جست‌وجوی استاد و پژوهشگر

Giorgos Minasمشاهده پروفایل

مدرس

• Bayesian Statistics
• Stochastic Processes
• Systems Biology
• +۳ مورد دیگر

Dr. Giorgos Minas is a Lecturer in Statistics at the University of St Andrews' School of Mathematics and Statistics. His research focuses on computational statistics, systems biology, and stochastic modeling with applications in biomedical fields. He holds a PhD in Statistics from the University of Warwick (2013) and has contributed to developing MATLAB tools like ReTrOS for transcriptional activity analysis. His work integrates dynamic experimental data to infer biological mechanisms, with notable publications on Bayesian inference for stochastic systems, protein biomarker identification in cancer, and parameter sensitivity in biochemical networks. Key research areas include Bayesian analysis of oscillatory systems, proteomics-driven cancer treatment outcome prediction, and systems-level modeling of signaling pathways. His recent work (2024) advances phase-corrected linear noise approximation methods for stochastic systems. Minas has supervised PhD students Chao Gao and Xiaoyue Yang. He secured a QJMAM Award (2019) supporting his research and actively contributes to statistical methodologies for clinical trials and neuroimaging studies.

• Statistical Modelling
• Epidemiology
• Bayesian Inference
• +۴ مورد دیگر

Dr Ben Swallow is a Lecturer in Statistics at the University of St Andrews, affiliated with the School of Mathematics and Statistics. His research focuses on statistical methodology applied to epidemiological and ecological problems, with a particular emphasis on Bayesian inference, agent-based models, and public health surveillance. He has contributed to high-impact studies on pandemic response, vaccine efficacy analysis, and wildlife population dynamics. Dr Swallow has supervised PhD students Xiahui Li and Rebecca Supple. His work bridges theoretical statistics with practical applications, including modeling virus transmission dynamics, analyzing ecological data from citizen science projects, and developing computational tools for epidemiological research. Key contributions include advancements in agent-based model calibration, statistical methods for evaluating vaccine safety, and Bayesian approaches to infectious disease modeling. His research outputs span journals such as Journal of Theoretical Biology , BMC Biology , and Journal of the Royal Statistical Society . He actively participates in academic discussions, including contributing to the 'Discussion Meeting on Analysis of citizen science data'.

Paul D. Allisonمشاهده پروفایل

استاد

• Causal Inference
• Statistical Methods
• Missing Data Techniques
• +۳ مورد دیگر

Paul D. Allison is a Professor of Sociology at the University of Pennsylvania, affiliated with the Department of Sociology within the School of Arts & Sciences. His research focuses on improving statistical methods for causal inference from non-experimental data, particularly fixed effects models and missing data techniques. He has contributed extensively to Stata modules (e.g., REG2LOGIT, XTDPDML) and authored influential books on logistic regression, survival analysis, and longitudinal data analysis. His work bridges methodological innovation with applications in social sciences. Education: Ph.D., Sociology, University of Wisconsin at Madison, 1976 M.S., Sociology, University of Wisconsin at Madison, 1973 A.B., St. Louis University, 1970 Research emphasizes causal inference, panel data analysis, and methodological rigor. Key areas include fixed effects models to control for unobserved heterogeneity, Bayesian approaches for cross-lagged panel models, and maximum likelihood estimation in dynamic panels. He critiques common pitfalls in statistical practices (e.g., lagged dependent variables in mixed models) and advocates for better missing data handling via multiple imputation and maximum likelihood. His recent work explores sample size requirements for mediation in hierarchical studies, asymmetric fixed-effects models, and the implications of measurement error in panel data. Articles often address practical methodological challenges faced by social scientists. Notable contributions include textbooks like Fixed Effects Regression Models and Survival Analysis Using SAS , which are foundational for applied researchers. He maintains a prominent statistical methods website and collaborates on interdisciplinary projects, such as analyzing gun violence risk and health outcomes.

