جست‌وجوی استاد و پژوهشگر

Robert Schickمشاهده پروفایل

عضو هیئت علمی

• Ecology
• Marine Biology
• Statistics
• +۵ مورد دیگر

Dr Robert Schick is an Honorary Senior Research Fellow at the School of Mathematics and Statistics, University of St Andrews, affiliated with the Centre for Research into Ecological and Environmental Modelling (CREEM). His work bridges ecology, statistics, and information visualization, focusing on animal movement dynamics and human behavioral patterns. Key research interests include marine organism vulnerability to human disturbance, ecological connectivity, and a SULSA LEADERS-funded project examining smoking behavior through movement patterns. He has collaborated on projects involving cetaceans, right whales, and turtle ecology, emphasizing statistical modeling and conservation applications. Education details are not explicitly listed, but his career includes roles at CREEM and MASTS (Marine Alliance for Science and Technology for Scotland). Research has addressed topics such as prey dynamics in leatherback turtles, body condition in marine predators, and spatial modeling of habitat mosaics. Collaborations span institutions including Duke University, Stanford, and NOAA. His work contributes to UN Sustainable Development Goals, particularly life below water (SDG 14) and good health and well-being (SDG 3). Projects like MapMySmoke (funded by EPSRC) apply mobile technology to behavioral health interventions. No specific scientific awards are listed, but his contributions to ecological methodology and conservation science are well-documented. He advises on interdisciplinary projects and maintains active research in marine and human movement ecology.

• Computer Vision
• Machine Learning
• Few-Shot Learning
• +۶ مورد دیگر

Da Chen is a Lecturer in the Department of Computer Science at the University of Bath, affiliated with the Bath Institute for the Augmented Human and the Centre for Sustainable Energy Systems. He focuses on advanced computer vision and machine learning techniques, particularly in few-shot learning, incremental learning, video understanding, and their applications to urban planning and sustainability. His work bridges AI with real-world challenges like solar energy prediction, urban mobility optimization, and environmental monitoring. Education: He holds a PhD titled 'The Visual Analysis of Complex Natural Phenomena' (2017), supervised by Prof. P. Hall and Prof. M. Brown. Research Interests: Chen’s research spans generative AI, video object detection, solar radiation modeling, and urban scene analysis. He explores how machine learning can address sustainability goals, such as optimizing bike-sharing systems and predicting energy demands through satellite imagery. His methods often involve GANs, convolutional neural networks, and hybrid sequence encoders. Grants & Collaborations: He co-leads a Royal Society-funded project (2024–2026) on improving transient heat transfer experiments using Bayesian statistics and neural networks. His collaborations span institutions globally, focusing on urban-scale AI applications and environmental science. Labs/Teams: Active in the Bath Institute for the Augmented Human, advancing human-centric AI, and the Centre for Sustainable Energy Systems, integrating computational methods for energy efficiency.

Xi Chenمشاهده پروفایل

مدرس ارشد

• Bayesian Inference
• Machine Learning
• Deep Learning
• +۳ مورد دیگر

Dr. Xi Chen is a Senior Lecturer (Associate Professor) in Machine Learning and Data Science at the Department of Computer Science, University of Bath. He holds affiliations with the UKRI CDT in Accountable AI, Institute for Mathematical Innovation, and the Centre for Mathematics and Algorithms for Data (MAD). His research focuses on Bayesian inference, machine learning, statistical signal processing, and Monte Carlo methods. He has contributed to interdisciplinary applications such as AI for medical imaging and offshore engineering. Dr. Chen completed his Ph.D. in Bayesian inference and statistical signal processing at the University of Cambridge. His teaching includes courses on Artificial Intelligence, Machine Learning, and Bayesian Machine Learning. He is also an Associate Editor for the IET Signal Processing Journal and a Fellow of the UK Higher Education Academy. His research interests span AI applications in collective intelligence, computational methods for wave dynamics, and biomechanics. Recent work includes developing Gaussian Process models for offshore engineering and wearable sensor-based systems for analyzing human movement. His publications emphasize interdisciplinary challenges in AI ethics, model vulnerability, and data-driven decision-making. Dr. Chen leads the A Novel Contour-based Machine Learning Tool for Reliable Brain Tumour Resection project (EPSRC-funded) and collaborates on offshore wind energy research (SEASWALLOWS project). His grants include funding from the National Institute for Health Research for AI-based disease monitoring tools. He supervises students in the UKRI CDT in Accountable AI and contributes to the Bath Institute for the Augmented Human. His labs focus on Bayesian machine learning and AI for science, fostering collaborations across engineering, mathematics, and healthcare domains.

