جست‌وجوی استاد و پژوهشگر

• Mathematical Statistics
• Non-Euclidean Data Analysis
• Algebraic Statistics
• +۳ مورد دیگر

Andrew McCormack is an Assistant Professor in the Department of Mathematical and Statistical Sciences at the University of Alberta. His research focuses on mathematical statistics, with a particular emphasis on non-Euclidean data analysis, algebraic statistics, and information geometry. His research interests include: Non-Euclidean Data Analysis: Developing methodologies for data on manifolds and metric spaces. Algebraic Statistics: Applying nonlinear algebra to understand statistical models, especially graphical models. Tensor Decompositions: Exploring low-dimensional representations of high-dimensional data using matrices and tensors. Information Geometry: Studying statistical models through geometric lenses, incorporating ideas from optimal transport. Statistical Decision Theory: Investigating foundational approaches to evaluating statistical methods from both classical and Bayesian perspectives. Mccormack's recent work spans robust statistical methods, information geometry in covariance modeling, and the application of algebraic techniques to statistical problems. His contributions often bridge theory and computation, addressing challenges in high-dimensional and geometric data analysis. No scientific awards listed. No advisees or grants mentioned. Labs or teams are not specified in the provided information.

Anthony Constantinouمشاهده پروفایل

مدرس ارشد

• Causal Machine Learning
• Decision Systems
• Bayesian Networks
• +۶ مورد دیگر

Anthony Constantinou is a Senior Lecturer at Queen Mary University of London, part of the School of Electronic Engineering and Computer Science. He leads the MInDS Research Group and the Bayesian AI Lab, focusing on causal machine learning for decision-making under uncertainty. His research spans healthcare, defense, sports, economics, and gaming, with a strong emphasis on Bayesian networks and causal inference. Research Interests: Causal machine learning, decision systems, Bayesian networks, uncertainty quantification, and their applications in healthcare, military defense, sports analytics, and economics. He collaborates with academia and industry to advance AI-driven decision-making systems. Grants: Noted for securing funding such as the Engineering and Physical Sciences Research Council grant (EP/S001646/1) for Bayesian AI research. His work emphasizes practical applications in diverse fields through collaborative projects. Labs & Teams: Head of the MInDS Research Group and Bayesian AI Lab, fostering interdisciplinary research in AI and decision systems.

Kyle O'Keefeمشاهده پروفایل

استاد

• Global Navigation Satellite Systems (GNSS)
• Indoor Positioning and Navigation
• Ultra-wideband Ranging
• +۶ مورد دیگر

Kyle O'Keefe is a Professor in the Department of Geomatics Engineering at the University of Calgary's Schulich School of Engineering. He holds dual B.Sc. degrees in Geomatics Engineering (University of Calgary, 2000) and Honours Physics (University of British Columbia, 1997), and a Ph.D. in Geomatics Engineering (University of Calgary, 2004). He is a Professional Engineer (P.Eng.) registered with the Association of Professional Engineers and Geoscientists of Alberta since 2005. His research focuses on positioning and navigation technologies, including Global Navigation Satellite Systems (GNSS) advancements Ultra-wideband (UWB) ranging for vehicle/pedestrian navigation Indoor positioning using wireless signals Wearable sensor integration for biomechanics and navigation GNSS spoofing detection and cybersecurity Notable projects include: Development of UWB-augmented GNSS for RTK surveying (2007–present) Wearable sensor systems for rowing/kayaking motion analysis (2017–present) CanX-2 nanosatellite GPS receiver operations (2008) Igliniit project with Inuit hunters for Arctic environmental monitoring (2006–2009) Multi-constellation GNSS evaluation across 20+ years He has received prestigious awards including the Michael Richey Medal (2011) and multiple Best Paper Awards at IPIN and ION conferences. His teaching includes courses like Advanced GNSS Theory and Wireless Location. Active in professional organizations, he co-edits special journal issues and advises industry on emerging navigation technologies.

