جست‌وجوی استاد و پژوهشگر

Gwendolyn Eadieمشاهده پروفایل

استادیار

• Astrostatistics
• Bayesian hierarchical modelling
• Milky Way structure and dynamics
• +۹ مورد دیگر

Gwendolyn Eadie is an Assistant Professor jointly appointed between the Department of Astronomy & Astrophysics and the Department of Statistical Sciences at the University of Toronto. She leads the Astrostatistics Research Team (ART) and chairs the American Astronomical Society's Working Group on Astroinformatics & Astrostatistics and the American Statistical Association's Astrostatistics Interest Group. Her research focuses on astrostatistics, applying advanced statistical methods to astronomical problems such as Milky Way dynamics, globular clusters, and Fast Radio Bursts (FRBs). She earned her PhD in Physics & Astronomy from McMaster University (2017), an MSc from Queen's University, and a BSc from Simon Fraser University. Education: PhD in Physics & Astronomy, McMaster University (2017) MSc in Physics, Queen's University (2013) BSc in Physics, Simon Fraser University (2010) Her research interests span astrostatistics, Bayesian hierarchical modeling, and time series analysis. Notable works include studies on Milky Way mass estimation using globular clusters and developing methods for FRB localization and analysis. She has received the 2018 J.S. Plasket Medal for her doctoral research on Milky Way dynamics. Advising and Grants: Supervised students include Amanda Cook (McGill Banting Fellow), Aarya Patil (Max Planck Institute postdoc), and Jeff Shen (Princeton PhD candidate) Directed the ART group's interdisciplinary collaborations Labs/Teams: Co-leads the Astrostatistics Research Team (ART) at UofT, and collaborates with CHIME/FRB and the eScience Institute.

Jeff Gillمشاهده پروفایل

استاد

• Quantitative Methodology
• Bayesian Statistics
• Political Behavior
• +۴ مورد دیگر

Jeff Gill is a Distinguished Professor of Government and Mathematics & Statistics at American University, serving as inaugural Director of the School of Public Affairs' Center for Data Science and member of the Center for Neuroscience and Behavior. His academic background includes a Postdoctoral Fellowship from Harvard University, PhD from American University, MBA from Georgetown University, and BA in Mathematics from UCLA. Gill's research applies Bayesian modeling and computationally intensive methods to social science methodology, political behavior, and health data analysis, with specific focus on physiology, blood circulation, pediatric traumatic brain injury, and epidemiological measurement using MCMC and stochastic optimization. Honors include Distinguished Professor status at American University and Inaugural Fellowship in the Society for Political Methodology, where he also served as past-President. He has been Editor-in-Chief of Political Analysis since 2018. As Center for Data Science Director, Gill develops federal research partnerships, supports faculty/student projects, and builds infrastructure for complex datasets across campus.

• Machine Learning
• Bayesian Nonparametrics
• Statistical Theory
• +۳ مورد دیگر

Peter Orbanz is a Professor of Machine Learning at the Gatsby Computational Neuroscience Unit, University College London (UCL). He previously held roles as Assistant and Associate Professor in the Department of Statistics at Columbia University, and completed postdoctoral research at the University of Cambridge under Zoubin Ghahramani. His research focuses on machine learning, Bayesian nonparametrics, symmetry in statistical models, and dynamics. He has spent sabbaticals at the Isaac Newton Institute, Microsoft Research New England, and UC Berkeley. Orbanz earned his PhD in 2008 from ETH Zurich under Joachim M. Buhmann, with a thesis on Bayesian nonparametric models. His work bridges theoretical foundations of machine learning with practical applications, emphasizing symmetry properties and scalable inference methods. Current research includes causal uncertainty quantification, Gaussian process dynamics, and equivariant neural networks. He teaches advanced courses on probabilistic learning, Bayesian nonparametrics, and statistical theory at UCL and Columbia. His research group at UCL includes PhD students Hugh Dance, Kevin H. Huang, Vasco Portilheiro, and Vince Velkey. Former advisees include Morgane Austern (Harvard), Benjamin Bloem-Reddy (UBC), and Victor Veitch (Chicago). Orbanz has authored influential papers on topics such as exchangeable random structures, Gaussian universality, and scalable MCMC methods. His work often integrates concepts from probability theory, statistical physics, and computational neuroscience. He maintains active collaborations with institutions worldwide and contributes to open-source tools for Bayesian modeling.

