Erica M. Porter is an Assistant Professor of Statistics at the School of Mathematical and Statistical Sciences (SMSS), Clemson University. Her academic journey includes a Ph.D. in Statistics from Virginia Tech (2023), advised by Dr. Chris Franck and Dr. Marco Ferreira. Education: Ph.D. in Statistics (2023), Virginia Tech Her research focuses on Spatial Statistics , Bayesian Model Selection , and computational methods for hierarchical models. She specializes in intrinsic conditional autoregressive (ICAR) priors and data augmentation techniques for spatial and pooled data applications. Recent publications highlight her work in cost-penalized Bayesian modeling, scalable computation for spatial hierarchies, and applications in medical diagnostics (e.g., heart disease diagnosis). Key keywords include Bayesian inference, spatial modeling, computational efficiency, and statistical software development.
Sudipto Banerjee is a Professor and Chair of the Department of Biostatistics at the University of California, Los Angeles (UCLA) Fielding School of Public Health , with secondary appointments in the Department of Statistics & Data Science and the UCLA Institute of the Environment & Sustainability. He is a Senior Associate Dean for Academic Programs at UCLA Fielding School of Public Health. Education: PhD in Statistics (University of Connecticut, 2000), M.STAT (Indian Statistical Institute, 1996), BS (Honours) in Environmental Science (University of Calcutta, 1194). Research Interests: Dr. Banerjee specializes in Bayesian hierarchical modeling , spatial and spatiotemporal statistics , and scalable Gaussian process models for big data. His work bridges spatial data science with public health, focusing on environmental exposures and their health impacts, such as through the Deepwater Horizon (GuLF Study) and Aliso Canyon gas leak projects. He develops computational algorithms for Bayesian inference, including predictive processes , nearest-neighbor Gaussian processes , and meta-kriging , enabling efficient analysis of massive spatial datasets. Recent Publications emphasize methods for nonstationary spatial covariance, scalable multivariate modeling, and applications in environmental epidemiology. His theoretical contributions include advancing probabilistic frameworks for spatial gradients (wombling) and integrating mechanistic models with machine learning for uncertainty quantification. Honors and Awards: Recipient of the Jerome Sacks Cross-Disciplinary Award (2024) , George Snedecor Award (2019) , ASA and IMS Fellowships , and the Mortimer Spiegelman Award (2011) . He served as President of the International Society for Bayesian Analysis (2022) . Leadership and Grants: Principal Investigator for over 14 NIH and NSF grants, advancing spatial-temporal methodology and its application to public health. He oversees data analysis for the Aliso Canyon gas leak study and has led exposure assessments in the Deepwater Horizon oil spill projects.
Bertil Wegmann is a Lecturer at the Department of Computer Science (IDA) , Linköping University. He is affiliated with the Statistics and Machine Learning (STIMA) division, where he contributes to research and education in modern data analysis.
Gaby Schneider is an Adjunct Professor at the Institut für Mathematik within the Fachbereich Informatik und Mathematik at Goethe-Universität Frankfurt am Main. Her research combines theoretical statistical analysis of point processes, stochastic modeling of neuronal firing patterns, and advanced correlation analysis techniques. Institution: Goethe-Universität Frankfurt am Main Department: Institute of Mathematics Research Focus: Temporal coordination in spike trains, change point detection, neuronal synchronization Key methodological contributions include multi-scale change point analysis, Cox process modeling for correlations, and stochastic descriptions of neuronal bursts/oscillations. Her work bridges mathematical statistics with neuroscience applications. Recent publications focus on bivariate change point detection in movement data (2024), spiking delay modeling (2020), multi-scale peak detection (2020), and hierarchical models for bistable perception (2017). Notable earlier work (2012) explored K-ATP channel effects on dopamine neuron firing. Contact: schneider@math.uni-frankfurt.de | Office: Robert-Mayer-Str. 10, Frankfurt | Phone: +49 69 798 23927
Professor Thijs Dekker is a faculty member in the Faculty of Environment at the University of Leeds , where he holds the Professor of Transport Economics position. He previously served as Associate Professor (2020-2024) and Lecturer in Transport Economics (2014-2020) at the Institute for Transport Studies. PhD in Economics, VU University Amsterdam (2012) MSc in Economics, Erasmus University Rotterdam (with highest honour, 2006) BSc in Economics, Erasmus University Rotterdam (2005) His research focuses on empirical analysis of travel behaviour and non-market valuation , with emphasis on discrete choice models , Bayesian econometrics , and Participatory Value Evaluation (PVE) . He develops statistical frameworks for preference heterogeneity and welfare measurement in transport contexts, and has pioneered PVE as an alternative to traditional cost-benefit analysis. Recent publications explore transport decarbonization , choice model robustness , and value of travel time across freight, rail-air intermodality, and shared mobility services. Methodological contributions include computational gradients for choice modelling and validity standards for experimental design. Scientific roles include: Elected Regular Board member, International Association for Travel Behaviour Research (2019-2023) Editorial advisory board member, Journal of Choice Modelling Editorial advisory board member, Transportation Research Part C: Emerging Technologies As Director of Postgraduate Research Studies, he supervises PhD researchers including Phil Churchman, Abdul Muti Sazali, and Robby Yudo Purnomo. His applied work includes UK national VTT studies, Dutch policy appraisals, and World Bank freight analysis. He contributes to the Choice Modelling research group and participates in projects like DRYvER (biodiversity in river networks) and VAAR (rail accessibility appraisal). Current responsibilities include leading the MSc Transport Economics program and developing robust transport valuation frameworks.
