Gianluca Iaccarino is a Professor of Mechanical Engineering at Stanford University and the Robert Bosch Chairholder. He serves as Director of the PSAAP Center and leads large-scale computational research initiatives in uncertainty quantification, exascale computing, and multiphysics simulations. His academic journey includes a PhD in Mechanical Engineering from Politecnico di Bari (2005), postdoctoral work at Stanford's Center for Turbulence Research, and progression from Research Engineer to full Professor. Education : PhD (Politecnico di Bari), MS/BS in Aeronautical Engineering (University of Naples) Research : Computational engineering, turbulence modeling, uncertainty quantification, biomedical fluid dynamics, and exascale-ready algorithms Publications : 15+ recent articles focus on turbulence modeling, data-driven simulations, and uncertainty quantification across diverse applications in aerospace, biomedical, and energy systems Awards : PECASE (2010), APS Fellow (2019), multiple best paper awards (AIAA, ASME), Terman Fellow (2007) Students : Advises doctoral and master's students in mechanical engineering and computational methods Leadership : Director of PSAAP Center (2014-present), Chair of Mechanical Engineering Department (2024-present)
Mike Giles is a Professor of Numerical Analysis at the University of Oxford's Mathematical Institute and serves as Head of the Numerical Analysis Group. He is also a Professorial Fellow at Balliol College and a Fellow of the Royal Society (FRS). His academic career spans computational mathematics, scientific computing, and computational finance. Professor Giles' research primarily focuses on Monte Carlo methods, with particular emphasis on the development and numerical analysis of multilevel Monte Carlo methods over the past 15 years. His work has significant applications in computational finance, uncertainty quantification, and solving stochastic differential equations. He has also made substantial contributions to high-performance computing, especially in the exploitation of many-core GPUs for scientific computing applications. His research bridges theoretical numerical analysis with practical computational implementations. Analysis of his publication record reveals a consistent trajectory of innovation in Monte Carlo methodology, evolving from foundational work on path simulation to sophisticated multilevel techniques that dramatically improve computational efficiency. His research spans multiple disciplines including numerical analysis, computational finance, and high-performance computing, with a clear focus on developing practical algorithms that address real-world computational challenges in science and finance. Fellow of the Royal Society (FRS) Professor Giles actively teaches courses in numerical methods for the MSc in Mathematical and Computational Finance and is a leading educator in GPU programming, organizing an annual intensive course on CUDA Programming on NVIDIA GPUs. He has been instrumental in establishing JADE, Oxford's GPU supercomputer facility, which supports research in machine learning and scientific computing. His leadership extends to the Numerical Analysis Group at Oxford and the Mathematical and Computational Finance Group, where he fosters interdisciplinary research connecting mathematics, finance, and computer science. Through his educational initiatives and research leadership, Giles has significantly influenced both academic research and practical applications of advanced computational methods.
Dan Lizotte is an Associate Professor jointly appointed to the Department of Computer Science in the Faculty of Science and the Department of Epidemiology and Biostatistics in the Schulich School of Medicine & Dentistry at Western University. Additional affiliations include the Schulich Interfaculty Program in Public Health and a cross-appointment to the Department of Statistics and Actuarial Sciences. Based in Middlesex College, London, Ontario, his contact email is dlizotte@uwo.ca. His research centers on machine learning and biostatistics for health decision support, with emphasis on sequential decision-making in chronic disease management where evolving patient health status and preferences inform adaptive interventions. Core contributions involve adapting reinforcement learning frameworks to model dynamic health decisions in public health and primary care settings, addressing methodological challenges in personalized medicine and risk prediction. Analysis of his publication record reveals consistent focus on healthcare applications of machine learning, particularly in chronic disease risk modeling using electronic medical records, intersectionality frameworks in public health AI, and Bayesian methods for dose personalization. His work bridges reinforcement learning with clinical decision support systems, advancing dynamic treatment regimes and statistical methodologies for evolving patient data. No scientific awards were mentioned in the provided text. The text does not specify any advisees, grant funding, or educational background details. Lizotte leads a research laboratory focused on machine learning applications in health, as evidenced by the dedicated lab site referenced in his contact information. His team likely explores intersections of statistical methodology, AI ethics, and clinical implementation for personalized health interventions.
