جست‌وجوی استاد و پژوهشگر

Giacomo Zanellaمشاهده پروفایل

دانشیار

• Computational Statistics
• Applied Probability
• Machine Learning
• +۳ مورد دیگر

Giacomo Zanella is an Associate Professor in Statistics at Bocconi University and a research affiliate of the Bocconi Institute for Data Science and Analytics (BIDSA). His academic work focuses on the intersection of Computational Statistics, Applied Probability, and Machine Learning, with a particular emphasis on Bayesian computation. He earned his PhD from the University of Warwick in 2015 and was honored with the EPSRC Doctoral Prize Fellowship (2015–2016). Education: PhD in [Discipline Not Specified] from the University of Warwick, 2015 Research Interests: Zanella’s research spans Computational Statistics , Applied Probability , and Machine Learning . His work integrates theoretical advancements and practical applications, including Monte Carlo methods, Markov Chain Monte Carlo (MCMC) algorithms, computational complexity of Bayesian methods, Bayesian Nonparametrics, hierarchical models, random partition models, stochastic processes, and spatial statistics. He also explores applied areas like clustering, record linkage, and variable selection, leveraging interdisciplinary approaches to statistical analysis. Scientific Awards: EPSRC Doctoral Prize Fellowship (2015–2016) Various international research awards (names unspecified) Advising and Grants: Zanella’s role as an Associate Professor involves advising students, though specific advisees are not listed here. While no explicit grant details are provided, his research has been supported by fellowships such as the EPSRC Doctoral Prize Fellowship. He has also contributed to course development in advanced computational methods and Bayesian theory. Labs and Teams: He is affiliated with the Bocconi Institute for Data Science and Analytics (BIDSA) , collaborating with interdisciplinary teams to advance data-driven statistical methodologies and machine learning applications.

• Statistical Machine Learning
• Bayesian Statistical Inference
• Statistical Ecology
• +۷ مورد دیگر

Jabed Tomal is an Associate Professor in Statistics and Data Science at Thompson Rivers University (TRU), Canada. Previously, he held positions as an Assistant Professor at TRU (2018–2023) and the University of Toronto Scarborough (2014–2018), and a Postdoctoral Fellow at the University of British Columbia (2014). He earned a Ph.D. in Statistics (2013) from UBC, specializing in statistical machine learning, and dual M.Sc. degrees in Statistics (University of Windsor, 2007) and Biostatistics (University of Dhaka, Bangladesh). His research focuses on ensemble methods, Bayesian inference, and statistical ecology, with applications in drug discovery, protein homology, and environmental modeling. Key research interests include developing ensemble models for high-dimensional data, Bayesian methods for breakpoints detection in housing markets and ecological systems, and statistical approaches in healthcare. Notable contributions include work on QSAR studies, Bayesian hierarchical modeling of pandemic impacts, and ecological threshold detection. Tomal has secured grants such as the NSERC Discovery Grant ($102,500) and TRU internal funds for projects in big data and environmental thresholds. He has advised numerous graduate and undergraduate students on topics ranging from machine learning in healthcare to single-cell RNA sequencing analysis. His teaching spans courses like Bayesian Machine Learning, Multivariate Statistics, and Theoretical Machine Learning at the graduate level, alongside foundational statistics and calculus courses at TRU and the University of Dhaka. Administrative roles include Chair of the Award and Scholarship Committee for TRU’s Master of Data Science program and membership in Senate Research Committee. Education: Ph.D., Statistics (2013), UBC Vancouver M.Sc., Statistics (2007), University of Windsor M.Sc., Biostatistics (2005), University of Dhaka Awards: NSERC Discovery Grant (2021–2026) Research Training Recognition Fund (TRU,多次) SSC 2013 Talk Honourable Mention Labs/Teams: Active in interdisciplinary projects at TRU’s Department of Mathematics and Statistics, focusing on data science applications in ecology, healthcare, and genetics.