Yan Liمشاهده پروفایل

مدرس

• IoT
• Mobile Computing
• Wireless Sensor Networks
• +۷ مورد دیگر

Dr. Yan Li is a Lecturer in IoT and Networking at the School of Computing, Macquarie University. She holds a PhD in Electrical and Electronic Engineering from the University of Melbourne and a Master of Research in Electronic Engineering from the University of Technology Sydney. Previously, she was a Research Fellow at the Telstra-UoW AIoT Hub. Her research focuses on IoT, inertial navigation, mobile computing, wireless sensor networks, and machine learning applications in sensor data analytics. She has been recognized with the Michael Richey Medal and a DIN Pilot Project Grant, and contributed to initiatives like the Future Communications Research Centre and Smart Green Cities Research Centre. Education: Bachelor of Engineering in Aerospace Engineering, Northwestern Polytechnical University PhD in Electrical and Electronic Engineering, University of Melbourne Master by Research in Electronic Engineering, University of Technology Sydney Research interests include: IoT and networking infrastructure Indoor localization and tracking systems Wearable sensor technologies Machine learning for sensor data analytics Healthcare mobility monitoring Smart home situational awareness Notable awards and grants include the Michael Richey Medal (2023), DIN Pilot Project Grant (2022-2023), and the 2023 Faculty of Science and Engineering Award for Inter-School Collaboration. Her work spans over 300 citations and 25 research outputs, emphasizing practical applications in telecommunications and smart environments. Advising and grants: Supervises PhD/Master students in IoT, localization, and machine learning. Active in collaborative projects like the Future Communications Research Centre, focusing on IoT and smart infrastructure solutions. Labs/Teams: Affiliated with Macquarie's Future Communications Research Centre and Smart Green Cities Research Centre, advancing IoT and sustainable urban technologies.

Joseph G. Ibrahimمشاهده پروفایل

استاد

• Bayesian Inference
• Clinical Trials Design
• Genomics
• +۴ مورد دیگر

Dr. Joseph G. Ibrahim is the Alumni Distinguished Professor of Biostatistics at the University of North Carolina at Chapel Hill's Gillings School of Global Public Health. He holds leadership roles as Director of Graduate Studies in Biostatistics, Director of the Laboratory for Innovative Clinical Trials, and Biostatistics Core Director at the Lineberger Comprehensive Cancer Center. His academic tenure includes teaching courses like BIOS 762 (Linear Models) and BIOS 779 (Bayesian Statistics). Education: PhD/MSc in Statistics (University of Minnesota, 1988) and BS in Mathematics (1983). Research Interests: Bayesian methodology, clinical trial design, missing data, medical imaging, and cancer genomics. His work bridges statistical theory and application, with over 360 publications in top journals and two influential books on Bayesian survival analysis and Monte Carlo methods. Key Contributions: Developed statistical methods for joint modeling of longitudinal/survival data, power prior frameworks, and Bayesian approaches for multi-regional trials. His team's innovations include the LEAP prior for historical data integration and the glmmPen R package for high-dimensional models. Awards: Elected fellowships from ASA, IMS, RSS, and ISBA; Janssen Chair in Survival Analysis (2005); and UNC's Alumni Distinguished Professorship (2006). Service: Editor of JASA (Applications/Cases, 2013–2015), associate editor for Biometrics, Lifetime Data Analysis, and Bayesian Analysis. Served on NIH study sections (BMRD, EPIC) and ASA's Bayesian Section leadership. Labs/Teams: Leads the Center for Innovative Clinical Trials (Gillings Innovation Lab) and directs the Cancer Genomics Training Grant. Collaborates on NIH-funded breast cancer SPORE projects and epigenomic analysis tools like Epigrahmm.