Chun-Che Wen, PhD, is a Researcher at The Dartmouth Institute for Health Policy & Clinical Practice within the Geisel School of Medicine at Dartmouth College. His work focuses on advanced statistical methodologies for public health applications. PhD in Biostatistics from Medical University of South Carolina Specializes in Bayesian hierarchical modeling for complex data structures, including longitudinal, clustered, and spatiotemporal datasets. His research examines temporal changes in pharmacological intervention effectiveness and healthcare disparities, particularly in maternal health during public health crises. In his free time, he enjoys sports such as volleyball, running, and climbing.
Feng Guo is a Professor of Statistics and Patricia Caldwell Faculty Fellow at Virginia Tech's College of Science, with a joint appointment as Lead Data Scientist at the Virginia Tech Transportation Institute. His research focuses on transportation safety, naturalistic driving studies, and statistical methodology development. He holds dual Ph.D.s in Statistics and Transportation Engineering from the University of Connecticut (2007/2010), as well as M.S. and B.S. degrees from Tongji University. His work examines crash risk factors using large-scale naturalistic driving datasets, with notable contributions to understanding cellphone distraction impacts, driver aging, and automated vehicle safety. He leads statistical methodologies in transportation safety analysis, including Bayesian hierarchical models and causal inference frameworks. His research has been widely cited in media outlets like CBS, NBC, and NPR. Professional service includes chairing the ASA Transportation Statistics Interest Group and serving on Transportation Research Board committees (ABJ80, ANB20). Honors include the Taylor Technical Talent Award (2015) and Gottfried Noether Award (2004). His lab integrates computational statistics, spatial analysis, and machine learning to address complex transportation safety challenges. Key contributions include analyzing over 70 million miles of naturalistic driving data from the SHRP2 study, developing risk assessment models for automated vehicles, and evaluating driver fitness metrics for older populations. Current work focuses on smartphone-based safety services, real-time risk prediction, and AI applications in transportation safety.
Dr. Vianney Sicard is a Researcher in the Department of Ecological Modelling at the Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany, leading the Ecological Epidemiology research group. He holds a PhD in Epidemiological Modelling and Artificial Intelligence (2019-2022) from INRAE, Nantes, and prior engineering degrees in computer science (2018, École Polytechnique de l'Université de Tours) and software design (2012). His research focuses on agent-based modelling, ecological epidemiology, and computational epidemiology, including applications to livestock disease dynamics and multi-level simulation frameworks. Key contributions include the EMULSION modelling framework and work on Bayesian networks for veterinary diagnostics. Sicard's interdisciplinary work bridges computer science, environmental science, and public health, with publications in journals like PLoS Computational Biology and PAAMS. He collaborates with teams like FORMIND and EcoEpi, addressing challenges in sustainable systems and policy design under global change.
