جست‌وجوی استاد و پژوهشگر

• Operations Management
• Stochastic Networks
• Financial Engineering
• +۷ مورد دیگر

Chihoon Lee is a Professor in the School of Business at Stevens Institute of Technology, holding dual roles as Professor and former Associate Dean of Graduate Studies (2020–2024). He holds a PhD in Statistics from the University of North Carolina at Chapel Hill (2008) and a BS in Statistics and Mathematics from Seoul National University (2003). His research bridges applied probability and statistics with applications in operations management, stochastic networks, financial engineering, and infectious disease modeling. Notable contributions include analytical tools for large-scale service systems, stochastic control, and sequential learning for stochastic processes. As Associate Dean, Lee oversaw strategic growth of 10 Master’s and 3 MBA programs, managing 1,400–1,600 graduate students. He chairs the School Graduate Curriculum Committee and collaborates across disciplines, including quantum computing and epidemiology. His work has been recognized with awards such as the 2022 EURO Best Paper Award and the Early Career Statistician Award (2012). Financially, his grants include $200K+ in projects addressing quantum finance and stochastic networks. Lee’s research emphasizes interdisciplinary impact, spanning queueing theory, stochastic control, and statistical learning. He actively serves on editorial boards (e.g., Journal of Korean Statistical Society) and professional committees. His teaching includes courses in multivariate analytics, statistical models, and experimental design.

Monica Bugalloمشاهده پروفایل

استاد

• Statistical Signal Processing
• Bayesian Analysis
• Sequential Monte Carlo Methods
• +۶ مورد دیگر

Mónica Bugallo is a Professor in the Department of Electrical and Computer Engineering at Stony Brook University, located within the College of Engineering and Applied Sciences. Her research focuses on statistical signal processing, including Bayesian analysis, sequential Monte Carlo methods, adaptive filtering, and stochastic optimization, with applications in multiuser communications, smart antenna systems, target tracking, and vehicle navigation. She is affiliated with Stony Brook's ECE department and holds office at 245 Light Engineering. Dr. Bugallo's work bridges theoretical advancements in signal processing with practical implementations in communication systems and navigation technologies. Her contact information includes a direct phone line (631-632-8395) and an email address. Her personal homepage appears unavailable at the provided URL ("/~monica/").

Loukia Meligkotsidouمشاهده پروفایل

دانشیار

• Bayesian Inference
• Computational Statistics
• Hidden Markov Models
• +۴ مورد دیگر

Loukia Meligkotsidou is an Associate Professor of Statistics at the Department of Mathematics of the National and Kapodistrian University of Athens. She holds a BSc in Statistics from Athens University of Economics and Business (2002) and a PhD in Statistics from Lancaster University (2005), focusing on filtering methods for population genetics, phylogenetics, and mixture models. Her research interests span Bayesian inference, computational statistics, hidden Markov models, biostatistics, and econometrics. She teaches undergraduate courses on Bayesian Inference and postgraduate courses on Time Series analysis. Her work emphasizes methodological advancements in statistical modeling, particularly in longitudinal data analysis, competing risks, and model uncertainty. Recent publications address issues like shared parameter models for longitudinal data, Bayesian unit root testing, and quantile regression approaches in financial forecasting. She is a Fellow of the Royal Statistical Society and active in professional societies like ISBA and IMS. Her research integrates theoretical contributions with applied problems in economics, finance, and biology. Notable contributions include frameworks for handling informative dropout mechanisms and developing efficient Bayesian computational methods for complex models. Her work bridges statistical methodology and real-world applications, particularly in econometrics and biostatistics.

Pierre Del Moralمشاهده پروفایل

استاد پژوهشی

• Stochastic particle methods
• Feynman-Kac models
• Nonlinear filtering
• +۵ مورد دیگر