Julien Chiquetمشاهده پروفایل

مدرس پاره‌وقت

• Statistics
• Machine Learning
• Sparse Methods
• +۱۰ مورد دیگر

Julien Chiquet is a researcher and part-time lecturer at École Polytechnique (2020-2024), and the head of the UMR MIA Paris-Saclay research unit. He is affiliated with AgroParisTech on the Paris-Saclay campus and leads the SOLsTIS team. He serves as the chief editor of the journal Computo , focusing on computational statistics and data science. Education: Habilitation (2015) Research Interests: His work bridges statistical methodology and life sciences, with a focus on developing and applying advanced statistical methods such as sparse models, multivariate analysis, and optimization techniques. Key areas include dimension reduction in high-dimensional count data, probabilistic graphical models, and applications in ecology, genomics, and metagenomics. He emphasizes reproducible research practices, recently developing a Quarto template for HCERES reports integrating LaTeX and Pandoc. Recent Contributions: Recent work includes advancements in the Poisson lognormal model, stability selection via the sharp R package, and Python tools like pyPLNmodels for analyzing multivariate count data. His research often addresses challenges in ecological network inference, scRNA-seq analysis, and genomic prediction. Labs & Teams: Based at UMR MIA Paris-Saclay, he contributes to the SOLsTIS team, advancing computational methods for biological data analysis. The lab relocated to the new Palaiseau campus in 2022, fostering interdisciplinary collaborations.

• Statistics
• Probability
• Markov Chain Monte Carlo
• +۶ مورد دیگر

Professor Yves Atchade is a faculty member at the Department of Mathematics and Statistics at Boston University, holding the Duan Family Faculty Fellow of Data Science position. He leads the FORMES research group focused on statistical methods for environmental sciences. His work bridges probability, high-dimensional computation, and applied environmental modeling. Current projects include large-scale Bayesian inference , deep learning for rainfall prediction in West Africa , and inverse problems in remote sensing . Collaborations with institutions like ENSEA, Abidjan and the TAHMO Foundation drive environmental sensor network deployments in Côte d'Ivoire. His research emphasizes MCMC algorithm design and unbiased Monte Carlo methods , with applications spanning environmental science, machine learning, and high-dimensional statistics. Recent publications explore unbiased MCMC , cyclical sampling , and Bayesian PINNs . Scientific contributions include: Unbiased MCMC with Couplings (2020) High-dimensional Bayesian Computation (2018) Efficiency bounds in semiparametric models (2020) Adaptive MCMC Confidence Intervals (2016) He actively mentors students and postdocs, with methodological developments in Markov chain theory , stochastic optimization , and Monte Carlo variance reduction . The FORMES group welcomes inquiries from prospective researchers via email at atchade@bu.edu .

Richard Zemelمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Statistics
• +۶ مورد دیگر

Richard Zemel is the Trianthe Dakolias Professor of Engineering and Applied Science in the Department of Computer Science at Columbia University. He leads the NSF AI Institute for ARtificial and Natural Intelligence (ARNI). His research focuses on machine learning, artificial intelligence, statistics, neuroscience, and cognitive science with recent emphasis on robust AI, few-shot learning, algorithmic fairness, and continual learning. Education details are not explicitly listed, but his career includes roles as director of an NSF institute and extensive academic contributions. His research group includes active and former students/postdocs now in academia and industry roles at institutions like Google, DeepMind, NVIDIA, and universities globally. Key Research Themes: Fairness in AI, causal inference, generative models, and neural network architectures. Labs/Teams: ARNI Institute (NSF-funded), Zemel Research Group at Columbia. Advising and grants include supervision of over 40 students/postdocs and leadership in multi-institutional AI initiatives. His work spans theoretical foundations to real-world applications in healthcare, ethics, and computational neuroscience.

Maryclare Griffinمشاهده پروفایل

استادیار

• Statistical Inference
• Time Series Analysis
• Variable Importance
• +۳ مورد دیگر

Maryclare Griffin is an Assistant Professor in the Department of Mathematics and Statistics at the University of Massachusetts Amherst. Her research develops statistical methods for complex data analysis, with emphasis on time series modeling, variable importance measures, Bayesian computation, and interpretable machine learning. Current methodological work includes adaptive overdifferencing for non-stationary processes, mixture representations for Bayesian simulation, and structured shrinkage priors for high-dimensional inference. Applications span clinical genomics, environmental monitoring, and biophysical trend analysis. Recent publications demonstrate innovations in time series model identification, optimization algorithms for regularized regression, and variable importance frameworks for nonlinear models. Collaborative work extends to metastasis prediction in melanoma and lightning strike pattern modeling. Holds a PhD in Statistics from the University of Washington and completed postdoctoral training at Cornell's Center for Applied Mathematics. Maintains active research in computational statistics and model interpretability.