Raymond Ka Wai Wongمشاهده پروفایل

دانشیار

• Statistical Learning
• Causal Inference
• Functional Data Analysis
• +۴ مورد دیگر

Raymond Ka Wai Wong is an Associate Professor and Director of the PhD Program in the Department of Statistics at Texas A&M University. He holds a PhD in Statistics from the University of California, Davis (2014), an MPhil from The Chinese University of Hong Kong (2010), and a BSc with minors in Mathematics and Risk Management Science (2008). His research focuses on causal inference, functional data analysis, low-rank modeling, reinforcement learning, and statistical learning with applications in astronomy, brain imaging, and genomics. Wong's professional roles include Associate Editor for Journal of Computational and Graphical Statistics , Journal of the American Statistical Association , and member of the Research Institute for Foundations of Interdisciplinary Data Science (FIDS). He has secured grants from NSF, NASA, and NIH, including leadership in projects like Virtual Assistant for Spacecraft Anomaly Treatment and phenomic selection in maize hybrids. His awards include Top Reviewer distinctions at NeurIPS (2023) and ICML (2020), and a 2016 Discussion Paper in the Annals of Applied Statistics. He has advised numerous doctoral students, with notable advisees receiving awards such as the ICSA Student Paper Award and Emanuel Parzen Fellowship. Wong’s recent work emphasizes methodological advancements in reinforcement learning (e.g., distributional off-policy evaluation) and matrix/tensor completion under informative missingness. His research bridges theoretical statistics with practical applications in interdisciplinary domains such as neuroscience, agriculture, and space exploration.

Andriy Noretsمشاهده پروفایل

استاد

• Bayesian Econometrics
• Dynamic Discrete Choice Models
• Nonparametric Estimation
• +۳ مورد دیگر

Andriy Norets is a Professor in the Economics Department at Brown University. His research focuses on Bayesian econometrics, dynamic discrete choice models, and nonparametric estimation methods. He has contributed to advancements in econometric theory, particularly in areas such as posterior consistency, adaptive estimation, and computational methods for complex models. Key research themes include Bayesian nonparametric models, instrumental variable regression, and the development of efficient computational algorithms for variable dimension models. Norets has published extensively in top journals like Econometrica , Journal of Econometrics , and Annals of Statistics . His work emphasizes methodological rigor and practical applications, such as analyzing stock market trading activity and improving inference in nonstandard econometric problems. He also collaborates with co-authors like Ulrich Müller, Justinas Pelenis, and Debdeep Pati on projects spanning from theoretical foundations to applied econometric problems. Norets teaches courses including Undergraduate Econometrics and Graduate Econometric Theory II, demonstrating his commitment to both research and education in economic methodology.

Tingting Zhangمشاهده پروفایل

استاد

• Neuroimaging Data Analysis
• Human Brain Mapping
• Bayesian Statistics
• +۴ مورد دیگر

Dr. Tingting Zhang is a Professor in the Department of Statistics at the University of Pittsburgh's Dietrich School. She holds a Ph.D. in Statistics from Harvard University (2008), an M.S. in Statistics from Harvard (2005), and a B.S. in Mathematics from Peking University (2003). Her research focuses on neuroimaging data analysis, human brain mapping, and Bayesian statistics, with applications to neurological disorders. She collaborates with clinicians and neuroscientists to develop statistical methods for analyzing multimodal brain imaging data, including fMRI and EEG. Her methodological contributions include Bayesian network models, high-dimensional data analysis, and variational inference techniques. Recent work examines functional connectivity across lifespan stages using HCP datasets and epileptic seizure analysis through directional brain networks. She has developed software tools including the Bayesian Modular and Indicator-based Dynamic Directional Model (BMIDDM) and the Bayesian hierarchical model for state-space stochastic block analysis. Dr. Zhang has taught courses in Bayesian statistics, stochastic processes, and experimental design. Her work bridges statistical theory with clinical applications, emphasizing translational research in neuroimaging. She maintains an active research program with over 30 peer-reviewed publications in top journals like Annals of Applied Statistics and NeuroImage.

