of Sheffield, 2011) MSc Advanced Control (University of Sheffield, 2007) BEng Electrical Engineering (First Class, University of Malta, 2006) Her research focuses on embedding employability and inclusive engineering design into technical curricula, with a particular emphasis on systems engineering principles. She teaches programming, embedded systems, and project-based learning frameworks such as ACS130 , ACS233 , and the Group Control Project module. Recent publications highlight her expertise in Bayesian system identification , nonlinear dynamics , and medical robotics , with applications spanning EMG signal analysis, magnetic capsule endoscopy, and structural health monitoring. Her methodological work includes probabilistic models and mixture-of-experts frameworks for robust system estimation. As Mechatronic and Robotic Engineering Programme Lead and Computer Systems Engineering Programme Lead , she shapes interdisciplinary curricula while maintaining active collaboration between ACSE and the Mechanical Engineering Department.
Dr. Gery Geenens is a Senior Lecturer in the School of Mathematics & Statistics at UNSW Sydney, specializing in nonparametric and semiparametric statistical methods. He holds a PhD in Sciences (Statistics) from Université catholique de Louvain (Belgium, 2008) and joined UNSW in 2009 after postdoctoral work at the University of Melbourne under Professor Peter Hall. His research focuses on developing flexible nonparametric models for statistical analysis, with particular expertise in copula modeling, functional data analysis, and high-dimensional density estimation. Key contributions include: Nonparametric copula-based conditional density estimation Wavelet-based multivariate density estimation with shape preservation Applications in sports analytics (e.g., football/soccer outcome modeling) Solutions to the "Curse of Dimensionality" in functional regression Recent publications demonstrate strong activity in dependence modeling and theoretical foundations of nonparametric statistics, with significant work on Sklar's theorem and Hellinger correlation. His research bridges theoretical advances with practical applications in finance, hydrology, and sports analytics. As Director of Postgraduate Studies (Coursework) and Associate Editor of "Statistics and Probability Letters", he actively contributes to academic service. His teaching portfolio includes core statistics courses for engineering and advanced probability. Current research is supported by UNSW Faculty Research Grants (2014-2016) and prior Early Career Research Grants (2010-2013). He co-supervises PhD candidates including Carlos Aya Moreno on wavelet-based density estimation.
Ralf Wilke serves as a Professor in the Department of Economics at Copenhagen Business School's School of Economics and Management. His academic profile demonstrates extensive expertise in econometric methods applied to duration analysis and labor market dynamics. Wilke's research focuses on advanced statistical techniques for analyzing time-to-event data, with particular emphasis on: Competing risks models under various identification frameworks Copula-based approaches for dependent censoring and spell durations Quantile regression applications in unemployment and maternity leave studies Spatial diffusion patterns of historical technologies Addressing omitted variable bias in regression analysis His methodological innovations bridge theoretical econometrics with empirical labor economics applications. Analysis of his recent publications reveals consistent contributions to duration modeling literature, particularly in developing robust frameworks for handling dependent censoring mechanisms and competing risks structures. His work demonstrates increasing integration of copula methods with traditional survival analysis techniques. Wilke actively contributes to economic history through spatial analysis of 19th-century technological diffusion patterns, showcasing interdisciplinary methodological transfer.