Prof. Ruth King is the Thomas Bayes’ Professor of Statistics at the University of Edinburgh’s School of Mathematics. Her research focuses on applying Bayesian statistical methods to ecological and public health challenges, including population estimation for hidden groups (e.g., injecting drug users, modern-day slaves) and wildlife conservation. She develops computationally efficient techniques for analyzing large datasets, such as spatial capture-recapture models for animal populations and spatio-temporal abundance models for hidden human populations. Key projects include estimating survival rates of guillemots (30,000 individuals) and improving capture-recapture models to account for animal movement dynamics. Her work bridges statistical methodology with real-world applications, emphasizing rigorous inference and scalable algorithms. King’s academic contributions span Bayesian modeling frameworks, parameter clustering in neuroscientific data, and hierarchical centering in random effects models. She collaborates with biologists and policymakers to address conservation and public health issues. Notable recent projects include incorporating memory effects into spatial capture-recapture models and developing semi-complete data augmentation for state-space models. Her interdisciplinary approach addresses challenges in ecology, epidemiology, and computational statistics, with a focus on methodological innovation for large-scale data. Her scientific contributions are highlighted through over 100 peer-reviewed articles, including work on integrated population models, animal movement dynamics, and hidden Markov models for seabird behavior. King emphasizes the importance of statistics in uncovering hidden information within datasets, advocating for robust methodologies that ‘stand up in court’ when applied to critical real-world problems.
Abhijit Sarkar is a Professor in the Department of Civil and Environmental Engineering at Carleton University, Ottawa. His work centers on computational dynamics and probabilistic modeling, with office MC 3076 in the Minto Centre for Advanced Studies in Engineering and contact details including phone (613) 520-2600 x6320 and email abhijit_sarkar@carleton.ca . Education: D.Phil. from University of Oxford M.Sc. from Indian Institute of Science (IISc) B.E. from Calcutta University Professional Engineer (P.Eng.) designation His research drives innovation in uncertainty quantification for complex engineering systems. Core interests include dynamics of nonlinear structures, probabilistic mechanics for stochastic finite element methods, and Bayesian inference frameworks for parameter estimation. He pioneers scalable high-performance computing solvers for large-scale systems and sparse learning algorithms to address overfitting in statistical modeling. Recent publications (2022-2024) reveal three dominant trends: (1) Bayesian model calibration for stochastic compartmental systems applied to epidemiology and aerospace, (2) domain decomposition techniques for scalable uncertainty quantification in stochastic PDEs, and (3) sparse learning methods for nonlinear aerodynamic encoding. Key applications span wind turbine vibration analysis, flutter margin prediction, MEMS resonator optimization, and geospatial pandemic modeling. Scientific awards: No awards, fellowships, or medals listed in the source material Graduate supervision includes 6 current students (Ajay Kumar, John Clarabut, Nastaran Dabiran, Sakhi Mittal, Michael Pantano, Brandon Robinson) and 18 graduated students across 17 years (2006-2023). His research leverages high-performance computing for projects in structural dynamics, aeroelasticity, and computational epidemiology, frequently co-supervised with Dominique Poirel and Chris Pettit. Notable grants focus on wind tunnel validation for nonlinear systems and pandemic spread modeling. Based in the Minto Centre for Advanced Studies in Engineering, his computational mechanics group develops algorithms for stochastic dynamics using Carleton University's high-performance computing infrastructure. Collaborations span aerospace engineering (flutter analysis), civil infrastructure (seismic wave propagation), and public health (Covid-19 modeling).
