Find Academic Supervisors

• Bayesian Statistics
• Uncertainty Quantification
• Statistical Theory
• +3 more

Eduard Belitser is an Associate Professor in the Department of Mathematics at the Faculty of Science, Vrije Universiteit Amsterdam. His research focuses on Bayesian statistics, uncertainty quantification, and statistical theory, contributing to the UN Sustainable Development Goals. He currently teaches courses such as Experimental Design and Data Analysis , Statistical Models , and Statistics for AI . Belitser is actively involved in academic collaborations and has presented his work at international conferences and institutions. Belitser’s research interests include Bayesian methods, robust estimation, and applications to change point analysis, inverse problems, and variable selection. He explores topics like empirical Bayes procedures, optimality in estimation, and uncertainty quantification, with a focus on mathematical foundations and practical implications in statistical modeling and data analysis. Belitser's recent publications (2022–2025) emphasize Bayesian uncertainty quantification in change point detection, inverse problems, and robust statistical estimation. His work highlights methodological advancements, such as empirical Bayes approaches for sparse quantiles and optimality principles in variable selection and multiple testing frameworks, reflecting a blend of theoretical rigor and applied data analysis. No scientific awards or honors are explicitly mentioned in the provided information. In advising, he has supervised 3 PhD theses, though student names are not listed. His professional activities include editorial roles ( Statistica Neerlandica ), visiting research at North Carolina State University (March–April 2024), and organizing the 34th European Meeting of Statisticians (July 2023). He has also delivered invited lectures on Bayesian UQ and change-point models in 2023 and 2024.

Francisco BlasquesView profile

Professor

• Econometrics
• Statistics
• Data Science
• +5 more

Francisco Blasques is a Full Professor of Econometrics and Data Science at the Vrije Universiteit Amsterdam (VU) and a Research Fellow at the Tinbergen Institute. He holds leadership roles, including Co-founder of Churned (QuantIQ) and datastuff BV, and served as TI Doctoral Director for the School of Business and Economics. His research focuses on econometrics, statistics, and data science, with expertise in time-series analysis, financial econometrics, and nonlinear dynamics. Teaching includes advanced econometrics courses at VU and Tinbergen Institute, covering topics like time-series modeling and financial econometrics. His PhD supervision spans over 15 students since 2012, focusing on econometric methods and applications. Research contributions emphasize score-driven models, volatility analysis, and spatial econometrics. Key publications address topics such as natural disaster impacts on economies, robust volatility estimation, and fusion of experimental/observational data. Awards include the NWO-VIDI Grant (2019) for methodological innovations in econometrics. Professional activities include ancillary roles as Partner at QuantiQ BV and Director at Metyis, alongside academic duties. His work contributes to UN Sustainable Development Goals through economic resilience and policy analysis.

Jim SmithView profile

Professor

• Bayesian statistics
• Decision theory
• Probabilistic graphical models
• +7 more

Jim Smith is a Professor in the Department of Statistics at the University of Warwick. He actively leads research initiatives in Bayesian statistics, decision theory, and causal modeling with applications to food security, forensic science, and public health. His work bridges theoretical advancements in graphical models with real-world implementations through collaborations with government agencies and industry partners. PhD in Statistics (University of Warwick) Research focus: Bayesian networks, chain event graphs, expert judgment elicitation, causal inference Key collaborations: Martine Barons, Manuelle Leonelli, Ann Nicholson Research Interests Smith's work centers on high-dimensional Bayesian decision analysis, causal discovery in dynamic systems, and integrating expert judgments into formal models. He has developed novel Chain Event Graphs for complex system modeling and focuses on applications in: Food security risk assessment Forensic evidence evaluation Counter-terrorism strategies Medical imaging analysis Crisis management Energy security Recent Publications Trend His recent work demonstrates increasing focus on adversarial risk analysis (2025), secure data modeling for law enforcement (2024), and methodological improvements in Bayesian networks (2023-2024). Key themes include: Polynomial regression-based moment propagation Multi-agent decision frameworks Dynamic graphical model extensions Causal analysis in security contexts Mathematical foundations of Bayesian graphs Scientific Awards Fellow of the Alan Turing Institute (2017-2024) COST European Cooperation award for expert judgment research EPSRC-funded OxWaSP PhD training consortium co-director Grants & Collaborations Currently serving as PI in 6 Alan Turing Institute projects and CI on 7 others. Major grants include: European Food Standards Agency expert elicitation funding EPSRC OxWaSP initiative National Digital Twin Programme research Home Office policy evaluation projects Labs & Teams Co-leads a university research team studying cross-domain expert coherence. Member of the Food Global Research Priority team coordinating UK food security studies. Developed software tools in R/Python for Chain Event Graph analysis.