Michael McCrackanمشاهده پروفایل

پژوهشگر

• Astrophysics
• Cosmology
• Data Reduction
• +۳ مورد دیگر

Michael McCrackan is a Postdoctoral Associate in Physics at Yale University, affiliated with the Simons Observatory (SO) Collaboration . He holds a PhD from the University of Massachusetts Amherst (2024) with dual mentorship: his dissertation was supervised by Professor Grant Wilson, focusing on data reduction for the TolTEC millimeter camera and hierarchical Bayesian modeling of galactic dust in dwarf galaxies, while his current work is under the advisement of Professor Laura Newburgh in cosmology and cosmic microwave background (CMB) science. Education : Bachelor of Science, University of New Mexico, 2015 PhD in Physics, University of Massachusetts Amherst, 2024 Research Interests : His work bridges observational astrophysics and cosmology, with expertise in: Data reduction pipelines for large-scale telescopes (e.g., Large Millimeter Telescope, Simons Observatory) Modeling interstellar dust dynamics in dwarf galaxies as analogs for high-redshift systems Algorithm optimization for time-ordered data in CMB experiments Collaborations : Member of the Simons Observatory team, contributing to SAT/LAT telescope data analysis and pipeline development (sotodlib/so3g).

Hugo Maruri-Aguilarمشاهده پروفایل

مدرس

• Algebraic Statistics
• Computer Experiments
• Statistical Data Analysis
• +۴ مورد دیگر

Dr Hugo Maruri-Aguilar is a Lecturer in Statistics at Queen Mary University of London, affiliated with the School of Mathematical Sciences and the Centre for Probability, Statistics and Data Science. His research focuses on algebraic statistics, computer experiments, and statistical modeling using likelihood and penalized likelihood techniques. He explores design of experiments through algebraic methods and develops emulators for computer experiments using polynomial models regularized by smoothness. His research interests include Lasso regression, topological data analysis, and applications in music generation and healthcare. Recent work emphasizes model selection and sparse polynomial prediction. He has collaborated on projects ranging from biomarker prognostic models in heart failure to AI-driven music composition. Dr Maruri-Aguilar has contributed to over 15 peer-reviewed publications across journals like Statistical Papers , Annals of the Institute of Statistical Mathematics , and Technometrics . His methodologies address challenges in optimal design, computational topology, and interdisciplinary applications in music and medicine.

• Applied Population Ecology
• Wildlife Conservation
• Hierarchical Modelling
• +۴ مورد دیگر

Associate Professor Jason Marshal is affiliated with the School of Animal, Plant and Environmental Sciences at the University of the Witwatersrand. He holds a PhD from Arizona State University and has expertise in quantitative population ecology, hierarchical modelling, and wildlife conservation. His research focuses on estimating animal population dynamics and their interactions with environmental factors. He teaches applied population ecology and advanced data analysis courses. Recent work emphasizes Bayesian analysis and the impacts of climate variability on large herbivores. His publications span over three decades, covering topics like rhino population management, herbivore niche partitioning, and predator-prey dynamics in fragmented ecosystems. Awards are not listed, but his lab actively recruits postgraduate students interested in quantitative ecological methods. PhD: Arizona State University Teaching: 3rd-year applied ecology, 4th-year data analysis modules Research interests include applying statistical methods to understand large mammal ecology, particularly in human-impacted environments. His work frequently addresses conservation challenges in South Africa and beyond, such as rhino population monitoring and habitat fragmentation effects. Key article themes include: population estimation techniques, climate-driven ecological shifts, and spatial modeling of herbivore behavior. Grants and lab activities are not explicitly detailed but inferred through publication patterns. Labs/teams: Works with wildlife conservation groups and protected area managers to implement field-based research projects.

Victor De Oliveiraمشاهده پروفایل

استاد

• Bayesian Methods
• Environmental Statistics
• Geostatistics
• +۳ مورد دیگر

Victor De Oliveira is a Professor in the Department of Management Science and Statistics at the Carlos Alvarez College of Business, The University of Texas at San Antonio (UTSA). He joined UTSA in 2006 after prior roles at the University of Arkansas and Simon Bolivar University. His academic qualifications include a Ph.D. in Statistics from the University of Maryland and master's and bachelor's degrees in Mathematics and Water Resources from Universidad Simón Bolívar. His research focuses on Bayesian methods, environmental and geostatistical modeling, spatial statistics, and statistical computing. Notable areas of interest include Gaussian random fields, copula models, and applications in environmental science and public health. He develops software tools such as geoCount and gcKrig for geostatistical analysis of count data. Education: Ph.D. in Statistics, University of Maryland M.S. in Water Resources, Universidad Simón Bolívar B.S. in Mathematics, Universidad Simón Bolívar Dr. De Oliveira is a Fellow of the American Statistical Association and an Elected Member of the International Statistical Institute. His teaching spans advanced statistical courses such as Spatial Statistics and Advanced Inference. He actively contributes to the statistics community through software development, editorial roles, and collaborative research projects. His recent work addresses spatial prediction, non-Gaussian spatial models, and applications in biomechanics and environmental monitoring. He emphasizes Bayesian methodologies and interdisciplinary collaborations to solve complex data-driven challenges.