James Trippمشاهده پروفایل

پژوهشگر

• Cognitive Science
• Data Visualization
• Statistical Analysis
• +۳ مورد دیگر

James Tripp is a Senior Research Software Engineer at the University of Warwick's Centre for Interdisciplinary Methodologies (CIM). He holds a BSc(Hons) and PhD in Psychology from the University of Warwick. His work spans statistical analysis, software development, and technical support for research and teaching infrastructure at CIM. He specializes in tools like R, Python, and geospatial analysis, and leads workshops on digital methods and visualization. Tripp has developed software packages such as Backfillz (for MCMC visualization), LE-CAT (lexicon-based analysis tool), and contributed to projects like WAYS (Turing-funded). His technical roles include managing servers hosting Jupyter Notebooks, ShinyProxy, and data capture systems. He collaborates with academic partners like the Media of Cooperation Group at the University of Siegen. His research interests bridge cognitive science and computational methods, with a focus on interdisciplinary digital methodologies.

Filippo Paganiمشاهده پروفایل

پژوهشگر ارشد

• Markov Chain Monte Carlo
• Variational Inference
• Tempering
• +۷ مورد دیگر

Filippo Pagani is a Postdoctoral Research Fellow at the University of Warwick, working on the OCEAN grant under Professors Gareth Roberts and Adam Johansen. His research focuses on advanced statistical methodologies and their applications in diverse fields such as healthcare and astrophysics. His research interests include: Markov Chain Monte Carlo (MCMC) Variational Inference Tempering and Irreversibility techniques Bayesian Statistics and Machine Learning Applications in disease subtyping, biomarker discovery, and inverse problems Recent publications explore topics such as Bayesian outcome-guided models for precision medicine, numerical methods for MCMC sampling (e.g., ZigZag algorithms), and applications in astrophysical data analysis. Notable works include contributions to disease subtyping via variational mixtures and modeling the impact of the COVID-19 pandemic on healthcare systems. He is affiliated with the OCEAN grant and has contributed to short-term forecasting models during the early stages of the pandemic. No formal advisees are listed.

Dootika Vatsمشاهده پروفایل

دانشیار

• Markov Chain Monte Carlo (MCMC)
• Stochastic Simulation
• Output Analysis for Simulation
• +۳ مورد دیگر

Dootika Vats is an Associate Professor in the Department of Mathematics and Statistics at the Indian Institute of Technology, Kanpur. She holds a Ph.D. from the University of Minnesota, Twin-Cities, under Prof. Galin Jones, and previously served as an NSF Postdoctoral Fellow with Prof. Gareth Roberts at the University of Warwick. Her research focuses on Markov chain Monte Carlo (MCMC), output analysis for stochastic simulation, and stochastic optimization algorithms. She is an Associate Editor for Bayesian Analysis , Journal of Computational and Graphical Statistics , and Sankhya B , and has contributed to software development for statistical methods, including packages like qbld and mcmcse . Dr. Vats has advised students such as Dwija Kakkad, Saee Kamat, and Shlok Mishra, all of whom secured PhD admissions. Her recent work includes a project on seasonal adjustment methods for economic indices for India’s Ministry of Statistics. She is also a Faculty Associate at the International Center for Theoretical Sciences (ICTS) and actively participates in workshops and conferences, including BayesComp 2025 in Singapore and the MATRIX Institute Workshop in Australia. Her research outputs emphasize advancing MCMC methodologies, with contributions to convergence diagnostics, variance estimation, and algorithmic efficiency. She has published widely on topics such as Moreau-Yosida envelopes, lugsail lag windows, and Hamiltonian Monte Carlo techniques, showcasing her expertise in bridging theoretical and applied computational statistics.

• Statistical Modeling
• Machine Learning
• Bioinformatics
• +۶ مورد دیگر

Farhad Shokoohi is an Assistant Professor in the Department of Mathematical Sciences at the University of Nevada, Las Vegas (UNLV). His research focuses on statistical modeling, machine learning, and their applications in healthcare analytics, bioinformatics, and financial forecasting. He has contributed to advancements in Bayesian methods, small area estimation, and computational statistics, with particular emphasis on cancer genomics and epigenetic analysis. Key research interests include hybrid statistical models (e.g., SP-RF-ARIMA for energy load forecasting), hidden Markov models for genomic data analysis, and sparse estimation techniques in high-dimensional data. His work bridges theoretical statistics with practical applications in finance, healthcare, and policy evaluation. He has developed R packages like fmrs for finite mixture models, emphasizing computational tools for statistical inference. His articles reflect a blend of methodological innovation and domain-specific solutions, spanning topics such as differential methylation in colorectal cancer, dual-input deep learning for forex prediction, and trans-dimensional MCMC for genomic studies. While no awards or grants are explicitly listed in the provided texts, his interdisciplinary approach underscores contributions to both computational and applied statistical research.