Quan Zhouمشاهده پروفایل

استادیار

• Markov chain Monte Carlo sampling
• Bayesian methodology
• Variable selection
• +۷ مورد دیگر

Quan Zhou is an Assistant Professor in the Department of Statistics at Texas A&M University, part of the College of Arts & Sciences. His research focuses on developing advanced sampling methods, particularly Markov chain Monte Carlo (MCMC) algorithms, with applications in Bayesian methodology, variable selection, stochastic optimization, and statistical genetics. He holds a BS from Fudan University and a PhD from Baylor College of Medicine, followed by a postdoctoral fellowship at Rice University. He teaches courses such as Mathematical Probability, Multivariate Analysis, and Advanced Stochastic Processes. Notable contributions include work on informed MCMC samplers, Schrödinger bridge theory, and high-dimensional structure learning. He advises PhD students like Hyunwoong Chang (now at UT Dallas) and Guanxun Li (Beijing Normal University). Active in academic service, he served as President of the Southeastern Texas Chapter of the American Statistical Association (SETCASA).

Katherine B. Ensorمشاهده پروفایل

استاد

• Data Science
• Environmental Statistics
• Computational Finance
• +۹ مورد دیگر

Katherine B. Ensor is the Noah G. Harding Professor of Statistics at Rice University's George R. Brown School of Engineering and Computing. She serves as Director of the Center for Computational Finance and Economic Systems (CoFES) and previously led the Kinder Institute Urban Data Platform (UDP). Her expertise spans statistical methods for complex problems in public health, environmental science, and finance. Ensor has a 35-year academic career at Rice, including 14 years as Department Chair. She pioneered wastewater surveillance for pathogens, expanding to monitor 29 communicable illnesses through Houston Wastewater Epidemiology. Education: Ph.D. in Statistics (Texas A&M University), M.S. and B.S.E. in Mathematics (Arkansas State University). Research focuses on high-dimensional time series, spatial processes, Bayesian methods, and computational finance. She leads interdisciplinary initiatives, such as the UDP providing urban data for Greater Houston studies. Research interests include data science applications in public health, environmental analytics, and quantitative finance. Her work bridges academia and community, addressing issues like flood impacts via the Texas Flood Registry and asthma surveillance through collaborations with Houston Health Department. Ensor chairs the National Academies committee on Statistics in Science and Engineering and holds leadership roles in AAAS and ASA. Awards include the ASA Founders Award (2024), AAAS Fellowship (2013), and Texas A&M's Distinguished Former Students honor. She advises on national boards, including NSF's IPAM and ABET's CSAB. Her grants and collaborations drive innovations in statistical methods for societal challenges. Labs/Teams: Co-directs Houston Wastewater Epidemiology and Rice Environmental Statistics & Health (RESH) group with Dr. Loren Hopkins. Active in CoFES, UDP, and the Kinder Institute's urban data initiatives.

Seth Sullivantمشاهده پروفایل

استاد

• Algebraic Statistics
• Computational Algebra
• Combinatorics
• +۳ مورد دیگر

Seth Sullivant is a Distinguished Professor in the Department of Mathematics at North Carolina State University (NC State), within the College of Sciences. His research focuses on algebraic statistics, computational and combinatorial algebra, and mathematical phylogenetics. He holds a Ph.D. in Mathematics from the University of California, Berkeley (2005). His expertise spans interdisciplinary areas, including algebraic approaches to statistical problems, combinatorial methods in phylogenetics, and the development of algebraic tools for graphical models. He is affiliated with research groups in Algebra and Combinatorics, Mathematical Biology, and Symbolic Computation at NC State. Recent research trends in his publications emphasize the application of algebraic geometry and combinatorics to statistical models, phylogenetic network analysis, and identifiability problems in systems biology. His work bridges abstract mathematical concepts with practical statistical methodologies, addressing challenges in data analysis and model interpretation. No specific scientific awards are listed in the provided texts. His advising and grant activities are not detailed here, though his extensive publication record suggests active research collaborations. He is part of research teams focused on advancing algebraic methods in statistics and computational biology.