Vlad Stefan BARBU is an Associate Professor of Mathematics (Statistics) at the Laboratory of Mathematics Raphaël Salem UMR 6085, University of Rouen - Normandy (URN) - CNRS, France. He serves as Director of the Research Federation Normandy-Mathematics, Scientific Secretary of the Romanian Society of Probability and Statistics, and Vice-president of the Romanian Society of Applied and Industrial Mathematics. His research focuses on stochastic processes, particularly semi-Markov models and their applications in reliability, survival analysis, and biostatistics. Education: HDR (Habilitation to Conduct Research) in Statistics (2017) PhD in Statistics (2005) Master in Applied Statistics and Optimization (1997-1998) BA in Mathematics (Bac + 5) (1992-1997) Barbu's research interests center on semi-Markov and Hidden semi-Markov processes, Markov models, statistical inference for stochastic processes, and nonparametric estimation. His work extends to reliability and survival analysis, biostatistics with applications in DNA modeling, entropy and divergence measures, and model selection. He has developed several R packages for semi-Markov modeling, demonstrating his commitment to translating theoretical advances into practical tools for researchers and practitioners. Analysis of his recent publications reveals a consistent focus on advancing semi-Markov theory with applications across diverse domains. His work spans theoretical developments in estimation methods, hypothesis testing, and reliability analysis, while maintaining strong connections to practical applications in reliability engineering, biostatistics, and risk modeling. The interdisciplinary nature of his research is evident in publications spanning statistics journals, mathematics journals, and applied fields. Research Grants: Coordinator of project 'Reliability and Survival Analysis of Multi-State Random Systems' (2024-2025) Team leader for LMRS in ANR project 'Hidden Semi Markov Models: INference, Control and Applications' (2022-2025) Participant in ANR project 'Swimming and Para-swimming: All United for our Champions' (2020-2024) Participant in multiple regional and international research projects Barbu has supervised numerous PhD students and served on doctoral committees in France and abroad. His research leadership extends to coordinating significant research projects and organizing international conferences. He maintains active research collaborations across Europe, with frequent visits to institutions in Greece, Romania, and other countries. His work bridges theoretical statistics with practical applications, particularly in reliability engineering and biostatistics. As Director of the Research Federation Normandy-Mathematics, Barbu leads a substantial mathematical research network. His international engagement is further evidenced by his leadership roles in Romanian statistical societies and his participation in European research networks and projects.
Ken Frank is MSU Foundation Professor of Sociometrics and Professor in the Department of Counseling, Educational Psychology and Special Education within the College of Education at Michigan State University. He also holds appointments in Fisheries and Wildlife within the College of Agriculture and Natural Resources. Frank is affiliated with multiple research centers including the Center for Systems Integration and Sustainability, the Education Policy Center, and the Center for Statistical Training and Consulting (CSTAT). Frank received his academic training at prestigious institutions: Ph.D. in measurement, evaluation and statistical analysis from the University of Chicago (1993) Masters of Arts in Higher and Adult Continuing Education from the University of Michigan (1986-1988) Bachelor of Arts in Statistics and English from the University of Michigan (1981-1985) Dr. Frank's research program integrates substantive interests in the study of schools as organizations, social structures of students and teachers, school decision-making, and social capital with methodological expertise in social network analysis, sensitivity analysis and causal inference, and multi-level models. His work has significant implications for educational policy, organizational behavior, and environmental sustainability. Frank has developed innovative approaches to understanding how social contexts shape individual outcomes, particularly in educational settings. Frank's recent publications demonstrate a consistent focus on developing and applying robust methods for causal inference, with particular emphasis on sensitivity analysis through his KONFOUND framework. His work spans multiple domains including education policy, organizational behavior, environmental management, and healthcare, reflecting his interdisciplinary approach to understanding complex social systems. The recurring theme across his research is examining how network structures and social contexts influence individual behaviors, decision-making processes, and outcomes. Among his notable achievements: Named one of nation's top education influencers (2023) Elected to National Academy of Education (2021) Dr. Frank actively collaborates on numerous research projects examining how beginning teachers' networks affect their response to the Common Core, how schools respond to increases in core curricular requirements, school governance structures, teachers' use of social media, implementation of the Carbon-Time science curriculum, epistemic network analysis, social network interventions in natural resources and construction management, complex decision-making in healthcare, and the diffusion of knowledge about climate change. His work often involves interdisciplinary collaboration across education, sociology, environmental science, and statistics. Frank leads or participates in several research teams and initiatives, including the AHAA project supplementing the Add Health database with high school transcript information, the teachersinsocialmedia.com project examining educators' social media use, the Carbon-Time science curriculum project, and the epistemic network analysis initiative. His research frequently bridges theoretical advances in methodology with practical applications to address real-world problems in educational and environmental contexts.