Juannan ZhouView profile

Professor

• Quantitative Biology
• Evolutionary Biology
• Genotype-Phenotype Mapping
• +3 more

Juannan Zhou is a Professor in the Department of Biology at the University of Florida , where they recently established their research lab. Their work focuses on genotype-phenotype mapping using machine learning , mathematical modeling , and wet lab experiments to understand evolutionary dynamics. Research Lab : Department of Biology, University of Florida Contact : juannanzhou@ufl.edu Key research themes include: Mapping genotype-phenotype relationships in diploid organisms Investigating protein fitness landscapes through unsupervised language models Developing machine-learning assisted directed evolution techniques Studying complex probability distributions over biological sequence spaces Exploring interactions between biochemical constraints and evolutionary outcomes Recent publications demonstrate technical innovations in: Gaussian process priors for sequence-function modeling Field-theoretic density estimation methods Higher-order epistasis analysis Nonparametric Bayesian approaches to biological sequence space Differentiable Smith-Waterman algorithms for sequence alignment The lab actively seeks postdocs, lab technicians, and graduate students interested in machine learning applications in population genetics and evolutionary modeling . Current projects include developing gpmap-tools for genotype-phenotype map visualization and implementing empirical variance component regression frameworks.

Tobias HeilmannView profile

Lecturer

• Statistical Inference
• Change Point Detection
• Differential Privacy
• +5 more

Dr. Tobias Heilmann is a Lecturer at the Department of Humanities, Social and Political Sciences at ETH Zurich. His research focuses on statistical inference, change point detection, differential privacy, high-dimensional data analysis, network analysis, and machine learning. He holds a PhD in Statistics from Fudan University, advised by Zhiliang Ying, and has contributed to software packages such as changepoints , GMPro , and APPLE . His work bridges theoretical statistics and practical applications, emphasizing privacy-preserving techniques, dynamic systems analysis, and algorithmic robustness. Key areas include nonparametric methods, contextual bandits, federated learning, and functional data analysis. Recent contributions address challenges in distributed systems, adversarial robustness, and high-dimensional model selection. He has published extensively in top journals like Annals of Statistics , SIAM Journal on Mathematics of Data Science , and IEEE Transactions on Information Theory . Dr. Heilmann's research also involves interdisciplinary collaborations, including social psychology of groups and biomedical applications. His software tools are widely used for change point analysis and community detection in networks. Current projects explore optimal privacy-utility trade-offs and adaptive algorithms for non-stationary environments.

• Genomics
• Quantitative Genetics
• Animal Breeding
• +2 more

Michael Goddard is an Honorary Professor at the Institute for Molecular Bioscience, University of Queensland, with a distinguished research career spanning statistical genetics, genomics, and animal breeding. His work bridges agricultural applications in livestock and human complex trait genetics, contributing foundational methods for genomic prediction and genome-wide association studies. His primary research interests include: Quantitative Genetics and Genomic Prediction Genome-Wide Association Studies (GWAS) Statistical Methods for Complex Trait Analysis Animal Breeding (Cattle and Sheep Genomics) Human Genetics of Complex Diseases Recent publications (2016-2022) demonstrate sustained innovation in multi-trait genomic prediction, functional annotation of animal genomes (FAANG consortium), and cross-species genetic architecture studies. His work frequently addresses non-additive genetic effects and integrates sequence data to improve prediction accuracy, with applications ranging from livestock improvement to psychiatric disorder risk assessment.