Simon Spencerمشاهده پروفایل

استاد

• Bayesian Inference
• Epidemiology
• Statistical Modeling
• +۳ مورد دیگر

Simon Spencer is a Professor of Statistics at the University of Warwick, affiliated with the Zeeman Institute for Systems Biology and Infectious Disease Epidemiology Research (SBIDER) and the Warwick Analytical Sciences Centre (WASC). His research focuses on Bayesian inference applied to epidemiology, stochastic epidemic models, and statistical methods for analytical science. He has held previous positions at the University of Nottingham and Massey University in New Zealand. His teaching includes advanced courses such as CH923: Statistics for Data Analysis , ST925: Graduate Topics in Statistics , and MA4M1/MA6M1: Epidemiology by Example . His research group currently includes PhD students Matthew Adeoye and Richard Haughey, and MSc students Olli Smith and Sangavi Pirabakaran. He collaborates extensively with global health institutions on projects addressing infectious disease modeling and public health policy. Spencer’s work bridges statistical methodology and real-world applications, with a focus on outbreak detection, model comparison, and the integration of geostatistical data with transmission models. His recent contributions include frameworks for lymphatic filariasis elimination projections and analyses of HIV transmission dynamics in Uganda. He actively contributes to interdisciplinary research in systems biology and analytical chemistry, leveraging advanced statistical techniques to address complex health challenges.

Cristian Román-Palaciosمشاهده پروفایل

استادیار

• Phylogenetics
• Biodiversity Drivers
• Paleoclimatic Reconstructions
• +۷ مورد دیگر

Cristian Román-Palacios is an Assistant Professor in the Department of Ecology and Evolutionary Biology at the University of Arizona, where he also serves as Coordinator and Advisor for the Master of Science in Data Science (MSDS) and Master of Science in Information Systems (MSIS) programs. He is a core faculty member in Artificial Intelligence and Machine Learning, Data Management, Analysis and Visualization, and Environmental, Health and Biological Sciences. Education: PhD in Ecology and Evolutionary Biology, University of Arizona (2020) BS in Biology, Universidad del Valle, Colombia (2015) His research lies at the intersection of phylogenetics, biodiversity modeling, and machine learning, focusing on large-scale biodiversity patterns, the impacts of climate change on species survival, and the development of statistical tools for paleoclimatic reconstructions. He employs computational and data-intensive methods to explore evolutionary and ecological questions across diverse taxa. His recent publications (2024–2025) demonstrate a strong trend toward interdisciplinary research, combining computational biology, geochemistry, climate science, and open-source software development. Key themes include biodiversity informatics (e.g., Animal Culture Database), paleoclimatic modeling (e.g., clumped isotope thermometry), reproducibility in science, and tools for collaborative research (e.g., LabOps, SSARP). His work increasingly integrates data science with biological and environmental applications. Scientific Contributions: Published over 25 peer-reviewed papers, many as first author Research featured in Science News, Popular Science, CNN, USA Today Developed open-source tools: phruta , treedata.table , SSARP , LabOps Cristian advises graduate students through the infosci-msadvise@arizona.edu email and Calendly appointments. He was previously a staff researcher at UCLA’s Tripati Lab. He leads initiatives such as the Southwest Center on Resilience for Climate Change and Health and promotes inclusive, collaborative science through online toolkits and leadership ecosystems aimed at addressing climate and social inequities. His lab, the Román-Palacios Lab, and involvement with the Data Diversity Lab reflect his commitment to open, reproducible, and equitable research practices in data-intensive biology.

Xin Gaoمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Statistical Genetics
• +۵ مورد دیگر

Xin Gao is a Professor in the Department of Mathematics and Statistics at York University, Toronto. His research focuses on Artificial Intelligence , Machine Learning , and Statistical Genetics , with applications in biomedical data analysis and planetary science. He leads the Artificial Intelligence and Machine Learning Lab , which has developed impactful tools like an online Type 2 Diabetes risk predictor using logistic regression and Mars rock composition analyzers for NASA. His methodological work includes penalized composite likelihood and multi-task feature learning , implemented in R packages FusionLearn and lassoGEE . Scientific Awards NSERC Discovery Acceleration Award ($120,000, 2018-2020) Key Software Contributions FusionLearn : Correlated multi-task feature learning lassoGEE : High-dimensional clustered/longitudinal data analysis Notable Collaborations Vector Institute (AI scholarship mentoring) Fields Institute (committee roles) International genomic data integration projects