Dr Wendy J Harrison is Associate Professor in Biostatistics at the University of Leeds’ School of Medicine, based in the Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM) and the Specialist Science Education Department. Since arriving as Lecturer in 2007 she has led quantitative teaching across MBChB and postgraduate programmes, and now directs the Health Informatics with Data Science MSc launched in 2022. Education & Qualifications PhD Latent Variable Modelling for Complex Observational Health Data (part-time, awarded 2017) PGCLTHE – Postgraduate Certificate in Learning and Teaching in Higher Education (2011) MSc Medical Statistics, University of Leicester BSc(Hons) Combined Studies Mathematics / Physics Research Interests Her methodological work focuses on causal inference techniques—especially directed acyclic graphs (DAGs), multilevel and latent-class / latent-variable modelling—for analysing complex observational health data. Applied domains include obesity, cancer epidemiology and paediatric cardiology. She uses insights from this research to embed contemporary quantitative methods into undergraduate and postgraduate medical education. Publications Snapshot Across 15 recent papers (2008-2021) three cross-cutting themes emerge: development and teaching of DAG-based causal inference tools, evaluation of multilevel latent-class methodologies via simulation and real data, and collaborative epidemiological studies in cancer, cardiology and perinatal health. These outputs underline a commitment to both advancing and disseminating robust statistical practice. Professional Memberships Fellow of the Royal Statistical Society (GradStat) Fellow of the Higher Education Academy (FHEA) Leadership & Administration She currently serves as Programme Lead for Postgraduate Programmes in Health Informatics with Data Science, Admissions Lead for all postgraduate programmes in her department, and Chair of the Postgraduate Special Circumstances Committee.
Dr. Damiano Varagnolo is a Senior Lecturer at the Control Engineering Group, Luleå University of Technology, Sweden. His research focuses on distributed optimization, control systems for data centers and HVAC, and biomedical engineering applications. Current affiliation: Luleå University of Technology Primary research areas: Distributed optimization, Newton-Raphson Consensus methods, networked systems, energy-efficient control Selected applications: Smart building systems, biomedical modeling of pain responses, data center thermal management His work on distributed optimization explores asynchronous protocols and robustness in networked systems. Recent publications demonstrate applications in peer-to-peer networks, biomedical engineering, and energy-efficient computing infrastructure. Key collaborative projects include IEEE Transactions publications on data center cooling, European Control Conference contributions on sensor calibration, and IFAC symposium presentations on biomedical modeling. His distributed Gaussian regression methods appear in TPAMI publications.
Dr. Dianliang Deng is a Professor in the Department of Mathematics & Statistics at the University of Regina, Canada. His academic affiliations include the Faculty of Science, where he specializes in advanced statistical methodologies and probability theory. He has held this position since at least 2000, as evidenced by his long-standing research output. Research Interests: Biostatistics and Probability: Survival analysis, statistical analysis of discrete data, longitudinal data analysis, and limit theorems in probability Focus areas include medical cost analysis, zero-inflated models, and applications in healthcare economics and genomics Recent Research Trends: Recent work emphasizes statistical models for medical costs, longitudinal quantile regression, and handling complex data structures like censored or sparse datasets. His publications span applications in genomics, healthcare economics, and time-series analysis. Awards & Grants: No specific awards or grants are explicitly mentioned in the provided texts. Labs & Teams: No lab affiliations or collaborative teams are listed, though his work suggests affiliation with interdisciplinary research groups in biostatistics and genomics.
Ali Zarezade is a researcher at the Max Planck Institute for Software Systems (MPI-SWS), focusing on interdisciplinary research spanning algorithms, machine learning, and social computing. His work integrates theoretical foundations with practical applications in cyber-physical systems, distributed networks, and human-centric AI. Key research areas include human-in-the-loop machine learning, optimal control strategies for social networks, and spatio-temporal modeling of social media activity. His methods often combine stochastic control theory, sparse regression techniques, and online algorithm design to address challenges in education technology, security, and information diffusion. Prior contributions involve developing algorithms like RedQueen and Cheshire for optimizing social network activity, probabilistic models for hyperspectral unmixing, and visual tracking systems resilient to occlusions. His research bridges computational theory with real-world systems, emphasizing scalable solutions for distributed and networked environments.