Michele Peruzziمشاهده پروفایل

استادیار

• Spatial Statistics
• Bayesian Methods
• Environmental Health
• +۴ مورد دیگر

Overview Michele Peruzzi is an Assistant Professor of Biostatistics at the University of Michigan–Ann Arbor's School of Public Health. His research focuses on Bayesian methods for spatial and multivariate data, particularly in environmental health applications, high-resolution sensor data, and scalable statistical computing. He develops software tools like the meshed and spamtree R packages to address computational challenges in large-scale analyses. Education PhD in Statistics, Università Bocconi (2019) MSc in Economic and Social Sciences, Università Bocconi (2013) BSc in Economic and Social Sciences, Università Bocconi (2010) Research Focus Peruzzi's work addresses three core areas: (1) scalable Bayesian geostatistical methods for environmental sensors (e.g., satellite imaging, air quality), (2) response surface modeling for large health datasets, and (3) climate change impacts on ecosystems and human health. His methods emphasize computational efficiency and reproducibility. Key Contributions Developed meshed Gaussian processes for partitioned domain analysis Pioneered spatial multivariate trees for big data regression Advanced phenology modeling via satellite data for continental-scale vegetation analysis Affiliations Based in Ann Arbor, MI, Peruzzi holds office in SPH II M4531. His work intersects statistics, environmental science, and public health, with applications in climate change mitigation and precision medicine.

• Statistics
• Spatial Statistics
• Bayesian Inference
• +۴ مورد دیگر

Francisco Palmí-Perales is an Assistant Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, University of Valencia. He earned his PhD in 2020 from the Universidad de Castilla-La Mancha with a thesis on Bayesian multivariate spatial models for the joint analysis of several diseases, supervised by Dr. Virgilio Gómez-Rubio. His research focuses on advanced statistical methodologies for spatial and spatio-temporal data, particularly within the Bayesian framework. His primary interests include spatial statistics , Bayesian inference , disease mapping , and multivariate analysis . He specializes in computational techniques such as the Integrated Nested Laplace Approximation (INLA) for efficient posterior inference in complex models. His recent publications center on extending INLA for multivariate spatial models and applying Bayesian methods to the joint analysis of multiple diseases. This work demonstrates a strong trend in developing and applying sophisticated statistical tools to address challenges in public health and epidemiology, with a particular emphasis on computational efficiency and model complexity. He has collaborated with researchers such as Virgilio Gómez-Rubio, Pablo Fernández-Navarro, and Gonzalo López-Abente. There is no public information available regarding scientific awards, grants, or students he has advised.

Eric Thraneمشاهده پروفایل

استاد

• Gravitational Waves
• Astrophysics
• Cosmology
• +۵ مورد دیگر

Eric Thrane is a Professor at Monash University's School of Physics and Astronomy, specializing in gravitational wave astronomy, astrophysics, and cosmology. His work combines data from gravitational wave observatories like LIGO with electromagnetic telescope observations to study compact binaries, neutron stars, and black holes. Institution: Monash University (School of Physics and Astronomy) Research Themes: Gravitational waves, cosmology, Bayesian inference, dark matter detection Thrane's recent research focuses on gravitational wave transient analysis, including GW231123 , black hole mass-spin correlations, and nanohertz gravitational wave detection via pulsar timing arrays. He develops advanced Bayesian frameworks like GammaBayes for dark matter searches and Bilby for gravitational wave inference. His group publishes extensively in journals like Physical Review D , Monthly Notices of the RAS , and Nature Astronomy . Collaborations span LIGO, Virgo, OzGrav, and the Cherenkov Telescope Array. Thrane leads major grants including a $460K ARC Discovery Project and contributes to $37M OzGrav 2 center. Awards: Special Breakthrough Prize in Fundamental Physics (2016), ARC Future Fellowship (2015) Outreach: Featured in ABC National Breakfast, Catalyst, and The Conversation