Emily Foxمشاهده پروفایل

استاد

• Time Series Analysis
• Machine Learning
• Health Informatics
• +۲ مورد دیگر

Emily Fox is a Professor in the Departments of Statistics and Computer Science at Stanford University, with affiliations at the Wu Tsai Neurosciences Institute, Wu Tsai Human Performance Alliance, Bio-X, and Center for Artificial Intelligence in Medicine & Imaging (AIMI). Previously, she was Amazon Professor of Machine Learning at the University of Washington and Distinguished Engineer at Apple. Education includes: Ph.D. in Electrical Engineering and Computer Science from MIT (2009), E.E. from MIT (2008), M.Eng. from MIT (2005), and S.B. from MIT (2004). Research focuses on modeling complex time series in health applications, particularly from wearables and neuroimaging. Key areas include Bayesian nonparametrics, time series analysis, and developing statistical methods for high-dimensional data. Significant awards include: IMS Fellow, CZ Biohub Investigator, PECASE Award, Sloan Fellowship, ONR Young Investigator Award, and NSF CAREER Award. Advises doctoral students and postdocs in machine learning and statistics. Research supported by NSF, ONR, Amazon, and other grants. Leads research on health AI applications and develops novel statistical methods for large-scale data analysis.

Jery Russell Stedingerمشاهده پروفایل

استاد

• Hydrology
• Water Resources Engineering
• Risk Analysis
• +۶ مورد دیگر

Jery Russell Stedinger is a Professor in the Department of Civil and Environmental Engineering at Cornell University, affiliated with the College of Engineering. He holds a B.A. in Applied Mathematics from the University of California, Berkeley (1972), and a Ph.D. in Environmental Systems Engineering from Harvard University (1977). His career includes sabbaticals at institutions such as the U.S. Geological Survey, U.S. Army Corps of Engineers, and Technische Universität Wien. Stedinger’s research focuses on statistical methodologies in hydrology, flood frequency analysis, and water resource systems optimization. He has pioneered approaches to incorporate historical and paleoflood data into risk assessments and has contributed to national guidelines like Bulletin 17C for flood flow frequency. His work extends to geothermal resource assessment and energy systems engineering. Awarded the ASCE Ven Te Chow Award (2014) and elected to the National Academy of Engineering (2014), Stedinger has authored over 140 professional papers and textbooks, including co-authorship of the foundational Water Resource Systems Planning and Analysis . He teaches courses on uncertainty analysis (CEE 3040) and risk management (CEE 5970), mentoring students in systems engineering and environmental management. Stedinger serves on committees for dam safety, flood risk, and water resources policy, advising federal agencies like the USACE and Bureau of Reclamation. His contributions bridge academic research, policy, and practical applications in hydrology and sustainable engineering.

Ana Cukarskaمشاهده پروفایل

پژوهشگر

• Machine learning for dynamical systems
• Numerical aspects of machine learning algorithms
• Koopman operator theory
• +۳ مورد دیگر

Ana Cukarska is a PhD candidate at the Technical University of Munich (TUM) within the School of Computational Engineering and Science, Department of Computer Science. She joined TUM in November 2023 under the supervision of Prof. Felix Dietrich. Her academic background includes an M.Sc. in Computational Science and Engineering (2021-2023) and a B.Sc. in Mechanical Engineering (2017-2023). Research Interests: Ana focuses on machine learning applications in dynamical systems, with particular interest in numerical aspects of ML algorithms, Koopman operator theory, and its applications in crowd dynamics modeling. Her work bridges computational science and machine learning methodologies. Teaching Involvement: She has contributed to various courses at TUM, including Scientific Computing and Machine Learning , Machine Learning in Crowd Modeling & Simulation , and multiple iterations of Physics-Informed Machine Learning exercises.

Tong Xinمشاهده پروفایل

دانشیار

• Probability Theory
• Stochastic Optimization
• Machine Learning
• +۳ مورد دیگر

Tong Xin is Associate Professor in the Department of Mathematics at National University of Singapore. His research develops theoretical frameworks for high-dimensional stochastic systems, focusing on algorithmic stability in optimization and sampling methods. Core research areas include dimension-independent convergence guarantees for stochastic gradient descent, efficiency of ensemble Kalman methods in inverse problems, and replica exchange techniques for non-convex optimization. Recent work establishes robustness bounds for Markov Chain Monte Carlo variants and analyzes generalization errors in machine learning. Applications span uncertainty quantification in physical systems, statistical inference for non-i.i.d. data streams, and computational methods for large-scale inverse problems. Editorial roles include SIAM Journal of Uncertainty Quantification and Statistics and Computing.