George Constableمشاهده پروفایل

مدرس ارشد

• Mathematical Biology
• Stochastic Modelling
• Population Genetics
• +۴ مورد دیگر

Dr. George Constable is a Senior Lecturer in Mathematical Biology at the University of York's Department of Mathematics. He holds an MSci from the University of Glasgow and a PhD from the University of Manchester, with postdoctoral research conducted at Princeton University and the University of Zurich. His research is funded by a Leverhulme Early Career Fellowship. Constable's research focuses on theoretical frameworks for understanding eco-evolutionary patterns through mathematical models, particularly examining how stochasticity influences population dynamics. Core research themes include: Population genetics and evolution of sexual reproduction Public goods theory and multicellularity Stability mechanisms in complex ecosystems Mating type evolution in isogamous species Demographic noise effects on population dynamics His recent publications demonstrate strong emphasis on evolutionary theory, particularly reproductive strategies like parthenogenesis and facultative sex, with advanced stochastic modeling approaches applied across biological scales from cellular dynamics to ecosystem stability. Article keywords frequently involve evolutionary biology, theoretical ecology, and complex systems. Honors include: Leverhulme Early Career Fellowship (2017-2020) He currently supervises doctoral candidate Xiaoyuan Liu and leads the Mathematical Biology research group at York, investigating noise-driven phenomena in biological systems through analytical and computational methods.

Joshua Loyalمشاهده پروفایل

استادیار

• Statistical network analysis
• Bayesian inference
• Machine learning
• +۲ مورد دیگر

Joshua Loyal is an Assistant Professor in the Department of Statistics at Florida State University. His research focuses on statistical network analysis, Bayesian inference, machine learning, data science, and statistical computing. Education: Ph.D. in Statistics, University of Illinois at Urbana-Champaign M.S. in Physics, Yale University B.S. in Physics and Mathematics, Duke University His research interests span statistical methods for network analysis and computational statistics, with applications ranging from social networks to environmental science and particle physics. His work develops novel methodologies for analyzing complex, high-dimensional data structures. His publications demonstrate a consistent focus on developing statistical models for network data, with recent work advancing Bayesian nonparametric methods and dimension reduction techniques for dynamic networks. He leads research projects applying statistical network analysis to diverse domains including arctic ecology, pandemic modeling, and particle physics.

Jian Caoمشاهده پروفایل

استادیار

• Computational Statistics
• Gaussian Process Regression
• Spatial Statistics
• +۴ مورد دیگر

Jian Cao is an Assistant Professor in the Department of Mathematics at the University of Houston, specializing in Computational and Spatial Statistics. He earned his Ph.D. in Statistics from KAUST under Dr. Marc Genton and completed postdoctoral work at Texas A&M University under Dr. Matthias Katzfuss. His research focuses on scalable Gaussian Process (GP) regression methods, including Vecchia approximations, truncated multivariate normal distributions, and applications in climate and agricultural science. Education includes a B.S. in Mathematics from University of Science and Technology of China (2014), a Master in Finance from Shanghai Jiaotong University (2016), and a Ph.D. in Statistics from KAUST (2020). Research interests emphasize computational efficiency in statistical modeling, with contributions to scalable algorithms for high-dimensional data. Key themes include Gaussian processes, multivariate normal probabilities, and low-rank matrix methods. His work bridges theory and application, addressing challenges in environmental and agricultural data science. Notable awards include the Al-Kindi Statistics Student Research Award (2020) and Best Student Paper (2019). His publications span journals like the Journal of the American Statistical Association and conferences such as ICML, focusing on scalable statistical methods and software development (e.g., tlrmvnmvt package). Teaching responsibilities include courses on statistics for the sciences and inferential statistics at the University of Houston. His software contributions prioritize high-performance computing solutions for large-scale geospatial and statistical problems.