Pierre Del Moral is a prominent Research Professor at INRIA (Institut National de Recherche en Informatique et en Automatique), specifically affiliated with the Bordeaux - Sud Ouest Center. He serves as Head of the INRIA team project ALEA, focusing on stochastic models and uncertainty, and has led the INRIA theme 'stochastic models and uncertainty' (theme 1) since 2010. His significant contributions include strategic planning for EDF-INRIA collaborations and leadership of multiple research initiatives. Del Moral's research centers on Feynman-Kac models and interacting particle systems , with applications spanning nonlinear filtering, Bayesian statistics, financial mathematics, biology, and computational chemistry. His work has established foundational mathematical frameworks for particle filters and sequential Monte Carlo methods, addressing complex problems in robotics, image processing, signal processing, and rare event analysis. His influential publications include the monograph Feynman-Kac formulae. Genealogical and interacting particle approximations (Springer, 2004) and Mean field simulation for Monte Carlo integration (Chapman & Hall/CRC, 2013). Analysis of his recent publications (2023-2025) reveals a continued focus on theoretical foundations of particle methods, with increasing attention to entropic optimal transport, Schrödinger bridges, and Sinkhorn algorithms. His work demonstrates deep connections between probability theory, functional analysis, and computational mathematics, with applications ranging from quantum mechanics to biological systems modeling. The research shows consistent progression from foundational particle filter theory toward more sophisticated mathematical frameworks for high-dimensional and complex stochastic systems. As an academic leader, Del Moral has supervised numerous PhD students including Christophe Baehr, Fredrik Lindsten, and Samuel Remy. He has secured significant research funding through CNRS, INRA, INRIA, and European Commission projects. His methodological contributions have been implemented in the BIIPS software for Bayesian inference with interacting particle systems, developed by the INRIA ALEA team. Del Moral maintains an active role in the academic community as an independent expert for the European Commission (since 2013), member of the Excellence in Research for Australia 2015 evaluation committee, and participant in numerous university selection committees across Europe. His ALEA research team continues to be a leading center for theoretical and applied work on stochastic particle methods.

• Machine Learning
• Spatial Statistics
• Bayesian Inference
• +۳ مورد دیگر

James Martin is an Honorary Research Fellow in the Statistics Section of the Department of Mathematics at Imperial College London, part of the Faculty of Natural Sciences. He previously served as a Senior Teaching Fellow, leading modules on the MSc in Machine Learning and Data Science. His research focuses on machine learning applications in engineering, spatial statistics for ecological systems, and Bayesian inference methods using likelihood-free approaches, particularly sequential Monte Carlo techniques. He is affiliated with the Artificial Intelligence Network, Mathematics research and teaching staff, and the Statistics group. His work bridges theoretical statistical methodologies with practical applications in environmental and engineering domains. Recent publications (2012–2025) emphasize advancements in Bayesian computational methods, spatial point processes, and network analysis, reflecting his interdisciplinary approach to solving complex data-driven problems. While no formal advising or grant details are provided, his academic contributions underscore a commitment to advancing computational and applied statistics. He collaborates within Imperial’s mathematics and AI communities, contributing to cutting-edge research initiatives in data science.

Jeffrey Regierمشاهده پروفایل

استادیار

• Bayesian Statistics
• Astronomy
• Genomics
• +۳ مورد دیگر

Jeffrey Regier is an Assistant Professor in the Department of Statistics at the University of Michigan. He is affiliated with the Center for Computational Medicine and Bioinformatics and the Michigan Institute for Data Science. His research focuses on Bayesian methods applied to astronomy and genomics, utilizing tools such as variational inference, sequential Monte Carlo, and diffusion models. He holds a Ph.D. in Statistics and maintains an active research group exploring interdisciplinary applications of statistical theory. Research interests include advancing statistical methodologies for complex data challenges, particularly in astronomical image analysis, genomic data interpretation, and computational biology. Recent work emphasizes scalable inference techniques and their application to problems like deblending crowded starfields, modeling glioblastoma evolution, and improving accuracy in cell-perturbation experiments. His group collaborates across disciplines to bridge statistical theory with real-world scientific problems. Publications highlight contributions to variational inference algorithms, neural posterior estimation, and probabilistic modeling in genomics and astrophysics. He has no listed scientific awards but is actively engaged in advancing methodological innovations in Bayesian statistics. His advising role extends to mentoring students in the Ph.D. and Master's programs, focusing on training in computational and theoretical statistics. Office: 250 West Hall, Department of Statistics, University of Michigan. Contact: regier@umich.edu.

Arnaud Doucetمشاهده پروفایل

استاد

• Computational Statistics
• Generative Modeling
• Sequential Monte Carlo
• +۲ مورد دیگر

Arnaud Doucet is Professor of Statistics at Oxford University and Senior Staff Research Scientist at Google DeepMind. A leading expert in computational statistics, he develops Monte Carlo methods for machine learning with applications to generative modeling and Bayesian inference. Research Innovations: Pioneered sequential Monte Carlo methods and particle Markov chain Monte Carlo techniques. Current work focuses on diffusion models, optimal transport, and generative AI. Recipient of Royal Statistical Society's Guy Silver Medal (2020) for contributions to statistical computing. Leadership: Institute of Mathematical Statistics Medallion Lecturer (2016), IMS Fellow (2017). Author of influential texts on sequential Monte Carlo methods used widely in statistical computing and machine learning.