Dr. Bo Wang is an Assistant Professor at the University of Toronto's Department of Laboratory Medicine & Pathobiology, with a joint appointment in the Department of Computer Science. He holds the Canada CIFAR Artificial Intelligence Chair and serves as Chief Artificial Intelligence Scientist at the University Health Network (UHN). Previously, he was affiliated with the Department of Medical Biophysics and Surgery. Dr. Wang leads the Temerty Centre for AI Research and Education in Medicine (T-CAIREM), focusing on integrating AI into biomedical research and education. He earned his PhD in Computer Science from Stanford University in 2017, specializing in computational biology, cancer subtype prediction, and single-cell analysis. His research interests bridge artificial intelligence, genetics, and healthcare, with a focus on causal inference, machine learning applications in biomedicine, and genomic data analysis. He has pioneered courses like Basic Principles of Machine Learning in Biomedical Research alongside Dr. Rahul G. Krishnan. His work includes developing algorithms for high-dimensional variable selection and instrumental variable methods in causal analysis. Dr. Wang's publications emphasize causal mediation analysis, genetic risk scoring, and longitudinal health outcomes. Notable contributions include studies on vaccine efficacy against long-COVID, schizophrenia treatment efficacy, and Alzheimer’s disease biomarker modeling. Awards include the Canada CIFAR AI Chair (Vector Institute). His research is supported by collaborations with leading institutions and spans computational biology, healthcare analytics, and precision medicine.
Jerome Reiter is Professor of Statistical Science at Duke University. He holds no named chair appointments. Dr. Reiter's research focuses on methodological approaches for preserving data confidentiality, handling missing values, integrating information across data sources, and analyzing surveys and causal studies. He actively collaborates with non-statisticians in social sciences and public policy. Key research areas include: Differentially private data analysis methods Synthetic data generation for confidential datasets Multiple imputation techniques for complex surveys Causal inference with privacy constraints His work develops statistical frameworks to enable research access to confidential data while protecting privacy.
Dr. Christian Rohrbeck is a Senior Lecturer in the Department of Mathematical Sciences at the University of Bath. He holds a PhD in Statistics and Operational Research from Lancaster University and leads projects in statistical climatology through the EPSRC Centre for Doctoral Training in Statistical Applied Mathematics and Centre for Climate Adaptation & Environment Research. His research combines extreme value theory, spatial statistics, and Bayesian methods to analyze environmental and financial data. Current projects focus on flood risk modeling, climate extremes standardization, and monotonic regression techniques. Dr. Rohrbeck accepts doctoral students in extreme value analysis, flood risk, and climate adaptation statistics.
Paulus Bekker is a Professor in the Department of Economics, Econometrics and Finance at the University of Groningen's Faculty of Economics and Business. His research specializes in advanced econometric methodologies and financial modeling techniques. Research focuses on developing statistical methods for economic and financial applications. Primary interests include econometric theory, instrumental variable techniques, dynamic financial modeling, and arbitrage-free pricing frameworks. Recent work examines concentrated instrument methodologies and generalized yield curve modeling. Publications demonstrate consistent methodological innovation in econometrics. Recent articles feature applications in panel data modeling, dynamic mean-variance analysis, and robust estimation techniques. Research frequently addresses challenges in financial model specification and statistical inference.
Guanqun Cao is an Associate Professor in the Department of Computational Mathematics, Science and Engineering (CMSE) at Michigan State University, affiliated with both the College of Engineering and the College of Natural Science. His research focuses on functional data analysis, statistical inference, machine learning, and deep learning applications. He specializes in developing methodologies for high-dimensional and complex data structures, with contributions to fields ranging from computational statistics to biotechnology and public health. Dr. Cao's work emphasizes the integration of advanced statistical techniques with modern computational tools. Recent studies include applications in cross-domain Wi-Fi sensing, CRISPR gene editing efficiency in aquaculture, and socio-economic disparities in food environments. His research often bridges theoretical developments and practical implementations, addressing challenges in classification, prediction, and robust estimation. His publications reflect a strong emphasis on functional data analysis through deep neural networks, with contributions to multi-class classification, feature selection, and algorithmic optimization. Notable trends in his work include interdisciplinary collaborations, leveraging machine learning for scientific discovery, and advancing statistical methodologies for complex data. Dr. Cao has no listed scientific awards or formal advisees, though his academic contributions span over a decade with publications in top journals. His research is conducted at MSU’s Wells Hall, Room C426, where he continues to explore cutting-edge solutions in computational mathematics and engineering.
