Tero Mäkinen is a postdoctoral researcher at Aalto University's Department of Applied Physics, specializing in materials science and computational modeling. His work bridges disciplines like physics, rheology, and sustainable materials engineering. Key Affiliations: Aalto University, Complex Systems and Materials research group. His research focuses on: Mechanical properties of metallic glasses and high-entropy alloys Rheology of colloidal hydrogels and thermogelation processes Computational methods for material design and failure prediction Development of sustainable biobased foams and coatings Recent publications highlight his contributions to: Creep failure mechanisms in heterogeneous and amorphous materials Data-driven approaches for alloy phase prediction Open-source software tools for rheological analysis Green materials engineering using cellulose and lignin
Camila de Souza is an Associate Professor in the Department of Statistical and Actuarial Sciences within the Faculty of Science at Western University. She serves as Vice-director of Western Data Science Solutions (WDSS) and interim director of the Master of Data Analytics professional program, bridging advanced statistical methodology with real-world applications across healthcare, environmental science, and engineering domains. Her educational foundation includes a PhD in Statistics from the University of British Columbia, complemented by Master's and Bachelor's degrees in Statistics from Brazil's University of Campinas. This international training informs her interdisciplinary approach to complex data challenges. De Souza's research program develops cutting-edge statistical methods for analyzing large-scale complex data structures, with particular expertise in Bayesian variational inference, clustering algorithms, hierarchical mixture models, and survival analysis. Her work on hidden Markov models and nonparametric regression enables breakthroughs in fields ranging from ICU patient monitoring to astronomical data interpretation, consistently addressing methodological gaps in handling high-dimensional and heterogeneous datasets. Recent publications reveal a pronounced trend toward healthcare analytics applications, particularly in intensive care settings where her survival analysis models predict mechanical ventilation duration and patient flow optimization. Simultaneously, she extends statistical frameworks for environmental risk assessment (tornado-flood hazards) and energy systems through functional data analysis, demonstrating remarkable methodological versatility across disciplines. Her scientific recognition includes: 2014 Journal of Nonparametric Statistics Best Student Paper Award De Souza actively mentors doctoral candidates Ana Carolin Da Cruz and Chengqian Xian alongside MSc student Renan S. Barbosa, while securing research funding from natural sciences and health councils. Her supervisory approach emphasizes methodological rigor coupled with domain-specific application, preparing students for careers at the statistics-data science interface. Through WDSS, she leads a team providing statistical consulting services across Western University's research ecosystem, while shaping the Master of Data Analytics curriculum to meet industry demands for advanced modeling capabilities in an era of exponential data growth.
Jaakko Peltonen is a Visiting Professor at the Department of Computer Science within Aalto University , affiliated with the Probabilistic Machine Learning group and the Helsinki Institute for Information Technology (HIIT) . His research focuses on machine learning, information retrieval, and interactive visualization, with significant contributions to probabilistic modeling and data analysis. Tekniikan tohtori (Doctor of Technology), Helsinki University of Technology (2004) Diplomi-insinööri (Master of Science in Technology), Helsinki University of Technology (2001) His research spans machine learning applications in computational biology (e.g., analyzing cellular processes with regression planes), probabilistic matrix factorization for recommendation systems, and hierarchical Dirichlet processes for topic modeling in text mining. He has pioneered methods for exploratory search with visual interactive intent modeling and graph-based priors for scalable ML algorithms. Notable activities include international collaborations (e.g., visiting researcher roles in 2016), organizing Eurovis 2016 workshops, and conference presentations across Spain, United States, and other countries. His publications (85+ total) demonstrate expertise in probabilistic models, dimensionality reduction, and interactive visualization techniques.
Peter E. Rossi holds the James A. Collins Chair in Management as a Distinguished Professor of Marketing, Economics and Statistics at UCLA's Anderson School of Management. With extensive contributions across multiple disciplines, he has published in premier journals including Marketing Science , Journal of Marketing Research , American Economic Review , and Econometrica . Education: Ph.D. Econometrics, 1984, University of Chicago MBA Management Science, 1980, University of Chicago B.A. Mathematics and History, 1976, Oberlin College Rossi's research spans pricing and promotion, target marketing, direct marketing, limited dependent variable models, and Bayesian statistical methods. His work in target marketing anticipated developments in electronic couponing and web-based retail targeting, while his research on data-based pricing influenced contemporary analytic pricing tools. He pioneered Bayesian Hierarchical choice models, which have become the industry standard for choice and conjoint data analysis. His recent publications demonstrate continued leadership in marketing analytics and econometrics, with forthcoming work examining product competition and flexible work arrangements. Rossi's articles consistently address fundamental questions in consumer behavior and market structure while advancing methodological approaches in econometric modeling. Scientific Awards: Fellow of the American Statistical Association Fellow of the Journal of Econometrics Rossi has served as founding editor of Quantitative Marketing and Economics and held senior editorial positions at Marketing Science , Journal of the American Statistical Association , Journal of Econometrics , and Journal of Business and Economic Statistics . His influential book Bayesian Statistics and Marketing (2005) and the R package bayesm have shaped methodological approaches in the field. Previously, he founded the Kilts Center for Marketing at the University of Chicago's Booth School of Business. Outside academia, Professor Rossi is an avid fly-fisherman and pilot with instrument and commercial ratings.
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.
