Mathias Drton is a Professor of Mathematical Statistics at the Technical University of Munich, where he has worked since 2019 after a long career in the United States (Chicago, Seattle). His research focuses on methodological and theoretical aspects of statistical problems involving multivariate data, particularly graphical models where algebraic methods have shown utility. He maintains active collaborations with the Nonlinear Algebra group at the Max Planck Institute for Mathematics in the Sciences, including a visit during the institute's 25th anniversary in 2021.
Michel Besserve is a Senior Research Scientist in the Empirical Inference department at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. His research bridges machine learning theory with applications in neuroscience and complex systems analysis. He leads a research group focused on developing causal machine learning tools to uncover the internal structure and transformations of complex artificial, physical, and socioeconomic systems. Dr. Besserve's primary research interests center on causal machine learning and its applications to understanding complex systems. His work investigates how causality can provide principled ways to study and improve AI algorithms, particularly focusing on the identifiability of causal models and the principle of Independence of Causal Mechanisms (ICM). He develops theoretical frameworks and practical tools for causal inference in complex equilibrium systems, neural circuits, and socioeconomic contexts. His research has significant implications for building trustworthy and interpretable AI systems that can reliably handle real-world complexity. Analysis of Dr. Besserve's recent publications reveals a strong focus on causal representation learning, with significant contributions to independent mechanism analysis and the identifiability of nonlinear generative models. His work spans both theoretical foundations and practical applications, connecting machine learning with neuroscience to understand brain function through causal inference. The interdisciplinary nature of his research is evident in publications spanning top machine learning conferences (NeurIPS, ICML, ICLR) and leading neuroscience journals (Nature, PLOS Biology). Dr. Besserve has established productive collaborations across multiple institutions, particularly with researchers at the Max Planck Institute and ETH Zurich. His work demonstrates how integrating causal principles with machine learning can address fundamental challenges in AI robustness and interpretability, with applications ranging from brain network analysis to economic modeling. His research group focuses on developing the Causal Computational Model (CCM) framework, which aims to create digital representations of real-world systems that integrate data, domain knowledge, and interpretable causal structure. This work has potential applications in climate modeling, industrial digital twins, and economic simulation.
Theresia Gschwandtner is a Researcher at TU Wien's Research Division of Visual Analytics (E193-07). Her work focuses on advancing visual analytics methodologies for temporal data, fraud detection, and uncertainty visualization. She leads the Network Lab and contributes to tools like TimeCleanser for data cleansing and NEVA for fraudulent network identification. Her research emphasizes interactive systems for guidance in data analysis, provenance tracking, and enhancing user-centric visualization frameworks. Key research interests include temporal data preprocessing, multivariate time series analysis, and the integration of automated guidance systems into visual analytics platforms. She has collaborated on projects such as Hermes (economic network exploration) and TBSSvis (temporal blind source separation), which combine algorithmic innovation with intuitive user interfaces. Guidance frameworks and user studies are central to her work, exploring how automated support impacts performance and mental state during complex data analysis tasks. She has advised students on theses addressing data quality, cyclical pattern detection, and lighting design visualization. Notable contributions include the Quantifying Uncertainty in Time Series Processing framework and the LightGuider system for interactive lighting design guidance. Her work bridges theoretical advancements with practical applications in healthcare, finance, and engineering domains.
Silvia Miksch is a Full University Professor of Visual Analytics at TU Wien's Faculty of Informatics, leading the CVAST Center. She holds a PhD from the University of Vienna and has held roles including Head of the Department of Information and Knowledge Engineering at Danube University Krems. Her research focuses on Visual Analytics, Information Visualization, Temporal Data Analysis, and Medical Informatics. She has supervised numerous PhD and Master’s students, with notable advisees including Ignacio Baltazar Pérez Messina and Davide Ceneda. Her work bridges theory and practice, addressing challenges in Visual Analytics for healthcare, business intelligence, and digital humanities. Awards include the IEEE VGTC Technical Achievement Award (2023) and induction into the IEEE Visualization Academy (2020). She actively contributes to conferences like IEEE VIS and EuroVis as program chair and steering committee member. Her projects, such as 'VisuExplore' and 'DisCo', have received recognition for advancing visualization in medical and cultural domains. Key research areas include guidance-enriched systems, network visualization, and temporal reasoning. She explores applications in fraud detection, cultural heritage analysis, and pandemic data visualization. Her lab's tools, like 'Hermes' and 'COVIs', exemplify task-driven design for real-world data challenges.