• Theoretical Community Ecology
• Evolutionary Biology
• Phylogenetic Community Ecology
• +۷ مورد دیگر

Prof. Rampal S. Etienne is a Full Professor in Theoretical and Evolutionary Community Ecology at the University of Groningen's Faculty of Science and Engineering. He leads the Etienne Group and serves as Scientific Director of the Groningen Institute for Evolutionary Life Sciences. His research focuses on understanding biodiversity processes through theoretical models and phylogenetic analysis. Education: PhD in Metapopulation Ecology, Wageningen University (2002) MSc degrees in Philosophy of Science, Environmental Science, and Foundations of Physics (cum laude), Utrecht and Nijmegen Universities (1996–1999) Research Interests: Theoretical ecology, evolutionary dynamics, phylogenetic community ecology, diversification modeling, and island biogeography. Collaborative projects include studies on African buffalo populations, fruit fly behavior, and microbial evolution. He develops frameworks like DAISIE for island biodiversity modeling and secsse for trait-dependent speciation analysis. Publications & Grants: Over 184 peer-reviewed articles, including work on phylogenetic color evolution and species diversification. Secured grants such as the VICI grant (2014) and Dutch Research Agenda funding (2024). Editor for Ecology Letters and Journal of Theoretical Biology . Awards: Descartes-Huygens Prize (2020), Honorable Mention for American Naturalist Student Paper Award (2023). Recognized for collaboration with CNRS and contributions to evolutionary theory. Labs/Teams: Leads experimental and computational studies on microsnails, microbes, and mites. Coordinates the Adaptive Life research initiative exploring eco-evolutionary dynamics under climate change.

Peng Chenمشاهده پروفایل

استادیار

• Scientific Machine Learning
• Uncertainty Quantification
• Bayesian Inference
• +۵ مورد دیگر

Peng Chen is an Assistant Professor in the School of Computational Science and Engineering (CSE) at Georgia Institute of Technology, part of the College of Computing. His research focuses on Scientific Machine Learning (SciML), Uncertainty Quantification (UQ), and their applications in computational science and engineering. He holds a PhD and Master's from École Polytechnique Fédérale de Lausanne (EPFL) and a Bachelor's from Xi’an Jiaotong University. Education: PhD in Computational Mathematics (EPFL, 2014), Master in Computational Mathematics (EPFL, 2011), Bachelor in Mathematics (Xi’an Jiaotong University, 2009). Research interests include Bayesian inference, stochastic optimization, parallel computing, and scalable algorithms for high-dimensional problems. His work addresses challenges in data-driven modeling of complex systems, such as metamaterial design, plasma fusion optimization, and epidemic modeling. Advising: Supervises PhD and Master’s students in SciML and UQ. Notable students include Jinwoo Go, Yuan Qiu, and Phillip Si. Alumni include Irene Simo Munoz and Leyao Huang. Labs/Teams: Leads the SciML & UQ group at Georgia Tech, developing software like SOUPy and pSVGD for stochastic optimization and Bayesian inference. Collaborates on NSF-funded projects, including a $1.5M award for variational neural networks in physical systems.

David Warneمشاهده پروفایل

مدرس

• Statistical Data Science
• Bayesian Inference
• Computational Biology
• +۳ مورد دیگر

Dr. David Warne is a Lecturer in Statistical Data Science at the School of Mathematical Sciences , Queensland University of Technology (QUT). He is affiliated with the Centre for Data Science and the ARC Centre of Excellence for Mathematical and Statistical Frontiers . His research focuses on Bayesian inference, computational statistics, and their applications in ecology, environmental science, and computational biology. David holds a PhD in Mathematics (QUT), a BMath/BInfoTech (QUT), and a BInfoTech (Hons) (QUT). Research Interests: Bayesian methods, likelihood-free inference, parameter identifiability, stochastic modeling, and applications to ecological systems and environmental decision-making. His work bridges statistical theory with computational tools to address complex biological and environmental problems. Publications: David’s recent work emphasizes innovative statistical approaches for model calibration, uncertainty quantification, and decision analysis in ecological and biomedical contexts. Key themes include multifidelity Bayesian inference, model misspecification, and agent-based modeling of tumor growth. Awards: He received the Outstanding Doctoral Thesis Award during his PhD at QUT. His research has been published in high-impact journals such as Journal of Computational Physics , Ecological Indicators , and Bulletin of Mathematical Biology . Supervision: Topics include stochastic process modeling, coral reef recovery, and scalable Bayesian inference. Labs/Teams: Active contributor to the Centre for Data Science and the ARC Centre of Excellence for Mathematical and Statistical Frontiers.