Paul-Christian Burkner is a researcher in the Department of Computer Science at Aalto University. His work focuses on Bayesian statistical methods, computational modeling, and probabilistic programming. He collaborates with Professor Aki Vehtari's research group and has published extensively on topics like model sensitivity, spatiotemporal analysis, and variable selection techniques. His research interests include: Bayesian inference and model comparison Computational statistics Probabilistic programming Machine learning algorithms Statistical modeling in social sciences Neuroimaging data analysis Recent publications demonstrate expertise in simulation-based calibration, spatiotemporal modeling, and Gaussian process approximations. Collaborations span psychology, neuroscience, and machine learning domains. Contact: ext-paul-christian.burkner@aalto.fi
Li Cai is a Professor and Director at the National Center for Research on Evaluation, Standards, and Student Testing (CRESST) within the Graduate School of Education and Information Studies at the University of California, Los Angeles (UCLA). His work focuses on quantitative methods in education, particularly psychometrics and statistical modeling. Ph.D. in Quantitative Psychology from the University of North Carolina – Chapel Hill Research and teaching interests center on psychometrics, latent variable models, item response theory, nonlinear mixed models, and statistical computation. His methodological work addresses advanced techniques for educational assessment and model evaluation. His representative publications include studies on covariance structure models, item response theory, bifactor analysis, and goodness-of-fit testing. These works often emphasize computational algorithms and practical applications in educational measurement. Li Cai is affiliated with CRESST at UCLA, a leading center dedicated to rigorous research, assessment design, and evaluation methodology across diverse educational contexts.
David A. Stephens is a Professor in the Department of Mathematics and Statistics at McGill University, Montreal. He served as Chair of the Department from 2015 to 2019 and as Vice-Dean in the Faculty of Science from 2019 to 2025. His research focuses on Bayesian inference, biostatistics, causal inference, bioinformatics, and statistical genetics. He holds prestigious fellowships: International Statistical Institute (2015), American Statistical Association (2019), and Royal Society of Canada (2024). His work addresses challenges in epidemiology, HIV transmission dynamics, and clinical trial design. Key research themes include: Bayesian hierarchical modeling for infectious diseases (e.g., SARS-CoV-2, HIV) Causal inference in dynamic treatment regimes Survival analysis and censored data methods Statistical genomics and epigenetics His publications analyze public health trends, such as HIV transmission clusters in Quebec and SARS-CoV-2 seroprevalence in Canada. Methodologically, he develops novel techniques for time-series analysis, recruitment forecasting in clinical trials, and computational statistics. Notable contributions include: Advancing phylogenetic cluster inference in HIV studies Optimizing warfarin dosing strategies via SMART trials Modeling gut microbiota impacts on growth faltering in infants His academic leadership includes roles at McGill and prior experience at Imperial College London. His work bridges statistical theory and practical healthcare applications, emphasizing interdisciplinary collaboration.
Ankush Agarwal is an Associate Professor in the Department of Statistical and Actuarial Sciences at the University of Western Ontario. His research focuses on mathematical finance, financial statistics, and Monte Carlo methods, with applications to risk management and derivatives pricing. He supervises PhD students in quantitative finance and has taught courses on Monte Carlo methods and advanced financial modeling at Western University. Education: PhD in Mathematics from Tata Institute of Fundamental Research (2015) Research interests span regime-switching models, longevity risk hedging, stochastic differential equations, and rare event simulation. His work combines theoretical probability with computational techniques for financial applications. Recent publications include studies on McKean-Vlasov SDEs, implied Sharpe ratio estimation, and optimal portfolio strategies under stochastic volatility. These works demonstrate his expertise in stochastic processes and financial engineering. Supervision: Current PhD advisees include Ying Liao, Buchun Wang, and Shuya Zhang at the University of Glasgow. Former advisees include Yongjie Wang and Yihan Zou.
Nabil Kahale is an Associate Professor of Finance at ESCP Business School in Paris. His research focuses on financial derivatives, Monte Carlo methods, optimization, and machine learning. He holds a PhD in theoretical computer science from MIT (1993) and an HDR (French habilitation) from Université Paris 1 Panthéon-Sorbonne (2020), enabling him to supervise PhD students. His academic career includes prior roles in theoretical computer science and consulting for banks. He has published widely in top journals such as Mathematical Finance , Management Science , and SIAM Journal on Computing . Education: Bachelor of Science in Engineering, École Polytechnique (1987) PhD in Theoretical Computer Science, MIT (1993) HDR in Economics, Université Paris 1 Panthéon-Sorbonne (2020) Research Interests: His work bridges finance and computational methods, emphasizing practical applications of stochastic models and algorithmic efficiency. Key areas include derivative pricing, risk management, and the integration of machine learning into financial systems. Professional Contributions: He has served as a consultant for banking institutions and a referee for the French Ministry of Economy and Finance. His research also intersects with social and economic policy analysis, such as evaluating the economic impact of public health measures. Labs/Teams: While no specific lab affiliation is mentioned, his collaborations span interdisciplinary teams in finance, computer science, and applied mathematics through his publications and consulting work.
