Dr. Benoit Liquet is Professor of Probability and Statistics at the University of Pau and Pays de l'Adour, affiliated with the Laboratory of Mathematics and their Applications (LMAP). He holds a PhD in Biostatistics and HDR from Bordeaux University and serves as Honorary Professor at Macquarie University. His research develops statistical methods for: High-dimensional and omics data analysis Bayesian variable selection Multi-state and survival models Dimension reduction techniques Clinical and epidemiological applications He maintains active collaborations with ACEMS (Australia) and Imperial College London. Recent methodological work includes GPU-accelerated Bayesian computation and sparse partial least squares approaches for genomics. Administratively, he serves on committees for the French Biometrics Society and coordinates statistical pedagogy initiatives.
Philippe Bernardoff is an Associate Professor at the University of Pau and the Pays de l'Adour. His research focuses on advanced probabilistic models, including multivariate gamma distributions, negative multinomial laws, and Laplace transform applications in statistical dependence structures. His work bridges theoretical probability and practical applications in areas like polarimetric image processing. Bernardoff's research interests center on developing statistical methodologies for complex multivariate systems, with emphases on distribution theory, simulation algorithms, and copula-based dependence modeling. His publications consistently explore the mathematical frontiers of infinitely divisible distributions and their computational implementations. His recent articles demonstrate a strong focus on advancing simulation techniques for gamma distributions and expanding the theoretical understanding of Laplace transforms in multivariate contexts. This work has implications for high-dimensional data analysis and stochastic modeling.
Guangjian Zhang is an Associate Professor at the University of Notre Dame, affiliated with the Psychometrics and Dynamic Analysis (PDA) Lab. His primary research focuses on developing and improving quantitative methods in the behavioral and social sciences, particularly in factor analysis, structural equation modeling, time series analysis, and longitudinal data analysis. He holds a Ph.D. in Psychology from Ohio State University (2006) and an M.S. in Statistics from the same institution (2004), along with an M.Ed. in Psychology from Beijing University (1999) and a B. Med. from Tianjin Medical University (1994). Education: Ph.D. in Psychology, Ohio State University, 2006 M.S. in Statistics, Ohio State University, 2004 M.Ed. in Psychology, Beijing University, 1999 B. Med., Tianjin Medical University, 1994 His research interests emphasize enhancing measurement tools like questionnaires, with a focus on methodological advancements in psychometrics. His work spans dynamic factor analysis, multivariate time series, and computational efficiency in statistical methods. Notable contributions include the development of the EFAutilities package and bootstrap procedures for factor analysis validation. He teaches courses on statistics and advanced methodology in behavioral and social sciences. Dr. Zhang has published extensively on topics such as factor rotation, bootstrap standard errors, and longitudinal modeling. His research addresses challenges in psychological dynamics, clinical outcomes, and statistical methodologies, reflecting a commitment to bridging theoretical and applied quantitative psychology.
Yuri Goegebeur is an Associate Professor in the Department of Mathematics and Computer Science at the University of Southern Denmark, affiliated with the SDU Climate Cluster. His research focuses on extreme value theory, statistical modeling, and actuarial science, particularly in areas like reinsurance pricing, tail risk analysis, and censoring data methods. He has contributed extensively to methodologies for estimating extreme quantiles, tail dependence functions, and risk measures under various conditions. His work integrates advanced statistical techniques with applications in finance, insurance, and environmental risk assessment. Key research interests include extreme value analysis, robust estimation techniques, and the development of risk measures for time series and censored datasets. He has published over 80 articles in peer-reviewed journals and serves on editorial boards for journals like Extremes and Psychometrika . His collaborative projects involve analyzing extreme weather impacts on healthcare systems and advancing multivariate regression models for extreme values. Recent publications emphasize conditional tail moment estimation, dependent risk measures, and applications in reinsurance. His research bridges theoretical statistics with practical challenges in actuarial science and environmental modeling. He has co-led projects funded by institutions like Villum Fonden, focusing on extreme value methodologies for real-world problems such as climate-related health risks.