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.

Steven Grossمشاهده پروفایل

استاد

• Philosophy of Language
• Philosophy of Mind
• Foundations of Mind-Brain Sciences
• +۳ مورد دیگر

Steven Gross is a Professor of Philosophy and Chair at Johns Hopkins University's William H. Miller III Department of Philosophy, part of the Krieger School of Arts & Sciences. He holds secondary appointments in Cognitive Science and Psychological and Brain Sciences and is affiliated with the Science of Learning Institute and the Foundations of Mind Group. Gross earned an AB and PhD from Harvard University, with prior teaching roles at University College London, University of Pennsylvania, and Georgetown University. He previously served as President of the Society for Philosophy and Psychology. His research focuses on philosophy of language, mind, and the foundations of mind-brain sciences. Key topics include semantic perception, the perception-cognition border, innateness, cognitive penetration, attention, and probabilistic models of perception. Gross has also contributed to debates on methodology in linguistics and contextual semantics. His affiliations reflect interdisciplinary engagement, bridging philosophy with cognitive science and neuroscience. Notably, he explores questions like the ethical implications of autonomy in procreation and the philosophical underpinnings of perception and consciousness.

Ryan Martinمشاهده پروفایل

استاد

• Foundations of statistics
• Foundations of probability
• Generalized Bayes and Gibbs posterior distributions
• +۵ مورد دیگر

Ryan Martin is a Professor in the Department of Statistics at North Carolina State University (NC State). He holds a Ph.D. in Statistics from Purdue University (2009) and a B.A. in Mathematics from Franklin College (2003). His research focuses on foundational aspects of statistics and probability, including generalized Bayes methods, imprecise probability, and high-dimensional problems. He co-founded Researchers.One, an innovative peer review platform. Education: Ph.D., Statistics, Purdue University, 2009 M.S., Statistics, Indiana University-Purdue University Indianapolis, 2005 B.A., Mathematics, Franklin College, 2003 Research Interests: His work emphasizes foundational statistical theory, with applications in imprecise probability, Bayesian methods, and high-dimensional inference. Notable contributions include inferential models (IMs), possibilistic frameworks, and regularization techniques for anytime valid inference. He actively explores computational methods for statistical efficiency and reliability. Recent Contributions: Recent work includes advancements in e-processes for efficient uncertainty quantification, variational approximations for possibilistic models, and theoretical developments in Bernstein-von Mises theorems. He also investigates Fisher's underworld and reliability in scientific inference. Awards and Honors: D. D. Mason Award (2022) Van Dantzig Seminar Speaker (2021) Sigma Xi nominee (2020) Thank-a-Teacher Award (2018) I. W. Burr Award (2010) Advising and Grants: He has mentored over 50 graduate students and secured multiple NSF grants. Current projects include Bayesian model selection in high-dimensional settings and imprecise-probabilistic inference. Labs/Teams: Co-leads the Researchers.One initiative, promoting open scholarly communication. Active in collaborative networks on imprecise probability and statistical foundations.