Christophe Andrieu is a Professor in Statistics within the School of Mathematics at the University of Bristol. His research bridges theoretical probability, computational statistics, and applied mathematics, with significant contributions to Markov Chain Monte Carlo methodologies and Bayesian inference frameworks. He maintains active collaborations across engineering and data science domains. His educational background includes: M.A. from List.Natnl.Scis.App.Lyon Additional M.A. (institution unspecified) Ph.D. from Paris Andrieu's research focuses on Markov Chain Monte Carlo theory , where he develops convergence guarantees and efficiency bounds for complex samplers. His work extends to non-reversible MCMC algorithms , piecewise deterministic processes , and gradient-free optimization techniques. Recent publications demonstrate innovative approaches to state-space models and numerical integration, often addressing high-dimensional statistical challenges through stochastic approximation methods. His fingerprint reveals deep specialization in Markov chain convergence analysis and computational Bayesian statistics. His 15 most recent publications (2021-2025) exhibit consistent focus on theoretical foundations of Monte Carlo methods, particularly convergence analysis of Markov chains and novel sampler designs. Key trends include the application of weak Poincaré inequalities to pseudo-marginal MCMC, development of self-organizing state-space models, and exploration of hypocoercivity in piecewise deterministic processes. The work spans both theoretical advancements and practical implementations for engineering and statistical applications. Andrieu has secured significant research funding including: COmputational Statistical INference for Engineering and Security (COSINES) (2018-2023) New Approaches to Data Science (2018-2023) He has supervised 5 research students and maintains active collaborations in computational statistics and machine learning. His network shows strong connections with probability theory and engineering research groups.
Aleksandar Mijatović is a Professor of Probability at the Department of Statistics, University of Warwick, and Deputy Head of Department for Research. He was previously Chair in Probability at King's College London and Reader in Probability at Imperial College London. His research focuses on probability theory, stochastic processes, mathematical finance, numerical stochastics, and data science. He holds a Ph.D. in low-dimensional topology from Trinity College Cambridge and worked as a quantitative analyst in foreign exchange derivatives before academia. Research interests include stochastic analysis of processes with jumps, simulation methods (e.g., Monte Carlo), stochastic control, and applications in finance. He is a Fellow of the Alan Turing Institute and maintains a YouTube channel, Prob-AM, explaining his research. His work often bridges theoretical probability with practical applications in finance and data science. Key publications explore topics like reflected Brownian motion, Lévy processes, branching processes, and stochastic gradient descent. Collaborations with institutions like King’s College London and Imperial College London highlight his academic networks. His contributions span theoretical advancements and computational methodologies, with applications in risk management, option pricing, and algorithm development.