• Neuroscience
• Computational Modeling
• Meditation
• +۵ مورد دیگر

Giuseppe PAGNONI is an Associate Professor at the Department of Biomedical, Metabolic and Neurosciences of Università di Modena e Reggio Emilia . His research spans neuroscience , computational modeling , meditation , and brain network analysis . He teaches courses such as Neural Systems Modeling for Bioengineering students and Human Physiology and Pathophysiology in Medicine programs, with a focus on predictive coding , default mode network , and emotional valence processing . Pagnoni has contributed to ENIGMA-Meditation , a global initiative exploring meditation's neuroscientific basis, and developed sparse Bayesian models for analyzing brain network individual differences. His work on Alzheimer's disease investigates anosognosia mechanisms through resting-state fMRI, while other studies examine ghrelin's role in obesity-related reward processing and oxytocin's effects on social anxiety . He employs dynamic causal modeling and predictive processing frameworks to study cognitive effort, mind wandering, and neuromorphic learning. His methodological expertise includes functional MRI , independent component analysis , and computational simulations .

Sara Arenaمشاهده پروفایل

استاد

• Biomechanics
• Cooperative Learning Strategies
• Problem-Based Learning
• +۷ مورد دیگر

Sara Arena is a Collegiate Professor in the Department of Biomedical Engineering and Mechanics at Virginia Tech , where she also serves as Associate Department Head for Curriculum and Course Coordination . Her work spans biomechanics research and innovative engineering education methods. Education: Ph.D., M.S., and B.S. from Virginia Tech Research Interests: Focus on human movement biomechanics, particularly balance recovery and postural stability, combined with educational research emphasizing cooperative and problem-based learning strategies in engineering education. Current projects include modeling human movement dynamics and improving remote teaching methodologies. Scientific Awards: Leader in Teaching, BEAM (2019, 2023) National Defense Science and Engineering Graduate Fellowship (2008-2011) Panelist for NSF and Department of Defense Key Contributions: Developed novel methods for assessing landing kinematics outside laboratory settings, explored friction requirements in aging populations, and pioneered virtual conferencing tools for problem-based learning. Her work has been published in journals like Annals of Biomedical Engineering and Applied Ergonomics . Collaborations: Partnered with institutions including Virginia Tech, American Society of Engineering Education, and multidisciplinary teams focused on biomedical engineering education and clinical biomechanics.

Gert Zöllerمشاهده پروفایل

دانشیار

• Statistical seismology
• Statistical models for earthquakes
• Physical models for natural disasters
• +۳ مورد دیگر

Gert Zöller is an Associate Professor of Applied Mathematics at the University of Potsdam, specializing in statistical seismology and mathematical modeling of earthquake processes. He has held this position since 2018, following a period from 2008-2018 as a Research Associate and Lecturer at the Institute of Mathematics at the University of Potsdam. His research focuses on statistical and physical models for earthquakes and other natural disasters, seismic hazard assessment, and extreme value statistics. Dr. Zöller earned his Diploma in Physics from Rheinische Friedrich-Wilhelms-University Bonn in 1995, his Doctorate (Dr. rer. nat.) from the University of Potsdam in 1999, and completed his Habilitation (Dr. rer. nat. habil.) in 2006. His academic journey included visiting scholar positions at the University of Southern California and the University of California, Santa Barbara in 2005. He has been actively involved in several major research initiatives including the DFG Collaborative Research Center 1294 (Data Assimilation) since 2017 and the DFG Graduate School NatRiskChange (Natural hazards and risks in a changing world) from 2015-2024. His research centers on developing sophisticated statistical models for earthquake forecasting, particularly focusing on the Groningen gas field in the Netherlands where induced seismicity has been a significant concern. Dr. Zöller's work integrates physics-based models with statistical approaches to improve seismic hazard assessment, with recent publications exploring Bayesian methods, Gaussian process modeling, and spatio-temporal analysis of earthquake sequences. His publications span top journals including Journal of Geophysical Research, Geophysical Journal International, and Bulletin of the Seismological Society of America. Dr. Zöller serves as a reviewer for numerous prestigious scientific journals including Science, Geophysical Research Letters, and Journal of Geophysical Research. He was Associate Editor of Nonlinear Processes in Geophysics from 2006-2014 and served as Scientific Officer for 'Earthquake Hazards' in the European Geosciences Union from 2010-2014. His professional memberships include the Seismological Society of America, American Geophysical Union, and European Geosciences Union. Currently, Dr. Zöller teaches 'Mathematics II for Economists' and serves on the Mathematics Examination Board at the University of Potsdam. His ongoing research continues to contribute significantly to the field of statistical seismology and earthquake hazard assessment, with publications extending into 2025.