Jenny Häggströmمشاهده پروفایل

دانشیار

• Register-based research
• Causal inference
• Observational studies
• +۸ مورد دیگر

Jenny Häggström is an Associate Professor at the Umeå School of Business, Economics and Statistics (USBE), affiliated with the Department of Statistics at Umeå University. Her work focuses on method development and applications in register-based research , with expertise in causal inference, observational studies, and statistical modeling for medical and public health applications. Academic Affiliation: Umeå School of Business, Economics and Statistics (USBE), Umeå University Research Pillars: Causal inference, machine learning, health promotion, and high-dimensional data analysis Email: jenny.haggstrom@umu.se Research Themes : Häggström’s research bridges statistical methodology with public health and clinical applications. She leads the Stat4Reg research group, focusing on: Causal inference in observational time-to-event settings Development of risk prediction models for surgical outcomes Evaluation of universal health interventions like the Salut Programme Propensity score methods and bandwidth selection in kernel equating Recent Article Trends : Her recent work emphasizes: Medical statistics for surgical complications and survival analysis Machine learning applications in stroke outcome prediction Public health research on immigrant health disparities and preventive programs Methodological advancements in causal inference with high-dimensional data Projects & Grants : Active research projects include: Methodological development for marginal causal effects in time-to-event data (2019-2022) Evaluating the Salut Programme’s health and economic impact (2018-2020) Improving causal effect estimation in observational studies (2014-2016) Labs & Teams : She leads the Stat4Reg research group, collaborating on register-based studies and statistical methodology.

• Bayesian statistics
• Spatial statistics
• Shape analysis
• +۳ مورد دیگر

Qiwei Li is an Assistant Professor of Statistics in the Department of Mathematical Sciences at The University of Texas at Dallas. He holds a Ph.D. in Statistics from Rice University (2016), an M.A. in Statistics from Rice University (2015), an M.Phil. in Information Engineering from The Chinese University of Hong Kong (2010), a B.Sc. in Economics from Peking University (2009), and a B.Eng. in Electronic Engineering from Tsinghua University (2008). His research focuses on developing AI-powered Bayesian statistical methodologies and machine learning approaches to analyze high-dimensional, spatial, and shape data. Key areas include Bayesian clustering, spatial transcriptomics, microbiome community detection, and integrative omics analysis. He is actively involved in advancing methodologies for spatial molecular profiling and disease-relevant gene discovery. Recent publications highlight contributions to spatial transcriptomics integration, microbiome analysis, and health outcome studies. His work bridges statistical theory with biomedical applications, addressing challenges in genomics, imaging, and clinical data interpretation. Dr. Li is seeking highly motivated Ph.D. and undergraduate students to join his research group, offering financial support. He emphasizes interdisciplinary collaboration and innovation in statistical methodologies for solving complex biomedical problems.

• Econometrics
• Industrial Organization
• Antitrust
• +۴ مورد دیگر

Dong Li is a Professor of Economics at the University of Texas at Dallas (UT Dallas), affiliated with the School of Economic, Political and Policy Sciences. His research focuses on econometrics, industrial organization (particularly antitrust issues), financial economics, and the Chinese economy. He holds a Ph.D. in Economics from Texas A&M University (2000), an M.A. in Quantitative Economics from Huazhong University of Science & Technology (1994), and a B.A. in Quantitative Economics from the same institution (1991). Li's work emphasizes methodological contributions to panel data models, spatial econometrics, and semiparametric estimation techniques. His recent studies include analyses of cartel behavior in agricultural markets, military aid's impact on terrorism, and the implications of securities transaction taxes in emerging markets. His research bridges theoretical econometrics with applied policy questions, particularly in antitrust and financial regulation contexts. His articles span topics ranging from Bayesian auction analysis to China's economic policies, showcasing interdisciplinary rigor. While no specific awards are noted, his extensive publication record reflects sustained academic influence. His research often addresses practical economic challenges, such as optimizing college admissions systems and evaluating currency valuation impacts on macroeconomic variables like inflation and output growth.