PD Dr. habil. Thomas Wöhling serves as a Senior Research Scientist and Team Leader for Stochastic Modelling of Hydrosystems at the Chair of Hydrology, Dresden University of Technology's Faculty of Environmental Sciences. His research spans integrated environmental systems modeling with particular expertise in surface water-groundwater interactions, braided river systems, and vadose zone processes. Previously, he held research positions at Water and Earth System Sciences Competence Cluster in Tübingen (2010-2015) and Lincoln Environmental Research in New Zealand (2006-2010). Dr. Wöhling completed his Dipl.-Hydrol. (1999) and PhD in Hydrology (2005) at Dresden University of Technology, followed by habilitation in Stochastic Hydrology (2021). His educational background includes extensive research at the Institute of Hydrology and Meteorology at TU Dresden (1999-2005) where he developed foundational expertise in hydrological modeling. Wöhling's research focuses on integrated modeling of coupled environmental systems , particularly flow and contaminant transport in surface water-groundwater systems, nutrient and energy fluxes in soil-plant-atmosphere systems, and distributed hydrological modeling. His work emphasizes stochastic modeling and uncertainty analysis , with significant contributions to inverse modeling, model calibration, multiobjective optimization, and Bayesian model averaging techniques. He has pioneered methods for evaluating monitoring network worth and data utility for environmental models. His publication record demonstrates consistent contributions to hydrological science, with recent work (2023-2025) focusing on machine learning applications in hydrology, advanced statistical inversion techniques, and complex karst system modeling. Key trends include integration of physics-based and data-driven approaches, improved uncertainty quantification methods, and applications to climate change impacts on water resources. His work bridges theoretical advances with practical applications in New Zealand's braided rivers and European hydrological systems. STAHY Best Paper Award (2018) ASCE Journal of Irrigation and Drainage Engineering Best Reviewer Awards (2008, 2010, 2011, 2015, 2018) ASCE Journal of Irrigation and Drainage Engineering Best Paper Awards (2008, 2009) Dr. Wöhling leads the Stochastic Modelling of Hydrosystems team and has secured funding for numerous projects including Klimakonform, ISOSIM, VAMOS II, and the International Research Training Group 'Integrated Hydrosystem Modelling.' His work combines novel monitoring techniques with modeling and optimal sensor placement to improve prediction reliability for river-groundwater exchange fluxes. He collaborates extensively with international partners, particularly in New Zealand through the Lincoln Agritech's Braided Rivers program. His laboratory work focuses on combining traditional hydrological measurements with advanced computational techniques, including deep learning applications for soil surface hydrology and time-windowed Bayesian analysis for predictive modeling. The team maintains strong connections with field sites in Germany's Saxon region and New Zealand's Canterbury Plains, facilitating integrated theoretical and empirical research approaches.
Georgios Zouraris is a Professor at the University of Crete, where he has maintained an active research profile since earning his Ph.D. from the same institution in 1995. His work is centered in the School of Science and Engineering, focusing on advanced computational mathematics with applications in physics and engineering. Education: Ph.D. in Mathematics, University of Crete, 1995 Professor Zouraris specializes in the development and rigorous analysis of numerical methods for partial differential equations. His research spans finite element and finite difference techniques for nonlinear Schrödinger equations, logarithmic heat equations, and stochastic PDEs with space-time white noise. Key contributions include error estimation frameworks for relaxation schemes, convergence analysis of Crank-Nicolson methods, and efficiency improvements for multilevel Monte Carlo simulations. His theoretical work consistently addresses singular nonlinearities and complex domain geometries, bridging mathematical rigor with computational practicality. Analysis of his 2020-2025 publications reveals a sustained focus on high-accuracy numerical schemes for challenging PDEs, particularly those involving logarithmic singularities and stochastic forcing. Recent work demonstrates increasing sophistication in handling noncylindrical domains and coupling strategies, with applications ranging from quantum systems to material science. The publications show consistent emphasis on provable convergence rates and computational efficiency. Information regarding student advising, research grants, and laboratory facilities is not documented in the available sources. His active publication record through 2025 indicates ongoing research leadership in computational mathematics.
Fabio Nobile is a Full Professor at the École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences (SB), Department of Mathematics (MATH), holding the CADMOS Chair in Scientific Computing and Uncertainty Quantification. He leads the CSQI (Chair of Scientific Computing and Uncertainty Quantification) group. His work focuses on numerical methods for partial differential equations (PDEs), uncertainty quantification, stochastic modeling, and computational fluid dynamics. He is involved in collaborative projects involving fluid-structure interaction, cardiac electro-mechanics, and energy systems. Professor Nobile has extensive teaching experience, including courses on advanced analysis, stochastic simulation, and numerical integration of stochastic differential equations. He supervises numerous PhD students and has contributed to over 200 peer-reviewed publications, covering topics such as low-rank approximation methods, multilevel Monte Carlo techniques, and optimal control under uncertainty. His research emphasizes interdisciplinary applications, including biomedical engineering (e.g., personalized cardiac simulations) and renewable energy (e.g., probabilistic load forecasting). He collaborates with industries and academic institutions globally, advancing computational methodologies for engineering and scientific challenges.