Bret Largetمشاهده پروفایل

استاد

• Bayesian Statistics
• Evolutionary Biology
• Statistical Genetics
• +۳ مورد دیگر

Bret Larget is a Professor and Chair in the Department of Statistics and holds a joint appointment in the Department of Botany at the University of Wisconsin-Madison. He is affiliated with the School of Computer, Data & Information Sciences. His research focuses on Bayesian computational methods in evolutionary biology, statistical genetics, and data science education. Larget earned his Ph.D. in Statistics from the University of California, Berkeley, and has held academic positions since 2002 at UW-Madison. Education: Ph.D. Statistics (UC Berkeley, 1994), M.A. Statistics (UC Berkeley, 1991), B.Math. Mathematics (University of Minnesota, 1989) Research Interests: Bayesian phylogenetics, genome arrangement analysis, statistical methods in evolutionary genetics, and data science pedagogy He has led or co-led major grants totaling over $3 million from NSF and NIH, including projects on gorilla phylogeography and Enterobacteriaceae genome evolution. His work includes developing software tools like BUCKy for phylogenetic concordance analysis. Larget has advised numerous graduate students (e.g., Ruiyan Luo, Lancine Konate) and mentored undergraduates in research. He has taught courses in statistical methods, phylogenetics, and evolutionary genetics. Key Awards: Vilas Research Fellowship (2008–2010) Service: Served on university committees including the Biology Major Executive Committee and contributed to curriculum development in statistics and botany.

Geoff Nichollsمشاهده پروفایل

دانشیار

• Bayesian Inference
• Computational Statistics
• Monte Carlo Methods
• +۴ مورد دیگر

Geoff Nicholls is an Associate Professor of Statistics at the University of Oxford, affiliated with St Peter's College as a Fellow and Tutor. He holds a B.Sc. in Physics from the University of Canterbury (New Zealand) and a PhD in High-Energy Physics from the University of Cambridge. His research focuses on Bayesian inference, computational statistics, and Monte Carlo methods applied to interdisciplinary problems in geoscience, linguistics, genetics, and archaeology. Notable contributions include semi-modular Bayesian inference frameworks and embedded diachronic sense change models for ancient languages. He has advised students like Lorenzo Pacchiardi and Jessie Jiang, and collaborates on tools like the TraitLab package for binary tree-like data analysis. Key research areas include statistical methods for partial orders, biclustering forensic data, and scalable Bayesian techniques for high-dimensional problems. His work bridges theoretical developments with practical applications, such as modeling historical power dynamics using 12th-century documents and analyzing linguistic evolution in ancient Greek texts.

• Epidemiology
• Machine Learning
• Public Health
• +۵ مورد دیگر

Elizaveta Semenova is a Research Associate in the Department of Computer Science at the University of Oxford. Her work focuses on the intersection of artificial intelligence, epidemiology, and public health, with particular expertise in Bayesian modelling, spatial statistics, and disease surveillance systems. She develops advanced machine learning frameworks for addressing challenges in global health equity, including small area estimation, disease mapping, and pandemic response strategies. Her research spans computational methods for infectious disease modelling, AI-driven healthcare analytics, and the ethical application of foundation models to culturally diverse datasets. Notable contributions include frameworks for integrating mobile survey data with traditional epidemiological methods, and innovations in graph-based active learning for optimizing disease surveillance networks. Ms. Semenova collaborates with international groups such as the Country Data Author Group to analyze global health trends, and has pioneered techniques for quantifying health inequity impacts on disease transmission dynamics. Her methodological work includes advancements in probabilistic programming using tools like Numpyro, and scalable Bayesian inference through deep generative models.

Alexandru Voicuمشاهده پروفایل

دانشیار

• MCMC Methods
• Microeconometric Models
• Transition Economies
• +۳ مورد دیگر

Alexandru Voicu is an Associate Professor at the School of Business, College of Staten Island (CUNY), and a Doctoral Faculty member at the CUNY Graduate School. He teaches Microeconomics, Macroeconomics, Statistics, Econometrics, and Labor Economics in the Department of Economics. Education : BA, Polytechnical Institute (Bucharest, Romania) MA, Central European University (Prague, Czech Republic) PhD, Rutgers University Research Interests : Voicu focuses on applying MCMC methods to microeconometric models and analyzing transition economies. His work explores labor market dynamics, international trade impacts (e.g., NAFTA effects), and employment patterns in post-communist states. He also examines gender-based labor participation and policy implications of economic transitions. Publications Trends : His articles span topics like labor market impacts of childbirth, post-communist employment metrics, NAFTA’s economic effects, and computational modeling of green energy markets. Recent works (2021) address EU engagement in Eastern neighbors and technological applications in state-owned enterprises. Advising/Grants : No advising roles or grants explicitly mentioned in the text. Labs/Teams : No specific labs or collaborative teams noted.