جست‌وجوی استاد و پژوهشگر

Miguel-Ángel Fernández-Torresمشاهده پروفایل

استادیار

• Machine Learning
• Computer Vision
• Image Processing
• +۱۳ مورد دیگر

Miguel-Ángel Fernández-Torres is an Assistant Professor in the Department of Signal Theory and Communications at the Polytechnic School of Universidad Carlos III de Madrid (UC3M). He is also affiliated with the Image and Signal Processing Group at the University of Valencia, where he serves as a Senior Researcher. His academic journey began and concluded at UC3M, where he earned his B.S., M.S., and Ph.D. in Multimedia and Communications. Ph.D. in Multimedia and Communications, UC3M (2019) M.S. in Multimedia and Communications, UC3M (2014) B.S. in Audiovisual System Engineering, UC3M (2013) His research lies at the intersection of machine learning, computer vision, and Earth system sciences. He specializes in explainable deep learning, focusing on anomaly detection, extreme event forecasting, and visual attention modeling. His work applies to domains such as climate science, remote sensing, and biomedical imaging. He has conducted research stays at Purdue University and Technische Universität München, and studied at TU Wien during his bachelor's. The most recent publications reflect a strong trend toward applying deep learning and causal modeling to climate extremes, drought, and wildfire prediction. Themes include explainability, spatio-temporal modeling, and unsupervised learning for Earth observation data. His work increasingly integrates physics-aware models and aims to interpret deep learning outputs in environmental contexts. European Laboratory for Learning and Intelligent Systems (ELLIS) Member Teaching Associate Professor Certification (AVAP, 2023) Teaching Assistant Professor Certification (ANECA, 2019) Spanish Ministry FPU Grant (2013) Excellence Awards from UC3M and Social Council He has supervised numerous B.S., M.S., and Ph.D. students and has served as a reviewer for top journals including IEEE TGRS, Nature npj Climate, and CVPR workshops. He leads and contributes to major projects such as DeepExtremes (ESA), XAIDA (H2020), and USMILE (ERC Synergy). He has taught courses in digital signal processing, machine learning, and multimedia at both UC3M and the University of Valencia. He is actively involved in the research community, having served on the organizing committee for AISTATS 2022 and 2023, and contributed to open-source tools and datasets for climate AI.

Miguel Ángel Fernández Torresمشاهده پروفایل

استادیار

• Machine Learning
• Deep Learning
• Computer Vision
• +۸ مورد دیگر

Miguel Ángel Fernández Torres is an Assistant Professor in the Department of Signal Theory and Communications at the Polytechnic School of Universidad Carlos III de Madrid, Spain. He also holds a Senior Researcher position at the University of Valencia within the Image and Signal Processing Group, where he contributes to major climate and Earth science projects such as USMILE, XAIDA, and ESA DeepExtremes. His educational background includes a Ph.D. in Multimedia and Communications (2019), a Master's in the same field (2014), and a B.S. in Audiovisual System Engineering (2013), all from Universidad Carlos III de Madrid, where he received several academic excellence awards. He completed research stays at Purdue University and Technische Universität München, broadening his international research experience. His research focuses on Machine Learning for Earth and Climate Sciences, particularly in designing explainable deep generative models and attention mechanisms for anomaly and extreme event detection. His work spans computer vision, biomedical image analysis, and climate modeling, with a strong emphasis on interpretability and real-world impact. His recent publications reflect a clear trend toward applying deep learning to environmental challenges such as drought, wildfire, and climate extremes, often incorporating causal inference and model explainability. His scientific achievements include being an ELLIS member since 2022 and receiving teaching certifications from AVAP and ANECA. He has been awarded the FPU grant from the Spanish Ministry of Education and multiple excellence awards throughout his academic career. He has co-supervised several Ph.D. students at the University of Valencia and supervised numerous B.S. and M.S. theses at Carlos III University, covering topics like autism diagnosis, melanoma detection, and autonomous driving perception. He has secured competitive grants from ESA, H2020, and Climate Change AI, and actively serves as a reviewer for top journals and conferences in his field. He is involved in significant research groups and projects, including the Image and Signal Processing Group at the University of Valencia and the Multimedia Processing Group at Carlos III University, working on funded initiatives like H2020 XAIDA and ESA DeepExtremes, which aim to advance climate services and hazard prediction using AI.

John Quigleyمشاهده پروفایل

استاد

• Risk Modeling
• Decision Support Systems
• Bayesian Networks
• +۵ مورد دیگر

John Quigley is a Professor of Management Science at the University of Strathclyde, serving as Vice Dean (Research) of the Strathclyde Business School since 2021. Previously, he was Head of the Management Science Department (2016–2019). He holds a BMath in Actuarial Science from the University of Waterloo (1993) and a PhD in Management Science from the University of Strathclyde (1998). His research focuses on risk modeling and decision support systems, integrating data analysis and expert judgment. Key areas include large-scale manufacturing, offshore windfarm maintenance, and search and rescue (SAR) operations in Arctic regions. Notable projects include the NNSAR initiative (funded by NERC and NRC), enhancing SAR systems in Nunavut and Nunavik, and the Productivity and Sustainability Management project optimizing manufacturing processes via real-time data and probabilistic networks. He collaborates with industries like Rolls-Royce, Scottish Power, and the European Food Safety Agency. Quigley has received awards such as the FAIM 2025 Sullivan Best Paper Award and the 2010 KTP Award. His teaching spans undergraduate, postgraduate, and executive education programs globally, emphasizing technology integration in pedagogy.