Pujee Tuvaandorj is an Assistant Professor in the Department of Economics at York University, part of the Faculty of Liberal Arts & Professional Studies. His research focuses on econometric methods, particularly robust inference under weak identification and randomization techniques. He holds a Ph.D. from McGill University (2015), an M.A. from Hitotsubashi University (2009), and a B.A. from Kyoto University (2007). His work bridges theoretical econometrics and applied microeconometric models. Research interests span econometric theory, including permutation tests for linear models, instrumental variables analysis, and robust statistical methods. He also explores structural breaks, time series econometrics, and asymptotic theory. His recent work emphasizes methodological advancements in handling weak identification and serial dependence in econometric models. Teaching responsibilities include courses such as Introductory Statistics for Economists II , Financial Econometrics , and Econometric Theory . His publications in top journals like Quantitative Economics and Journal of Econometrics reflect his expertise in econometric inference and model development. No scientific awards are explicitly mentioned in the provided texts. His research portfolio includes contributions to regression discontinuity designs, generalized method of moments (GMM), and invariant tests. Current projects address digital adoption, cyber security, and labor market impacts on homelessness, leveraging Canadian administrative data.
Prof. Fionn Murtagh is a leading academic in data science and computer science, currently serving as Professor of Data Science at the University of Huddersfield (2017–present). He has held prominent roles including Head of the School of Computer Science and Informatics at De Montfort University (2013–2014), Head of the Information Communications and Emergent Technologies Directorate at Science Foundation Ireland (2007–2012), and Full Professor at Royal Holloway, University of London (2004–2013). He holds an Honorary Professorship in Computer Science at Royal Holloway (2013–present) and maintains part-time roles as Professor of Data Science at the University of Derby (2015–2017) and Visiting Professor at Goldsmiths University of London (2017–). His education includes a BA in Mathematics and BAI in Engineering Science from Trinity College Dublin (1976), an MSc in Computer Science from Trinity College (1979), a PhD in Mathematical Statistics from Université Paris 6 (1981), and an Habilitation à Diriger des Recherches in Astronomy from Université Louis Pasteur (1993). He is fluent in English, French, German, and Irish, with reading knowledge of Italian. Research interests focus on data analysis, pattern recognition, astronomical imaging, and multivariate statistical methods. His work emphasizes computational efficiency and applications in high-dimensional datasets. Notable contributions include pioneering work in clustering algorithms, image processing, and the application of mathematical-statistical methods in astronomy and computer science. He has received prestigious awards such as Fellowship from the International Association for Pattern Recognition (2008), Royal Irish Academy membership (2003), and Fellowships from the British Computer Society, Royal Statistical Society, and Royal Society of Arts. His leadership roles include Editor-in-Chief of The Computer Journal (2000–2007) and President of the Classification Society of North America (2008–2009).
Angela Agostiano is a Professor of Physical Chemistry at the Department of Chemistry, University of Bari , and serves as Head of the Bari division of the CNR-IPCF . Her research spans nanostructured materials , photochemical processes , and biomaterials . President Elected of the Italian Society of Chemistry Past Delegate of the Rector for postdoctoral policy Former Member of Academic Board of the University of Bari Her work focuses on nanosized semiconductors for environmental and sensing applications, photosynthetic systems , and biomaterials in energy transduction. She has authored over 220 publications with an H-index of 31. Her recent articles explore flexible nanocrystal solar cells , ionic liquid interactions , multivariate food analysis , and biofunctionalized heterostructures , demonstrating interdisciplinary applications in chemistry and biophysics. President Elected of SCI (2015) Delegate of Rector for doctoral policy (2015) Member of INSTM Scientific Board (2009)
Henk Kiers is a Professor in the Department of Psychometrics and Statistics at the University of Groningen's Faculty of Behavioural and Social Sciences. His work contributes to UN Sustainable Development Goals through advanced statistical methodologies. He holds a prominent academic role with extensive contributions to multivariate statistics and computational methods. Research focuses on three-way component analysis, principal component analysis, and matrix mathematics applications. Notable contributions include developing R packages for statistical analysis and advancing techniques like Simplimax rotation. His work bridges theoretical statistics with practical applications in fields like metabolomics and chemometrics. Awards: Psychometrika Reviewer Award (2017). Supervision: Advised 10 PhD students, including work on replication success metrics and Bayesian statistics. Collaborations: Visiting researcher at institutions such as Sapienza University of Rome (2016) and Rovira i Virgili University (2018). Labs/Teams: Involved in interdisciplinary projects integrating statistical methods with behavioral and health sciences through datasets shared on platforms like Figshare.