• Non-Invasive Neurostimulation Therapies
• Neurobiology of Psychiatric Disorders
• Depression Treatment
• +3 more

Dr. Fidel Vila-Rodriguez is an Assistant Professor in the Department of Psychiatry at the University of British Columbia, affiliated with the Faculty of Medicine and the School of Biomedical Engineering. He serves as Director of the Non-Invasive Neurostimulation Therapies (NINET) Laboratory and the Schizophrenia Program. His research focuses on translational applications of non-invasive neurostimulation therapies (NINETs), particularly in severe psychiatric disorders like schizophrenia and treatment-resistant depression. The NINET Lab is uniquely equipped with simultaneous TMS-fMRI and tDCS-fMRI capabilities, enabling advanced investigation of neurobiological mechanisms and biomarkers of neuromodulation. Research interests include optimizing TMS protocols, understanding neuroplasticity mechanisms, and developing biomarkers to predict treatment responses. Key areas of exploration involve the neurobiology of psychiatric disorders and bridging clinical practice with neuroscientific findings. Dr. Vila-Rodriguez’s work emphasizes improving therapeutic outcomes for patients with refractory mental illnesses through innovative neuromodulation approaches. His recent studies highlight advancements in single-day TMS regimens, theta-burst stimulation effects on depression subtypes, and the role of sleep and neuroplasticity in treatment efficacy. As part of the ENIGMA-Neuromodulation Working Group, he collaborates globally to standardize neuromodulation research methodologies. Dr. Vila-Rodriguez’s lab integrates clinical practice with cutting-edge neuroimaging and computational tools to address unmet needs in psychiatric care, aiming to enhance personalized treatment strategies for severe mental health conditions.

• Bayesian Methodology
• Statistical Machine Learning
• Variational Bayes
• +3 more

Dr. Minh-Ngoc Tran is an Associate Professor in Business Analytics at the University of Sydney Business School and an Associate Investigator in the ARC Centre of Excellence for Mathematical and Statistical Frontiers. His research develops Bayesian and machine learning methods for complex models with big data. His methodological innovations include quantum-enhanced variational Bayes, manifold optimization techniques, and subsampling MCMC algorithms. Current research focuses on time series modeling for financial markets, quantum computing applications in statistics, and variational inference for models with intractable likelihoods. Dr. Tran teaches quantitative methods, data mining, and predictive analytics. He currently supervises PhD students in deep learning for financial time series, explainable AI, and quantum machine learning. Recent research grants support quantum computation in business analytics and deep learning-based financial forecasting. His work has been recognized through awards including the University of Sydney Business School Emerging Scholar Research Fellowship and designation as Australia's top researcher in Probability and Statistics with Applications in 2021.

Naomi MazzilliView profile

Lecturer

• Hydrogeology
• Karst Aquifers
• Groundwater Modeling
• +3 more

Naomi Mazzilli is a Lecturer and Hydrogeologist at Avignon Université, affiliated with the Research and Innovation Support Department (DARI) within the Sciences and Agrosciences laboratories. Her research focuses on the characterization and modeling of karst aquifers, particularly in the Fontaine de Vaucluse region. She specializes in groundwater dynamics, monitoring techniques, and climate change impacts on karst systems. Key projects include automated measurement stations at the Rustrel Low Noise Underground Laboratory, collaborations with INRA and the National Karst Observation Service, and tracer studies in karst networks. Mazzilli emphasizes the importance of observational data and interdisciplinary approaches in advancing hydrogeological understanding. She is also involved in educational outreach, teaching hydrogeology and mentoring students through collaborative research projects. Her work integrates hydrogeophysical methods like Surface Nuclear Magnetic Resonance (SNMR) and superconducting gravimetry to study unsaturated zone processes. Recent initiatives include developing the KarstMod modeling platform and the KarstID application for analyzing spring discharge data. These tools enhance the assessment of karst system functioning and vulnerability to anthropogenic pressures. Mazzilli’s research contributes to sustainable water resource management in karst regions, addressing challenges such as climate variability and groundwater contamination. Collaborations span international projects, including studies in Peru’s Amazon-Andes karst systems and interdisciplinary efforts with geophysicists and environmental engineers. Her contributions to open science include publications on fluorescence-based tracing and Bayesian modeling frameworks, reflecting her commitment to advancing both theoretical and applied hydrogeology.