Christl Donnellyمشاهده پروفایل

استاد

• Epidemiology
• Infectious Disease Control
• Biostatistics
• +۳ مورد دیگر

Christl Donnelly CBE is a Professorial Fellow in Statistics at the University of Oxford's Department of Statistics, affiliated with St Peter’s College. She holds dual affiliations with Imperial College London (since 2000) and has held prior roles at the University of Edinburgh (1992–1995) and the Wellcome Trust Centre for the Epidemiology of Infectious Diseases at Oxford (1995–2000). Her academic credentials include a BA in Mathematics from Oberlin College and MSc/ScD in Biostatistics from Harvard School of Public Health (graduated 1992). Her research focuses on infectious disease epidemiology, with expertise in outbreak dynamics, statistical modeling, and public health policy. Key areas include Zika, Ebola, MERS, influenza, and rabies, alongside conservation and animal welfare. She is a leading member of the WHO Ebola Response Team and previously chaired the Independent Scientific Group on Cattle TB, overseeing the Randomised Badger Culling Trial (1998–2007). Her methodological contributions span phylogenetic analysis, epidemic modeling, and AI-driven surveillance. Donnelly has received prestigious recognitions, including Royal Society Fellowship, Academy of Medical Sciences Fellowship, and an Honorary Fellowship from the Zoological Society of London. Her work bridges academic research and real-world policy, emphasizing One Health approaches and interdisciplinary collaboration.

Rob Salomoneمشاهده پروفایل

پژوهشگر ارشد

• Statistics
• Machine Learning
• Computational Methods
• +۳ مورد دیگر

Dr. Rob Salomone is a Senior Research Fellow in Data Science at the Queensland University of Technology's (QUT) Centre for Data Science. He earned his Ph.D. in Statistics from the University of Queensland in 2018, focusing on advanced Monte Carlo methodology. Before joining QUT, he held research fellowships at the University of Queensland and UNSW Sydney. His research bridges statistics and machine learning, with an emphasis on developing robust computational techniques for large datasets and complex models. He specializes in creating scientific models that integrate advanced machine learning, domain expertise, and tailored computational methods to address intricate mathematical challenges. Salomone’s work spans Bayesian computation, Monte Carlo methods, and applications in computational biology and environmental systems. His contributions include advancements in nested sampling, CRISPR guide RNA optimization, and graph neural networks for anomaly detection. He also explores federated learning and uncertainty quantification in high-dimensional settings. Notable collaborations include work on agent-based tumor growth models and spectral subsampling for time series analysis. Though no formal students are listed, his research impact is evident through interdisciplinary projects and methodological innovations.

Ning Patricia Ningمشاهده پروفایل

استادیار

• Stochastic Processes
• Markov Chains
• Time Series Analysis
• +۷ مورد دیگر

Ning Patricia Ning is an Assistant Professor in the Department of Statistics at Texas A&M University, affiliated with the Institute of Data Science and Institute for Quantum Science and Engineering. She holds a B.S. from Shandong University, M.S. from USC, and Ph.D. from UCSB. Her research focuses on stochastic processes, Markov chains, Bayesian inference, networks, machine learning, and quantum computing. She has served as an Associate Editor for Bayesian Analysis, Journal of Computational and Graphical Statistics, and Statistics and Computing. Recent contributions include advancements in temporal interference stimulation modeling and applications of sequential Monte Carlo methods in machine learning. Her work bridges theoretical stochastic analysis with practical applications in epidemiology and finance. Notable publications include studies on MCMC optimal scaling and doubly reflected backward SDEs. She actively participates in academic events such as the Stochastic Numerics and Statistical Learning Workshop 2025 and the TAMIDS SciML Workshop. As an NSF panelist, she contributes to funding evaluations in mathematical sciences. Ning’s interdisciplinary approach integrates computational methods with real-world challenges, emphasizing scalable Bayesian inference and quantum algorithms. She has advised on multiple research initiatives and maintains a lab focused on stochastic processes and data science applications.

• Machine Learning
• Financial Mathematics
• Stochastic Control
• +۵ مورد دیگر

Ruimeng Hu is an Associate Professor at the University of California, Santa Barbara (UCSB), with a joint appointment in the Department of Mathematics and Department of Statistics and Applied Probability . They hold editorial roles at Digital Finance and Mathematical Finance , co-organizing special issues on deep learning and machine learning in finance. Their research focuses on interdisciplinary areas including machine learning, financial mathematics, stochastic control, game theory, and climate modeling. Education : Ph.D. in Statistics and Applied Probability, UCSB (2018) M.A. in Mathematical Statistics, UCSB (2014) B.S. in Pure and Applied Mathematics, Peking University (2012) Research Interests : Machine learning applications in finance and climate science Stochastic control and game theory in multi-agent systems Development of physics-informed neural networks Analysis of stochastic partial differential equations Their work bridges theoretical advancements with practical tools for financial risk management and environmental modeling. Key Contributions : Organized international conferences like the 3rd ACM International Conference on AI in Finance (ICAIF-22) Founded World Online Seminars on Machine Learning in Finance Developed frameworks for adversarial games and pandemic control using game theory and reinforcement learning Labs & Collaborations : Center for Financial Mathematics & Actuarial Research (UCSB) Active in interdisciplinary initiatives like the Financial Mathematics Mentoring Initiative