Dr. Xuan Huy Nguyen is an award-winning Associate Professor in Marketing at the University of Sussex Business School, specializing in consumer behavior, brand management, and global marketing strategies. His research integrates quantitative methodologies like choice modeling and Bayesian statistics, with a focus on industries such as sports and automobiles. He holds a PhD in Marketing from the University of New South Wales and serves as an External Examiner at the University of East Anglia. Dr. Nguyen is a Fellow of the Higher Education Academy and advises Harvard Business Review's Advisory Council. His teaching innovations, including cross-disciplinary approaches and inclusive education strategies, earned him the 2023 Teaching to Disrupt Award. He has also been a finalist in multiple Sussex Education Award categories (World Readiness, Inclusive Sussex, Better World) and contributed to global sustainability goals like Climate Action and Responsible Consumption. His academic journey spans roles from Lecturer (2017) to Senior Lecturer (2024) before his current position. Research interests include entrepreneurial passion’s impact on innovation, consumer decision-making processes, and brand strategy in dynamic markets. He actively publishes case studies on automotive and beverage industries, alongside presenting at leading conferences such as INFORMS and the European Marketing Academy. Award highlights include the World Readiness Award finalist status (2023) and sustained recognition for bridging academic rigor with real-world relevance. His multilingual proficiency (English, Japanese, Vietnamese) and international experience inform his globally oriented teaching modules.
Marta Nai Ruscone is an Associate Professor in the Department of Economics at the University of Genoa. She specializes in statistical methodologies, particularly focusing on copula models, data analysis, and their applications in environmental economics and econometrics. Her teaching responsibilities include courses such as Advanced Data Analytics, Statistical Forecasting, and Statistics for Business Economics. Her research emphasizes developing and applying copula-based techniques for clustering, dependence analysis, and modeling complex economic and environmental datasets. Key areas include studying the influence of economic sectors on financial markets, analyzing the dynamics of human development indicators, and exploring relationships between environmental factors and pandemic outcomes. She has also contributed to open-source statistical software like the R package OBsMD for Bayesian model discrimination. Dr. Nai Ruscone’s work bridges theoretical statistical advancements with practical applications, addressing challenges in multivariate analysis, ordinal data modeling, and interdisciplinary problem-solving. Her publications reflect a strong commitment to advancing methodologies in statistics and their relevance to real-world economic and environmental issues.
Katrien Antonio is a **full professor** in actuarial science and insurance analytics at **KU Leuven** and holds a **part-time professorship** in actuarial data science at the University of Amsterdam. She leads the **Insurance Research Group** and chairs the **Department of Accountancy, Finance and Insurance** within the **Faculty of Economics and Business (FEB)**. Her roles include directing the LRISK research center and overseeing education commissions at KU Leuven’s Leuven and Kortrijk campuses. **Research Interests**: Insurance analytics, data science, predictive modeling in insurance, actuarial science, and sustainable finance. Key projects include designing inclusive insurance products, compliant actuarial models, and risk analytics for societal impact. **Publications**: Her recent work focuses on environmental impacts on mortality, neural networks for insurance pricing, fraud detection, and IoT-driven maintenance risk assessment. She has published in top journals like *Journal of the Royal Statistical Society*, *North American Actuarial Journal*, and *European Journal of Operational Research*. **Education**: PhD in Mathematics (KU Leuven, 2007), with prior degrees in Mathematics from the same institution. Extensive teaching experience across higher education, emphasizing data-driven insights and innovative materials. **Grants & Projects**: Leads initiatives such as the “Actuaries and STatisticians” project (2022–2025) and “VALERIA” (2021–2024), focusing on emerging risks and sustainable finance. **Labs & Teams**: Directs the LRISK center, fostering interdisciplinary collaboration in risk analysis. Active in academic networks, including IMAC and the Faculty Board of Economics and Business.
Marios Chryssanthopoulos is a Professor of Structural Systems at the University of Surrey, affiliated with the School of Sustainability, Civil and Environmental Engineering. His academic journey includes a BSc from the University of Newcastle, an MS from MIT, and a PhD from Imperial College London. He has held roles such as Director of the Engineering Materials and Structures Research Centre and Head of the Division of Civil, Chemical and Environmental Engineering. His research focuses on structural reliability, risk-based asset management, and probabilistic modeling in infrastructure systems. Research interests include the influence of uncertainties in structural performance, corrosion effects on metallic structures, and integration of structural health monitoring (SHM) with decision-support tools. He has supervised over 25 PhD students and collaborated with industry on projects like fatigue life prediction of bridges and offshore structures. Funding sources include EPSRC, the EU, and industry partners. Teaching responsibilities include courses on structural safety, bridge management, and advanced composites. His work extends to editorial roles for journals like Structural Safety and participation in national codification committees (e.g., UK SCOSS, Eurocode 3 Drafting Panel). Key contributions include methodologies for corrosion detection in tubular structures, probabilistic frameworks for fatigue assessment, and frameworks enhancing building robustness through segmentation. His experimental work spans structural dynamics, material degradation, and consequence analysis of failures.