Emmanuel VAZQUEZView profile

Professor

• Bayesian Statistics
• Computer Experiments
• Uncertainty Quantification
• +3 more

Emmanuel Vazquez is a Professor and researcher at CentraleSupélec, specializing in Bayesian design and analysis of computer experiments. He coordinates Data Science projects and leads research in Gaussian processes, uncertainty quantification, and optimization. He holds a PhD (2005) and HDR (2015) from Université Paris-Saclay, and has taught Bayesian statistics at CentraleSupélec. His work focuses on sequential design strategies, multi-fidelity simulations, and risk assessment in engineering and microbiology. He collaborates with institutions like the GdR MascotNum and PGMO, advancing methodologies for efficient simulation-based decision-making. His research includes applications in TBM excavation modeling, renewable energy integration, and food safety through quantitative microbial risk analysis. Education: École Normale Supérieure de Cachan (1997–2001), DEA/M2 in Mathematics (2001), PhD in Applied Mathematics (2005), Habilitation (2015). Research interests span Bayesian optimization, Gaussian process metamodeling, and stepwise uncertainty reduction (SUR) strategies. He develops algorithms for efficient sampling in expensive-to-evaluate systems, with applications to engineering, environmental modeling, and public health. His recent articles emphasize quantile set inversion, risk assessment in food safety, and scalable Gaussian process methods. Collaborations include projects on railway infrastructure digital twins (MINERVE) and multi-year renewable energy planning. He has contributed to open-source tools like the STK toolbox for kriging.

Xiaoyu ChenView profile

Assistant Professor

• Machine Learning
• Advanced Manufacturing
• Cyber Manufacturing Systems
• +5 more

Xiaoyu Chen serves as an Assistant Professor in the Department of Industrial and Systems Engineering within the School of Engineering and Applied Sciences at the University at Buffalo. His academic foundation includes a PhD in Industrial and Systems Engineering (2021) and an MEng in Computer Science (2021) from Virginia Tech, complemented by a BS in Optical Information Science and Technology from Beijing Institute of Technology (2015). Dr. Chen's research spans machine learning methodology for advanced manufacturing, networked data analytics in cyber manufacturing systems, statistical methods for Bayesian deep learning, smart connected health applications for organ transplantation and perioperative medicine, and human behavior/performance modeling. His work bridges industrial engineering with cutting-edge AI techniques to solve complex problems in manufacturing and healthcare systems. Analysis of recent publications reveals a strong focus on developing interpretable and context-aware AI systems for manufacturing and clinical applications. Key trends include multimodal learning frameworks, distributed data processing for fog manufacturing, human-AI collaboration paradigms, and medical diagnostics through novel data modalities like breath analysis. Dr. Chen actively teaches core engineering courses including Probability & Statistics for Engineers (IE 360/560), Simulation (IE 541), and Predictive Analytics (Deep Learning) (IE 463/663), demonstrating commitment to both foundational and emerging topics in industrial engineering education.

Vincenzo CrupiView profile

Professor

• Judgment and decision making
• Logic and philosophy of science
• Conditionals
• +3 more

Vincenzo Crupi is a Full Professor of Logic and Philosophy of Science at the University of Turin. He previously held positions as Associate Professor (2014–2019) and Assistant Professor (2011–2014) at the same institution. His affiliations include being an External Member of the Munich Center for Mathematical Philosophy and a Postdoctoral Fellow there (2010–2011). He has also worked at institutions including the University of Florence, University IUAV of Venice, University of Trento, and University of Aix-Marseille. Crupi's research spans Bayesian epistemology Confirmation theory Probability logic Human reasoning Medical decision-making His experimental and theoretical work bridges formal epistemology, cognitive science, and clinical medicine. He has directed the Center for Logic, Language, and Cognition (LLC) at Turin (2014–2021) and served as associate editor for Ergo and European Journal for Philosophy of Science . Crupi has delivered ~150 presentations globally and authored 45 peer-reviewed articles.