Sotirios Bersimis is an Associate Professor at the University of Piraeus, Department of Business Administration. He holds additional roles as an elected member of the board of directors of the National Statistical Institute (Greece) and representative for FenSTATs and ECAS. Previously, he served as President of the Hellenic Organization for Health Care Services (EOPYY) and as President of the European Healthcare Fraud & Corruption Network (EHFCN). His education includes a PhD in Statistics from the University of Piraeus, an MSc in Statistics from Athens University of Economics and Business, and a BSc in Statistics and Insurance Science from the University of Piraeus. His research focuses on stochastic models for process monitoring, statistical process control, and health analytics. He has published over 60 peer-reviewed articles in journals like Journal of Quality Technology , Statistics in Medicine , and Annals of the Institute of Statistical Mathematics . His work emphasizes applications in healthcare surveillance, quality management, and fraud detection. Notable contributions include the development of the Process Monitoring Group and the 'Multivariate Statistical Process Control Charts: An Overview' paper, which remains highly cited in the field. Bersimis has received awards such as the 2018 ENBIS Best Manager Award and a public honor from the Greek Prime Minister. He actively collaborates with healthcare institutions, pharmaceutical companies, and international organizations, contributing to projects on health expenditure modeling and anti-fraud initiatives. His teaching spans undergraduate and postgraduate programs in statistics, biostatistics, and data science.
Anton Westveld is a Senior Lecturer in the Department of Statistics at the Australian National University (ANU), within the Research School of Finance, Actuarial Studies & Statistics (RSFAS). He also serves as an Affiliate Associate Professor at Virginia Commonwealth University since August 2023. His research focuses on Bayesian methodology, network analysis, game theoretic data, and statistical causality, with notable contributions to ecological modeling and agent-based stochastic simulations. Westveld holds a Bachelor’s in Economics and Political Science from the University of Michigan (Ann Arbor), a Master’s in Applied Economics and Statistics from the same institution, and a PhD in Statistics from the University of Washington. His work has been published in prestigious journals like the Annals of Applied Statistics and Proceedings of the National Academy of Sciences . His research interests span Bayesian inference, relational data analysis, and causal modeling, with applications in ecological and health sciences. Recent work includes developing Bayesian methods for ecological drivers in marine viral communities and latent socioeconomic health indices for policy evaluation. Notable articles include analyses of menstrual disorder surveys using Gaussian copulas, ecological metagenomics studies, and Bayesian-optimized bootstrap techniques for uncertainty quantification. His interdisciplinary collaborations bridge statistics with environmental science, public health, and economics.
Jack Gallant is a Professor of Neuroscience at the University of California, Berkeley, where he leads a research laboratory focused on cognitive, systems, and computational neuroscience. His work centers on developing and applying advanced methods for analyzing functional magnetic resonance imaging (fMRI) data, particularly through the Voxelwise Encoding Model framework. Dr. Gallant's research interests span computational neuroscience, cognitive neuroscience, and systems neuroscience, with a particular focus on understanding how the brain represents visual and semantic information. His lab develops cutting-edge tools for brain mapping and neural decoding, creating detailed maps of cortical organization related to visual and language processing. Recent work has emphasized group-level analysis techniques that integrate data across multiple participants while accounting for individual differences. His publication record demonstrates consistent innovation in fMRI methodology, with recent papers focusing on comprehensive frameworks for encoding models, individual differences in brain organization, and high-resolution mapping of semantic representations. Gallant's work bridges theoretical neuroscience with practical applications, resulting in widely used software tools that advance the field of neuroimaging. Sloan Research Fellow (1998) Dr. Gallant has mentored numerous graduate students and postdoctoral researchers, including Emily Meschke who recently completed her PhD. His lab maintains active collaborations and is currently recruiting postdocs to continue developing the Voxelwise Encoding Model framework. The lab also produces educational resources including interactive brain viewers and comprehensive tutorials that have become standard tools in the neuroimaging community.
Fang Han is an Associate Professor in the Department of Statistics at the University of Washington and an Adjunct Professor in Economics. He is also an Affiliated Investigator at Fred Hutchinson Cancer Research Center. His research focuses on rank-based and graph-based methods , statistical optimal transport , and nonparametric/semiparametric regression . Education: Ph.D. in Biostatistics, Johns Hopkins University (2015) - supported by Google Ph.D. Fellowship M.S. in Biostatistics, University of Minnesota B.S. in Mathematics, Peking University Research interests span rank correlation , causal inference , nonparametric regression , high-dimensional statistics , and random matrix theory . His recent work includes adaptations of Chatterjee's correlation to manifold data and rigorous analysis of matching estimators' variance. Scientific awards include Bernoulli Society New Researcher Award (2021) Google Ph.D. Fellowship (2013-2015) Margaret Merrell Award (2015) National Science Review 2015 Best Paper (2016) Teaching includes courses like Advanced Theory of Statistical Inference , Time Series Analysis , and Foundations of Machine Learning . He serves as Associate Editor for Bernoulli and editorial board member for Dependence Modeling .