Oliver Schulteمشاهده پروفایل

استاد

• Machine Learning
• Statistical Learning for Relational Databases
• Computational Game Theory
• +۴ مورد دیگر

Oliver Schulte is a Professor and School Director at the School of Computing Science, Simon Fraser University. His research focuses on Machine Learning, particularly in relational databases and computational game theory. He holds a Ph.D. from Carnegie Mellon University (1997) and has held academic roles since 1997, including Adjunct Professorships at the University of Alberta. His work includes foundational contributions to learning theory, generative graph models, and sports analytics. Awards include the NSERC Discovery Award and Best Paper Awards in AI conferences. He leads the Structured Machine Learning Lab and collaborates with institutions like SportLogiq. His teaching spans database systems, AI, and societal impacts of technology. Education: Ph.D. (Logic & Computation, CMU, 1997), M.Sc. (CMU, 1993), B.Sc. (Computing Science, U Toronto, 1992). Research Interests: Machine learning for relational data, Bayesian networks, reinforcement learning in sports, computational game theory, and formal epistemology. Recent work includes subgraph prediction, rule-enhanced graph learning, and privacy-aware graph generation. Awards & Grants: Over $500K NSERC Strategic Project Award (2020s), Best Paper Awards, and leadership in grants with industry partners like SportLogiq. His research bridges theory and applications, including hockey analytics and predictive analytics labs.

• Kernel Methods
• Gaussian Processes
• Machine Learning
• +۳ مورد دیگر

Motonobu Kanagawa is an Assistant Professor (Maitre de Conférences) in the Data Science Department at EURECOM since September 2019. Previously, he was a researcher at the University of Tuebingen and the Max Planck Institute for Intelligent Systems in Germany under Prof. Philipp Hennig (2017–2019), and completed his PhD at the Institute of Statistical Mathematics in Tokyo under Prof. Kenji Fukumizu (2013–2016). Education: Doctorate in Statistical Science (2016), Graduate University for Advanced Studies / Institute of Statistical Mathematics, Japan Master in Computer Science (2013), Nara Institute of Science and Technology, Japan Research Interests: His work focuses on kernel methods, Gaussian processes, and machine learning for enhancing computer simulations. Key areas include statistical validation of simulation models, applications in climate, disaster, economic, and financial analysis, and interpretable distribution comparison. He emphasizes bridging theory and practice in probabilistic numerics and Bayesian optimization. Publications Trends: His recent work addresses challenges in Bayesian quadrature, Gaussian process scaling, covariate shift adaptation, and pension system risk analysis. He frequently explores kernel-based approaches for computational efficiency and interpretability. Awards: Chris Daykin Prize (2022) from the International Actuarial Association for work on pension system risk analysis Grants & Leadership: He chairs a position at 3IA Cote d'Azur (2021) and organizes the ProbNum 2025 conference. His advisory roles include editorial board membership at the Journal of Machine Learning Research (JMLR) and reviewing for top venues like TPAMI, NeurIPS, and ICML. Labs & Collaboration: Active in the Data Science Department at EURECOM,他曾领导Max Planck的Probabilistic Numerical Computing Group，并与EDHEC等机构合作研究养老金系统风险。他的研究团队通过贝叶斯优化和核方法推进跨领域应用。

• Statistics
• Machine Learning
• Simulation
• +۵ مورد دیگر

Motonobu Kanagawa is an Assistant Professor (Maitre de Conférences) in the Data Science Department at EURECOM in France since 2019. Previously, he held research positions at the University of Tübingen and the Max Planck Institute for Intelligent Systems in Germany under Prof. Philipp Hennig. He earned his PhD in 2016 from the Institute of Statistical Mathematics in Tokyo under Prof. Kenji Fukumizu. His research focuses on statistical methodologies for complex systems simulation, including reliability validation of simulators, Gaussian processes, Bayesian optimization, and kernel methods. Recent work explores variable selection in distribution comparison and covariate shift adaptation. Key achievements include organizing the inaugural ProbNum 2025 conference, receiving the Chris Daykin Prize (2023) for pension system analysis with Bayesian optimization, and securing a chair position at the 3IA Côte d'Azur AI institute (2021). He reviews for leading journals like JMLR and conferences such as NeurIPS and ICML. Publications span topics like k-NN regression optimization, Gaussian process scale parameter estimation, and counterfactual mean embeddings. His work bridges theoretical statistics and applied machine learning, emphasizing computational efficiency and uncertainty quantification.