Juan Carlos Escancianoمشاهده پروفایل

استاد

• Econometric Theory
• Financial Econometrics
• Risk Management
• +۳ مورد دیگر

Juan Carlos Escanciano holds the position of Research Chair in Economics and Full Professor (Catedrático) at the Economics Department of Universidad Carlos III de Madrid. He obtained his PhD in Economics from the same university in 2004. Prior to his current role, he served as Assistant Professor at Universidad de Navarra (2004-2006), Full Professor at Indiana University (2006-2018), and held visiting positions at Yale University, Cornell, Rochester, and MIT. His research focuses on Econometric Theory, including identification, estimation, specification testing, and applications in Financial Econometrics and Risk Management. Escanciano holds editorial roles at leading journals including Econometric Theory , Econometric Reviews , and Journal of Business and Economic Statistics , and serves as Co-Editor of Advances in Econometrics . He is a Fellow of the Journal of Econometrics and has published extensively in top-tier journals such as the Journal of the American Statistical Association and The Annals of Statistics . His research interests emphasize semiparametric/nonparametric methods, specification testing, and empirical asset pricing. Notable contributions include work on backtesting financial risk measures, conditional moment restrictions, and the development of robust estimation techniques. His recent work explores machine learning applications in econometrics and systemic risk analysis. Scientific Awards: Fellow of the Journal of Econometrics Grants & Advising: Extensive record in grant-funded research; has advised numerous PhD candidates through the university's Economics Doctoral Program. Labs/Teams: Leads the Quantitative Economics Research Group at UC3M, collaborating with global institutions on structural econometric modeling.

Jessica Stockdaleمشاهده پروفایل

استادیار

• Infectious Disease Modelling
• Genomic Epidemiology
• Bayesian Statistics
• +۵ مورد دیگر

Dr. Jessica Stockdale is an Assistant Professor in the Department of Mathematics at Simon Fraser University (SFU), where she is part of the Mathematics, Genomics & Prediction in Infection & Evolution (MAGPIE) research group and a member of The Pacific Institute on Pathogens, Pandemics and Society (PIPPS). Her research focuses on infectious disease modelling and genomic epidemiology, particularly combining epidemiological and genomic data using Bayesian statistics and mathematical models to understand disease transmission dynamics. She completed her PhD in Statistics at the University of Nottingham and held a postdoctoral fellowship at SFU before joining the faculty in 2022. Dr. Stockdale teaches courses such as MATH 498 (Communication and Research Skills) and MATH 499W (Honours Research Project) in 2025. She co-teaches the 'Reconstructing Transmission with Genomic Data' course at the Summer Institute in Statistics and Modeling in Infectious Diseases (SISMID), which emphasizes genomic data analysis and transmission reconstruction methods. Her work includes studying SARS-CoV-2 transmission in long-term care facilities and influenza vaccine candidate identification using phylogenetic approaches. Her research interests span mathematical epidemiology, genomic data integration, and public health ethics. Notable contributions include quantifying intervention impacts in outbreaks and developing statistical methods for outbreak analysis. She actively engages in interdisciplinary collaborations to bridge mathematics, genomics, and public health challenges.

John Stockieمشاهده پروفایل

استاد

• Fluid Dynamics
• Industrial Mathematics
• Scientific Computing
• +۷ مورد دیگر

John Stockie is a Professor in the Department of Mathematics at Simon Fraser University (SFU), part of the Faculty of Science. He earned his Ph.D. in Applied Mathematics from the University of British Columbia in 1997. His research focuses on fluid dynamics, industrial mathematics, and scientific computing, with an emphasis on developing numerical methods for solving complex physical systems governed by nonlinear PDEs. He combines asymptotic analysis, finite volume methods, and adaptive mesh techniques to address applications in fuel cells, biofluid dynamics, and porous media flow. Stockie’s work bridges theoretical and applied mathematics, addressing real-world challenges such as hydrogen fuel cell efficiency, tree sap exudation mechanisms, and glacier dynamics. His teaching includes courses like MACM 316 Numerical Analysis I. He has contributed to open-access educational resources, including a clicker question bank for numerical analysis. His research trends span environmental modeling (e.g., atmospheric dispersion, glacier simulation), biomedical applications (biofilm dynamics), and industrial problems (emission estimation). He emphasizes interdisciplinary collaboration, often involving computational tools like level set methods and Bayesian inversion approaches. Stockie’s lab focuses on developing robust numerical algorithms and their application to fluid-structure interactions, multiphase flows, and environmental systems. His work has implications for climate science, biomedical engineering, and sustainable energy technologies.