Dr. Kasia Bijak is a Lecturer in Management Science at the Southampton Business School, University of Southampton. Her research focuses on applying statistical methods (including Bayesian inference), data mining, artificial intelligence, stochastic processes, and econometrics to address challenges in credit scoring and healthcare analytics. She has contributed to dynamic consumer risk modeling, affordability assessment, and LGD model validation. Education: MSc in Quantitative Methods and Information Systems, Warsaw School of Economics (2004) PhD in Management Science, University of Southampton (2013) Her research explores dynamic modeling techniques such as Kalman filters and random effects in consumer credit risk assessment. She has also applied scoring methodologies in intensive care and cancer care services optimization. Teaching includes modules on business analytics and SAS programming. Research Highlights: Development of Bayesian models for car lease fraud detection Evaluation of classification tools in healthcare patient risk stratification Quantitative analysis of university research impact frameworks No scientific awards listed. Current role includes supervision of PhD candidates (details pending) and active engagement in operational research applications across financial and healthcare sectors.
Anqi Wu is an Assistant Professor at the School of Computational Science and Engineering (CSE) within the College of Computing at Georgia Institute of Technology. She previously held a postdoctoral fellowship at Columbia University’s Zuckerman Mind Brain Behavior Institute and earned her Ph.D. in Computational and Quantitative Neuroscience from Princeton University, with a graduate certificate in Statistics and Machine Learning. Her research focuses on developing probabilistic modeling approaches and scalable inference algorithms for neural and behavioral analysis, with applications in robotics and real-world problems. Education: Ph.D., Computational and Quantitative Neuroscience, Princeton University (2014–2019) Graduate Certificate in Statistics and Machine Learning, Princeton University Postdoctoral Fellow, Center for Theoretical Neuroscience, Columbia University (2019–2022) Research Interests: Probabilistic Modeling for Neural Latent Discovery: Developing disentangled generative models to extract interpretable neural representations. Behavior Analysis: Extracting multi-layered information from animal/human behavior via hierarchical models and reinforcement learning. Efficient Inference: Advancing Bayesian neural networks, Gaussian processes, and scalable algorithms. Her work bridges machine learning, neuroscience, and robotics, with applications in brain decoding, motor control, and reward learning. Publications Trends: Recent work emphasizes reinforcement learning (e.g., EmoBipedNav for emotion-aware robotics), generative models (diffusion models for neural dynamics), and Bayesian methods (Gaussian processes for multi-region brain analysis). Applications span robotics, neuroscience, and spatio-temporal data modeling. Affiliations: Director, BRAINML Lab (BRAIN Intelligence and Machine Learning) Affiliated with Machine Learning Center, Neuro @ GT, and Bioengineering at Georgia Tech
Martin G.T.A. Rutten is a University Researcher in the Department of Biomedical Engineering at Eindhoven University of Technology (TU/e), specializing in Biomedical Materials and Chemistry. His research focuses on developing advanced biomaterials, particularly hydrogels with tunable properties for biomedical applications. Rutten's research interests span multiple domains within biomaterials science and engineering. He specializes in hydrogel engineering , focusing on supramolecular chemistry approaches to create dynamic biomaterials with precisely controlled physical properties. His work integrates principles from polymer science, materials chemistry, and tissue engineering to develop synthetic matrices that mimic natural biological environments. Rutten investigates how molecular design influences macroscopic material properties, particularly examining hydrogen bonding interactions, self-assembly processes, and the relationship between biochemical complexity and cellular response. Analysis of Rutten's recent publication record (2018-2025) reveals a strong focus on supramolecular biomaterials, particularly hydrogels with dynamic properties. His research demonstrates an evolving trajectory from fundamental material characterization toward increasingly complex biological applications, including kidney organoid development and cardiac tissue engineering. The work consistently emphasizes the engineering of material properties at the molecular level to achieve specific biological outcomes, with growing attention to clinical translation potential.