Alexandra Dias is a Professor of Finance and Actuarial Science at the University of York, affiliated with the School for Business and Society and the Department of Accounting and Finance. She previously held roles at the University of Leicester and University of Warwick. She earned her PhD from ETH Zurich, with additional degrees from Universidade de Lisboa and Universidade Nova de Lisboa. Roles: Co-programme Leader for BSc Actuarial Science, Fellow of Advance HE, Member of the Pensions Gap Working Party (Institute and Faculty of Actuaries). Educations: PhD in Finance from ETH Zurich, MSc from Universidade de Lisboa, Licenciatura from Universidade Nova de Lisboa. Her research focuses on quantitative risk management, insurance finance, copula models for multivariate dependence, and extreme events analysis. Recent work includes studies on pension gaps, reinsurance impacts, and digital accessibility in academia. She has contributed to journals like Risks, Journal of Banking and Finance, and Quantitative Finance . Key articles explore topics such as copula-based risk aggregation (2025), pensions policy (2024), and stop-loss reinsurance effects (2022). Her work bridges theoretical finance with practical applications in insurance and policy. Awards: Fellow of Advance HE. Grants/Advising: Editor for European Journal of Finance , guest editor for Risks , and active in interdisciplinary research collaborations. Labs/Teams: Part of the York Management School’s actuarial science and finance research groups.
Yabo Niu is an Assistant Professor and Presidential Frontier Faculty in the Department of Mathematics at the University of Houston, with a joint appointment in the Department of Health Systems & Population Health Sciences within the Tilman J. Fertitta Family College of Medicine. They hold a Ph.D. in Statistics from Texas A&M University (2019) and a B.S. in Statistics from Nankai University, China (2013). Their research focuses on Bayesian statistical methods, including graphical models, nonparametric approaches, variable selection, and tree-based regression/classification techniques. Recent contributions include the 2024 JASA Reproducibility Award-winning paper on covariate-assisted Bayesian graph learning for heterogeneous data. Dr. Niu has developed novel methodologies in network modeling and hybrid estimation techniques (e.g., EPSOM-Hyb). Their work bridges statistical theory with applications in health systems, neuroscience, and genomics. In 2024, they introduced a new Bayesian Statistics course (MATH 6397) at the University of Houston. Education: Ph.D. in Statistics, Texas A&M University (2014–2019) B.S. in Statistics, Nankai University (2009–2013) Research Highlights: Developed robust high-dimensional network modeling frameworks Advanced Bayesian approaches for heterogeneous data integration Investigated astrocyte structural plasticity using machine learning Teaching: Bayesian Statistics course (Spring 2024) Current research emphasizes integrating omics data and advancing reproducible statistical practices. Dr. Niu’s group includes postdoctoral researchers like Ji Shi (jointly supervised with Demetrio Labate and Peng Zhao).
Gianluca Fusai is a Professor of Mathematical Finance at Bayes Business School, University of London. He also holds a Full Professorship at Università del Piemonte Orientale. His research focuses on Financial Engineering, Numerical Methods for Finance, and Energy Markets. He has authored/co-authored over 50 publications, including the textbook 'Implementing Models in Quantitative Finance' (Springer). Fusai has extensive consultancy experience with institutions like BNP Paribas and has served on editorial boards of journals such as Quantitative Finance and Mathematical Finance . Education: BSc in Economics, Bocconi University MSc in Statistics and Operational Research, University of Essex PhD in Finance, Warwick Business School Research Interests: His work spans Credit Risk, Commodity Derivatives, and advanced numerical techniques for pricing exotic options. He has developed methods for efficiently valuing Asian options, swaptions, and basket options under non-Gaussian models. Recent contributions address calibration risk and systemic risk in financial markets. Professional Activities: Organizer of the Financial Engineering Workshops at Bayes Business School Referee for journals including Journal of Banking and Finance and European Journal of Operational Research Consultant for public and private sector institutions Advising & Grants: Supervised multiple PhD students in topics like swaption pricing and counterparty credit risk. Active in grant-related research on systemic risk and quantitative finance applications. Labs/Teams: Leads research initiatives in computational finance and risk management at Bayes Business School, collaborating with institutions globally.