Bruno Ebner is a researcher at the Institute of Stochastics within the Department of Mathematics at Karlsruhe Institute of Technology (KIT). He maintains an active research program in theoretical and applied statistics, with particular expertise in goodness-of-fit testing and distribution characterizations. His office is located in Kollegiengebäude Mathematik (20.30) room 2.018, and he holds regular office hours on Tuesdays from 2 p.m. to 3 p.m. Dr. Ebner's primary research interests focus on asymptotic statistics , goodness-of-fit problems , stochastic processes , and distribution characterizations . His work prominently features Stein's method as a theoretical foundation for developing new statistical tests. He has made significant contributions to directional data analysis, particularly for hyperspherical data, and has developed novel approaches for testing uniformity on spheres. Analysis of his recent publications reveals a strong trend toward developing unified theoretical frameworks for goodness-of-fit testing across various distribution families. His work increasingly integrates computational methods with theoretical statistics, particularly through collaborations that bridge Stein's method with modern computational techniques. The development of R packages like gofIG, mnt, and gofgamma demonstrates his commitment to making theoretical advances accessible to practitioners. Dr. Ebner has developed several R packages that implement his theoretical work, including gofIG for Inverse Gaussian distribution testing, mnt for multivariate normality tests, and gofgamma for Gamma distribution testing. These packages represent significant contributions to statistical methodology with practical applications across various scientific domains. His teaching portfolio demonstrates expertise across multiple domains, including introductory stochastics for teaching candidates, generalized regression models, statistics for biology students, and specialized courses on Stein's method. He has also contributed to educational initiatives for economics students at KIT, reflecting his commitment to statistical education across disciplines.
Giancarlo Manzi is an Associate Professor of Statistics at the Department of Methods and Models for Economics, Territory, and Finance, University of Rome La Sapienza. He holds a PhD from the University of Milan-Bicocca and conducted thesis research at the University of Toronto. His career includes roles at the Medical Research Council Biostatistics Unit in Cambridge and the University of Milan. University of Rome La Sapienza (Current) Medical Research Council Biostatistics Unit (Former Researcher) University of Milan (Former Researcher and Associate Professor) University of Milan-Bicocca (PhD) University of Toronto (Thesis collaboration) His research spans Machine Learning , Bayesian Statistics , and Smart Mobility , with a focus on Covid-19 analytics , data visualization , and epidemiological modeling . He integrates Multivariate Statistics with Public Health to address complex challenges in health systems and urban environments. The 15 most recent publications highlight expertise in quantile regression , Bayesian networks , time-series analysis , and smart mobility optimization . His methodological contributions include wavelet analysis, cross-correlation models, and SIRD modeling frameworks applied to pandemic dynamics and bike-sharing systems. Scientific awards and honors are not explicitly mentioned in the provided texts. Giancarlo Manzi has taught at the University of Verona, Catholic University of the Sacred Heart, and Universidad Carlos III in Madrid, maintaining strong ties with Italy's academic institutions.
Prof. Dr. Matthias Meiners is a faculty member at Justus-Liebig-University Giessen, holding the Chair of Stochastics within the Mathematical Institute. His research focuses on Asymptotic statistics Branching processes Limit theorems Random walks in random environments Regenerative processes Renewal theory Statistical mechanics . Recent publications analyze complex stochastic phenomena across branching processes, random walks, and renewal equations, with 2025 preprints on CMJ explosions and Markov renewal expansions. 2024 works examine active Brownian particle dynamics and supercritical branching fluctuations. Earlier studies address kinetic equations, martingale convergence, and percolation model speeds. Contact: Email: Matthias.Meiners@math.uni-giessen.de Phone: 0641 99-32100 Office: Room 216, Arndtstr. 2, 35392 Gießen
Mogens Bladt is a Professor of Applied Probability and Insurance Mathematics at the Department of Mathematical Sciences, University of Copenhagen. He has held positions since 2018 after 24 years as Principal Researcher at the National University of Mexico (1994-2018), with visiting professorships at Technical University of Denmark and University of Copenhagen since 2001. Research: Focuses on time-inhomogeneous phase-type distributions, matrix-oriented life insurance models, heavy-tailed distributions, and diffusion bridge simulation Teaching: Offers graduate/undergraduate courses in Applied Probability, Stochastic Processes, Risk Theory, and Numerical Analysis Scientific Contributions: Developed R packages for Markov jump processes, phase-type distributions, and diffusion bridges. Holds grants from Mexico and Denmark, including Danish Research Council funding (2007–2008). Supervised 5 PhD, 8 Master’s, and 13 Bachelor’s theses Organized academic workshops and served as Associate Editor for Stochastic Models since 1997