Christopher L H Wredeمشاهده پروفایل

استاد

• Nuclear Astrophysics
• Experimental Nuclear Physics
• Low-Energy Nuclear Physics
• +۳ مورد دیگر

Professor Christopher L H Wrede is a tenured faculty member at the Department of Physics and Astronomy , Michigan State University , and leads experimental research at the Facility for Rare Isotope Beams (FRIB) . His work bridges nuclear physics and astrophysics , focusing on beta decays of proton-rich nuclides to study hydrogen burning in accreting compact stars and isospin-symmetry breaking effects in the Standard Model. Ph.D. in Physics from Yale University (2008) Research areas: Nuclear Astrophysics Low-Energy Nuclear Experiments Isospin Symmetry Detector Instrumentation His group develops advanced detectors like GADGET II , LIBRA , and DSL2 to measure nuclear reactions in novae, neutron stars, and cosmic explosions. Recent work leverages machine learning and MCMC Bayesian analysis for data interpretation. Scientific awards include the DOE Office of Science Early Career Research Program (2016). His students and postdocs contribute to international collaborations and instrumentation projects, often publishing in Physical Review C and Nuclear Instruments and Methods in Physics Research .

Marie-Christine Dükerمشاهده پروفایل

استادیار

• high-dimensional statistics
• time series analysis
• functional data analysis
• +۱۱ مورد دیگر

Marie-Christine Düker is an Assistant Professor in the Department of Statistics and Data Science at Friedrich-Alexander University (Germany). Her research focuses on high-dimensional statistics, time series analysis, functional data analysis, and extreme value theory with applications in economics, psychology, chemistry, and ecology. Previously, she was a postdoctoral associate at Cornell University's Department of Statistics and Data Science under David Matteson. She earned her PhD in Mathematics from Ruhr-University Bochum under Herold Dehling and spent part of her doctoral studies at the University of North Carolina at Chapel Hill with Vladas Pipiras. Current Position: Assistant Professor, Department of Statistics and Data Science, Friedrich-Alexander University Previous Academic Affiliation: Postdoctoral Associate, Cornell University Education: PhD in Mathematics, Ruhr-University Bochum; Part-time research at University of North Carolina Research Interests: Her work spans high-dimensional time series under long-range dependence and nonstationarity, discrete data modeling, nonlinear dynamics, dimension reduction, and change-point analysis. Applications include econometrics, neuroscience, chemical data analysis, and ecological forecasting. Recent Publications: Her 2025-2024 work covers Hilbert space-valued linear processes, kernel estimation for nonlinear dynamics, confidence interval approximations, and latent Gaussian count time series. Earlier papers address simultaneous diagonalization, long-run variance matrices, and transition rate estimation challenges. Contact: marie.dueker@fau.de

• Statistics
• Applied Data Science
• Veterinary Epidemiology
• +۲ مورد دیگر

Hans Vernooij is a Lecturer in Farm Animal Health at the Faculty of Veterinary Medicine, Utrecht University. He specializes in statistical methods and data science applications in veterinary epidemiology and animal health. His areas of expertise include: Statistical methods for veterinary research Applied Data Science in Life Sciences Epidemiological modeling Machine learning applications in animal health Vernooij has extensive experience in developing statistical models for animal health applications. His research focuses on applying advanced statistical techniques and data science methods to solve problems in veterinary epidemiology and farm animal health. He has particular expertise in Random Forest models, as demonstrated during his sabbatical at the Human Sciences Research Council in Pretoria where he developed a model for HIV status prediction based on demographic information and knowledge of HIV prevention from large-scale survey data. His publication record shows consistent contributions across veterinary epidemiology, with recent work emphasizing machine learning applications and big data analytics in animal health surveillance. The research demonstrates a clear trajectory from traditional statistical methods toward more advanced data science approaches. Vernooij is actively involved in teaching and mentoring: Teaches statistics to Bachelor students at the veterinary faculty Supports PhD candidates and Master students during data analysis phases of their research Provides statistics education for the Master of Epidemiology program at the Julius Centre of University Medical Centre