Anirban Bhattacharyaمشاهده پروفایل

استاد

• Bayesian Statistics
• High-dimensional Data Analysis
• Bayesian Nonparametrics
• +۳ مورد دیگر

Anirban Bhattacharya is a Professor and Patricia R. Smith and Dr. William B. Smith Faculty Fellow in the Department of Statistics at Texas A&M University, within the College of Arts & Sciences. His research focuses on Bayesian asymptotics, Bayesian nonparametrics, and high-dimensional data analysis. He holds a Ph.D. in Statistics from Duke University (2012) and Master's and Bachelor's degrees in Statistics from the Indian Statistical Institute (2008 and 2006). His academic journey includes advanced studies at prestigious institutions, blending theoretical rigor with computational innovation. Research interests emphasize robust statistical methods, scalable Bayesian computation, and applications in machine learning. Recent work explores challenges in high-dimensional inference, optimal transport, and fair clustering algorithms. Notable contributions include advancements in Gibbs posterior frameworks, constrained Gaussian processes, and variational inference techniques. He has been recognized with the prestigious Smith Fellowship, highlighting his impact in statistical methodology. His publications span foundational Bayesian theory to applied problems in physics and engineering. Bhattacharya’s work often bridges computational efficiency and statistical accuracy, addressing modern challenges in big data analysis. Ongoing research includes developing robust estimators for complex metric spaces and exploring fair machine learning methodologies.

Abhishek Royمشاهده پروفایل

استادیار

• Stochastic Optimization
• Machine Learning
• Adversarial Attacks
• +۷ مورد دیگر

Abhishek Roy is an Assistant Professor in the Department of Statistics at Texas A&M University, affiliated with the College of Arts & Sciences. His role involves teaching and research in statistical methodologies and their applications in machine learning and optimization. Research interests focus on stochastic optimization, adversarial machine learning, algorithmic fairness, Bayesian inference, nonconvex optimization, and game theory. He explores robust statistical algorithms under complex data conditions (e.g., heavy-tailed distributions, Markovian dependencies) and develops defenses against adversarial attacks in clustering and predictive models. His work intersects computational statistics with modern AI challenges, emphasizing theoretical foundations and practical implementations. Recent publications (2023–2025) emphasize online covariance estimation, nonstationary optimization, and quantifying fairness in machine learning models. Earlier works (2018–2022) address adversarial attacks, moving target defenses, and game-theoretic security frameworks. No scientific awards are explicitly mentioned. Details on grants or advising roles are not provided in the available texts. He is based in Blocker 415E with contact information listed on the Texas A&M Statistics Department website.

Mylène Bédardمشاهده پروفایل

استاد

• Computational Statistics
• Bayesian Methods
• Markov Chain Monte Carlo (MCMC)
• +۵ مورد دیگر

Mylène Bédard is a Full Professor in the Department of Mathematics and Statistics at Université de Montréal. She holds a Ph.D. from the University of Toronto (2006) and oversees the graduate programs in statistics. Her research focuses on computational statistics, Bayesian methods, and Markov Chain Monte Carlo (MCMC) algorithms, with emphasis on optimal scaling, algorithm efficiency, and robust statistical inference. She has collaborated on topics such as MALA algorithms, hierarchical models, and risk theory in actuarial science. Her teaching spans advanced courses in actuarial science (e.g., risk theory, financial mathematics) and statistical theory (e.g., Bayesian inference, survival analysis). She has supervised over 20 graduate students, many of whom have received prestigious awards including the Prix Serge-Tardif and Prix Constance-van Eeden. Her work integrates theoretical advancements with practical applications in finance, climatology, and healthcare. Key research themes include adaptive MCMC methodologies, Bayesian robustness, and computational techniques for high-dimensional data. Recent projects involve regional adaptive algorithms, non-stationary phase analysis of MALA, and geometric approaches to Bayesian marginalization. Her contributions bridge statistical theory and computational innovation, addressing challenges in sampling efficiency and algorithmic convergence.