Professor Paul White is a distinguished academic at the University of the West of England (UWE Bristol), serving as Professor of Applied Statistics within the Faculty of Engineering and Technology's Department of Engineering, Design and Mathematics. His work focuses on applying statistical methods to benefit society (ASBOS), with particular emphasis on collaborative research across UWE's applied and life sciences departments and with the National Health Service (NHS). Dr. White holds a BSc, MSc, and PhD, though specific institutions are not mentioned in the provided text. His academic journey has established him as a leading figure in applied statistics education and research methodology. Professor White's research spans multiple domains of applied statistics, with particular expertise in quantitative research methods, research methodology, sample size determination, and the design and analysis of experiments. He maintains a strong ethical focus in quantitative research while applying his skills to medical statistics and multivariate statistics. His work often intersects with healthcare applications, as evidenced by his involvement in clinical trials and medical research collaborations. A notable initiative he co-founded is the DARK ARTS (Design And Research Knowledge for Analysis of Randomised Trials) group, which focuses on advancing methodologies for randomized trials. His extensive publication record shows a clear trend toward interdisciplinary collaboration, particularly in healthcare applications of statistics. The most recent articles demonstrate expertise in clinical trials methodology, medical statistics, and psychological interventions. Many publications involve randomized controlled trials across diverse areas including eating disorders, body image interventions, respiratory medicine, oncology, and obstetrics. This reflects his commitment to 'Applying Statistics for the Benefit of Society' (ASBOS) through rigorous quantitative methods. Professor White is actively involved in mentoring students and colleagues, describing himself as 'a willing coach or mentor.' His teaching portfolio is diverse, including courses on 'GANSTA's' (Good At Numbers and STAts) for mathematics students, quantitative methods for healthcare programs, and professional development courses through the UWE Graduate School. He expresses particular interest in developing new statistical techniques using stochastic simulation and welcomes potential PhD students to collaborate on these research programs. Among his collaborative efforts, the DARK ARTS initiative stands out as a significant research group focused on randomized trials methodology. This team, which includes Dr. Caterina Gentili, Dr. Jason Anquandah, and Dr. Deirdre Toher, represents a concentrated effort to advance statistical approaches to clinical research design and analysis.
Nina Lazarevic is a Research Fellow at the Australian National University's Centre of Epidemiology for Policy and Practice within the National Centre for Epidemiology and Population Health. She holds a PhD in environmental epidemiology and biostatistics, along with Masters degrees in biostatistics and environmental science. Prior to her academic career, she worked as an econometrician in banking and market research. Research Fellow, Centre of Epidemiology for Policy and Practice PhD in Environmental Epidemiology and Biostatistics Masters in Biostatistics and Environmental Science Her research focuses on statistical methods in environmental epidemiology, particularly Bayesian nonparametric approaches for analyzing health effects of environmental chemical mixtures and exposome-health associations. Key projects include studies on PFAS exposure impacts on maternal and child health, telehealth policy evaluation, and asbestos-related cancer incidence in the ACT. Nina has published extensively on environmental chemical exposure impacts, including PFAS contamination studies (2023-2024) and prenatal exposure effects on fetal growth (2022). Her methodological work includes simulation studies for chemical mixture analysis (2020). Current projects involve: ACT Asbestos Health Study II: Linked Data Project ANU Telehealth in Primary Care Study PFAS Health Study Her work leverages large-scale linked data to inform government policy on cardiovascular disease prevention and chronic disease management.
Kyle Paradis is a Lecturer in Sport Sociology/Sport Psychology (Mental Health) at the School of Sport, Faculty of Life & Health Sciences, Ulster University, Jordanstown Campus. His work bridges sport psychology, mental health, and physical activity research, with a focus on longitudinal behavioral studies and depressive disorder correlations. Education: PhD in Psychology (Western University, 2014) Master of Education: (2023) Master of Human Kinetics: (University of Windsor, 2010) Bachelor of Arts (Honours): (Laurentian University, 2008) Research interests include: Sport Psychology Mental Health in Sports Contexts Group Dynamics Longitudinal Behavioral Analysis Physical Literacy Adolescent Movement Behavior Depressive Disorders His recent publications emphasize longitudinal studies of organizational stressors, physical activity's role in mitigating depression, and group dynamics in sports officiating. He actively participates in international conferences and serves on program committees for sport psychology events.