Kostas Kalogeropoulosمشاهده پروفایل

دانشیار

• Bayesian Inference
• Stochastic Differential Equations
• Markov Chain Monte Carlo
• +۶ مورد دیگر

Kostas Kalogeropoulos is an Associate Professor and MSc Statistics Programme Director in the Department of Statistics at the London School of Economics and Political Science (LSE). He previously served as a post-doctoral researcher at the University of Cambridge's Signal Processing Laboratory and holds a PhD from the Athens University of Economics and Business. His research focuses on Bayesian inference methodologies applied to stochastic differential equations, with applications in financial econometrics, biomedical modeling (e.g., epidemic dynamics), and growth curve analysis. Dr. Kalogeropoulos teaches advanced courses including ST451: Bayesian Machine Learning and ST308: Bayesian Inference at LSE, as well as the summer school course ME315: Machine Learning in Practice. He is actively involved in academic administration as Programme Director of LSE’s MSc in Statistics and organizes the Theory of Machine Learning Reading Group. His professional affiliations include the Data Science Group and Social Statistics Group within the LSE Department of Statistics, as well as leadership roles in the Greek Stochastics Team. His research outputs span computational statistics and applied Bayesian methods, with notable contributions to spatio-temporal epidemic modeling, stochastic volatility analysis, and dynamic term structure modeling. He has collaborated with international institutions on projects addressing public health challenges and financial econometric applications.

Chengchun Shiمشاهده پروفایل

دانشیار

• Statistical Learning
• Reinforcement Learning
• Precision Medicine
• +۳ مورد دیگر

Dr. Chengchun Shi is an Associate Professor in the Department of Statistics at the London School of Economics and Political Science (LSE). His research focuses on statistical learning methods for individualized decision-making and analyzing complex data, with applications in precision medicine, finance, and ride-sharing. He joined LSE in 2019 after completing his PhD in Statistics at North Carolina State University, supervised by Dr. Wenbin Lu and Dr. Rui Song. His work emphasizes developing computationally efficient algorithms with statistical guarantees for real-world challenges such as personalized treatment regimes, optimal investment strategies, and dynamic resource allocation. He has contributed to reinforcement learning, causal inference, and off-policy evaluation methodologies, addressing issues like interference in spatial experiments and handling high-dimensional, heterogeneous data. Key research areas include reinforcement learning algorithms, causal mediation analysis, and robust policy evaluation. His publications span topics like off-policy evaluation in confounded environments, spatial experimental designs, and deep learning approaches for distributional modeling. Dr. Shi’s research bridges theoretical statistics and practical applications, aiming to improve decision-making across diverse domains. He holds a strong academic background with a focus on high-dimensional statistical models and their applications in dynamic treatment regimes. His work has been recognized for its innovative approaches to tackling complex data challenges through interdisciplinary methods combining machine learning and rigorous statistical theory.

Chris Drovandiمشاهده پروفایل

استاد

• Bayesian Inference
• Sequential Monte Carlo
• Approximate Bayesian Computation
• +۳ مورد دیگر

Professor Chris Drovandi holds an ARC Future Fellowship at QUT, developing Bayesian methods for complex models. His research advances computational statistics for scientific applications. Key methodological contributions: Robust synthetic likelihood techniques for implicit models Sequential Monte Carlo innovations for high-dimensional inference Optimal experimental design frameworks Publication analysis shows strong interdisciplinary focus, with applications in ecology, cardiology, and materials science. Recent work emphasizes model misspecification robustness and expert knowledge integration. Honors include the Statistical Society of Australia's Moran Medal. He directs the Models and Algorithms Program in QUT's Centre for Data Science.

• Computational Statistics
• Monte Carlo Methods
• Bayesian Inference
• +۳ مورد دیگر

Dr. Leah South is a Senior Lecturer in the School of Mathematical Sciences at Queensland University of Technology (QUT), specializing in computational Bayesian statistics. She holds a Doctor of Philosophy from QUT. Her research interests include Bayesian inference, Monte Carlo methods, variance reduction techniques, and computational statistics. South is actively involved in developing statistical software tools, including R packages like 'RcppSMC' and 'BSL', which enhance Bayesian synthetic likelihood and sequential Monte Carlo methodologies. Her work often bridges statistical theory and practical applications, such as fisheries sustainability modeling and nested sampling for physical sciences. She supervises research topics such as scalable Bayesian inference and statistical methodologies for capturing patient variability in medical treatments. South is affiliated with the Faculty of Science at QUT and contributes to academic discussions through peer-reviewed journals and software development.