• Bayesian Statistics
• Time Series Analysis
• Econometrics
• +3 more

Gregor Kastner is Professor and Deputy Head of the Institute of Statistics at the University of Klagenfurt. His research focuses on Bayesian statistics, time series analysis, econometrics, and computational methods, with applications in finance, economics, and environmental modeling. He develops statistical software including packages for stochastic volatility modeling in R. Kastner's methodological work centers on Bayesian inference for high-dimensional problems, developing efficient computational algorithms for complex models. His applied research examines volatility dynamics in financial markets, macroeconomic forecasting, and spatial analysis of economic indicators. Recent projects include Bayesian nonparametric clustering for evaluating agricultural subsidies in Europe, sparse vector autoregressions for high-dimensional forecasting, and stochastic volatility models for commodity markets. He maintains active collaborations across economics, finance, and environmental science disciplines.

Yiming XuView profile

Assistant Professor

• Mathematics of Data Science
• Random Sampling and Approximation
• Statistical Ranking
• +3 more

Yiming Xu is an Assistant Professor in the Department of Mathematics at the University of Kentucky. He holds a Ph.D. from the University of Utah, completed a postdoc at the University of Waterloo, and worked as a Quantitative Analyst at Wells Fargo Bank. His research focuses on the mathematics of data science, including random sampling, statistical ranking, optimization-based uncertainty quantification, and multifidelity methods. Xu emphasizes problem-driven innovation and collaborates across disciplines. Education: Ph.D. in Mathematics, University of Utah, 2022 Postdoctoral Fellowship, University of Waterloo Quantitative Analyst, Wells Fargo Bank Research interests span computational statistics, numerical analysis, and machine learning applications. His work bridges theoretical foundations and practical data-driven problems, often integrating optimization and probabilistic techniques. Recent contributions include advancements in gradient descent for network analysis, multifidelity distribution estimation, and archetypal analysis using optimal transport. Publications reflect a trajectory in stochastic methods, statistical learning, and computational mathematics, with applications in PDE solvers and uncertainty quantification. Awards: None explicitly listed. Advising and grants: Actively seeks motivated students for collaborative projects. Teaching includes courses on matrix theory, data science, and numerical methods.

Yuexiao DongView profile

Associate Professor

• Sufficient Dimension Reduction
• High-dimensional Data Analysis
• Machine Learning
• +3 more

Yuexiao Dong is an Associate Professor in the Department of Statistics, Operations, and Data Science at Temple University's Fox School of Business and Management. He holds a PhD from Pennsylvania State University (2009) and a Bachelor's in Mathematics from Tsinghua University. His research focuses on sufficient dimension reduction, high-dimensional data analysis, and machine learning. Dr. Dong has been funded by the NSF for his work on 'New Developments in Sufficient Dimension Reduction' and serves as a Gilliland Research Fellow. He is an Associate Editor for the Journal of Systems Science and Complexity since 2015. His research is published in top journals such as the Annals of Statistics and Biometrika . Key interests include developing methodologies for efficient data analysis, particularly in handling high-dimensional and non-Euclidean data. He teaches Quantitative Foundations for Data Science at the undergraduate level. Education: Bachelor’s in Mathematics, Tsinghua University PhD in Statistics, Pennsylvania State University Awards: Gilliland Research Fellow NSF Grant: New Developments in Sufficient Dimension Reduction Dr. Dong’s work bridges statistical theory and applications, emphasizing computational efficiency and real-world relevance. Recent projects include Bayesian model averaging, real-time dimension reduction, and expectile-based regression techniques.