Malvina Marchese is an Associate Professor in Data Science for Finance at Bayes Business School, part of City, University of London. She serves as Academic Director of the Finance Cluster degrees and holds a PhD in Econometrics from the London School of Economics (LSE). Previously, she was Head of Risk Management at Shell Oil in Italy and has extensive industry experience in quantitative risk management since 2008. She currently advises Maersk Brokers on shipping econometrics and CBRE Investment on real estate forecasting. Her research focuses on econometric methodologies applied to commodity markets, multivariate GARCH models, long memory volatility analysis, and quantile regression. Key areas include energy markets, shipping economics, and financial stability analysis. Her work bridges academic theory with practical applications in risk management and policy. Publications span topics like green investment strategies in shipping, price premiums for eco-friendly vessels, and volatility modeling in energy and financial markets. She contributes to journals such as Transportation Research Part D , Energy Economics , and Journal of Banking & Finance . Malvina is a Non-Executive Director at Timberlake Consultants and maintains consultancy roles in the energy and real estate sectors. She is fluent in English, French, and Italian, with expertise in peer review and academic leadership.
Pankaj Choudhary is a Professor of Statistics in the Department of Mathematical Sciences at the University of Texas at Dallas, where he also serves as the Associate Dean of Graduate Studies for the School of Natural Sciences and Mathematics since 2021. Previously, he was Associate Head of the Department of Mathematical Sciences from 2015-2021. He has been a faculty member at UTD since 2002, progressing from Assistant Professor (2002-2008) to Associate Professor (2008-2017) and finally to Professor (2017-present). His educational background includes a Ph.D. in Statistics from Ohio State University (2002), an M.S. in Statistics from the Indian Institute of Technology, Kanpur (1998), and a B.S. in Statistics from the University of Delhi (1996). Dr. Choudhary's research focuses on Biostatistics, Statistical Methodology, Method Comparison Studies, Risk Prediction, and Proteomics . His work bridges theoretical statistics with practical applications in healthcare and biomedical research. He has developed specialized statistical methods for agreement assessment in method comparison studies, which have become influential in clinical and laboratory settings where multiple measurement techniques need validation against each other. His research in risk prediction, particularly for contralateral breast cancer through the CBCRisk calculator and R package, demonstrates his commitment to translating statistical methodology into clinical decision support tools. His publication record shows a clear trajectory from foundational work in statistical methodology for method comparison studies toward increasingly sophisticated applications in biomedical research, particularly in cancer risk prediction and substance use disorder modeling. Recent work demonstrates integration of Bayesian approaches and machine learning techniques with traditional statistical methodology. Dr. Choudhary has authored two significant books in his field: "Ordered Data Analysis, Modeling and Health Research Methods" (2015) and "Measuring Agreement: Models, Methods, and Applications" (2017), the latter of which has received positive reviews in major statistical journals. He has been involved in securing research funding, including a $20,000 grant from the Actuarial Foundation (2007-2008) for work on multivariate conditional density estimation and a $100,000 grant from the American Health Assistance Foundation (2005-2007) for proteomics research related to Alzheimer's disease. Dr. Choudhary has also developed the CBCRisk calculator, an online tool for predicting personalized risk of contralateral breast cancer. As Associate Dean of Graduate Studies, he plays a significant role in shaping graduate education within the School of Natural Sciences and Mathematics. He has taught specialized short courses at major statistical conferences and summer schools, including the Joint Statistical Meetings and MESIO UPC-UB Summer School.
Nan Wu is an Assistant Professor in the Department of Mathematical Sciences at the University of Texas at Dallas (UTD). He holds a Ph.D. in Mathematics from the University of Toronto (2018) and a B.Sc. in Mathematics and Physics from the same institution (2011). His research focuses on manifold learning, exploring geometric structures of high-dimensional data and their applications in nonparametric statistics and stochastic dynamics. Key areas include nonlinear dimension reduction, data denoising, and algorithm development for manifold-based analysis. Education Ph.D. in Mathematics, University of Toronto, 2018 B.Sc. in Mathematics and Physics, University of Toronto, 2011 Research Interests Wu’s work bridges differential geometry and machine learning, emphasizing manifold learning techniques. He develops mathematical foundations for nonlinear dimension reduction algorithms (e.g., Locally Linear Embedding, Diffusion Maps) and applies these to problems in statistics, such as kernel density estimation and efficient solvers for Langevin dynamics. His geometric methods address challenges in data denoising, boundary detection, and manifold reconstruction from noisy observations. Teaching He has taught courses in probability (UTD), multivariable calculus (Duke University), and introductory calculus (University of Toronto). Publications Overview His publications span topics like spectral convergence of graph Laplacians, Gaussian process modeling on manifolds, and geometric analysis of minimal surfaces. Recent work includes boundary detection algorithms and adaptive Bayesian methods for data with low intrinsic dimensionality.