Silvia Liveraniمشاهده پروفایل

استاد

• Bayesian Modelling
• Clustering Methods
• Computational Statistics
• +۷ مورد دیگر

Dr Silvia Liverani is a Reader in Statistics and Head of the Centre for Probability, Statistics and Data Science at Queen Mary University of London's School of Mathematical Sciences. Her research focuses on Bayesian methods, clustering, and spatio-temporal modeling with applications in epidemiology, environmental health, and genetics. She collaborates with institutions like the Royal Botanical Gardens, Kew, and has advised three PhD students. She leads interdisciplinary projects including a £1,000 LMS-funded LGBTQ+ Mathematics Open Day initiative and hosts international seminars. Research interests include Dirichlet process mixtures, causal methods, and health data modeling. Notable publications (2020–2025) address causal adversarial analysis, urban health inequalities, and Bayesian graph structures. Current projects involve biodiversity modeling using spatial statistics and misaligned data techniques. She has been cited in media outlets like phys.org and The Conversation for studies on cerebral palsy health risks and biodiversity loss. Her academic leadership includes roles in Equality, Diversity & Inclusion and collaborations with charities/industry. Recent grants include a Turing Institute Data Study Group project on biodiversity prioritization. Supervised PhD topics include Bayesian epidemiological modeling and biodiversity data analysis.

Irene Hudsonمشاهده پروفایل

استاد

• Biostatistics
• Data Science
• Public Health Surveillance
• +۷ مورد دیگر

Professor Irene Hudson is a Professor of Statistics and Data Analytics at RMIT University's School of Science, affiliated with the STEM College. She holds adjunct roles at the University of Newcastle and the University of Melbourne. Her research focuses on biostatistics, public health surveillance, computational intelligence, and data-driven solutions for global challenges like climate change and drug discovery. Key collaborations include work with the Global Burden of Disease Network (USA), the WHO, and CSIRO. Education & Experience: PhD (implied via academic roles) Prior academic posts at University of Cambridge, ANU, Melbourne University, Canterbury University (NZ), UniSA, and Swinburne University Research Interests: Biostatistical modeling of micro (molecules/brain anatomy) and macro systems (global disease/climate) Health informatics, causal inference, Bayesian statistics, and meta-analysis Drug discovery (e.g., calpain inhibitors for cataracts, protein kinase interactions) Climate change analytics and spatio-temporal modeling Key Projects: Chief Investigator in CRC Digital Health's Predictive Analytics (2022-2025) Sub-Program Leader in CRC HFPS (Melbourne University), mapping wood fiber microstructure with CSIRO Awards & Recognition: Elected Fellow of the Royal Statistical Society (UK) Grants & Labs: NHMRC collaboration on ovarian cancer markers ARC Centre of Excellence for Nanoscale BioPhotonics (diagnostics cytometry)

Justin Slaterمشاهده پروفایل

استادیار

• (Bio)statistics
• Data Science
• Epidemiology
• +۴ مورد دیگر

Justin Slater is an Assistant Professor in the Department of Math and Stats at the University of Guelph , starting in January 2023. His research focuses on Bayesian methods in statistical epidemiology, including infectious disease modeling, spatio-temporal analytics, and evidence synthesis. He holds a PhD from the University of Toronto (2023), an MSc from Queen’s University (2016), and a BSc from Dalhousie University (2014). Education: PhD in Statistical Sciences, University of Toronto (2019–2023) MSc in Statistics, Queen’s University (2016) BSc (Honours) in Math and Statistics, Dalhousie University (2014) Research Themes: Infectious Disease Statistics: Developing statistical models for epidemic prediction and uncertainty quantification. Spatio-Temporal Analytics: Leveraging cellphone mobility data for infectious disease modeling across disciplines like agriculture and veterinary sciences. Bayesian Statistics: Prior elicitation, hierarchical models in sparse data contexts, and Bayesian mixture models. Evidence Synthesis: Improving meta-analytic and adaptive trial methods for combining randomized and observational studies. Awards: NSERC Postgraduate Scholarship – Doctoral (PGS-D), 2021 His work bridges statistical theory and real-world applications, particularly in public health decision-making. No grants or lab affiliations are explicitly detailed in the provided text.

Murali Haranمشاهده پروفایل

استاد

• Statistical Computing
• Spatial Models
• Computer Experiments
• +۵ مورد دیگر

Murali Haran is a Professor of Statistics at Pennsylvania State University's Department of Statistics within the College of Science. He holds a B.S. in Computer Science from Carnegie Mellon University and advanced degrees (M.S. and Ph.D.) in Statistics from the University of Minnesota. His research focuses on computational statistics, spatial models, and applications in climate science and infectious diseases. He has held leadership roles including Chair of the Penn State Statistics Undergraduate Program (2012–2016) and co-editor of Bayesian Analysis (2016–2018). Research interests include statistical computing (MCMC algorithms), spatial/spatio-temporal models, complex computer model calibration, and interdisciplinary applications. He has contributed to climate risk management initiatives like NSF-sponsored SCRiM and directed Penn State's node in the STATMOS network. Awards include ASA Fellowship (2016), Abdel El-Shaarawi Young Researcher Award (2015), and ASA ENVR Young Investigator Award (2014). Key publications include advancements in ice sheet model calibration, Bayesian inference for intractable models, and spatiotemporal disease dynamics. His work bridges statistical theory with real-world challenges in climate and health. Teaching responsibilities span courses like Computational Statistics, Probability Theory, and Data Science. Professional service includes editorial roles for Technometrics, Biometrics , and others. He advises on climate policy through roles like ASA Climate Change Committee member (2009–2014) and ISBA Treasurer (2014–2016). His software contributions include the ngspatial R package and tools for MCMC error estimation.

Cosma Shaliziمشاهده پروفایل

دانشیار

• Nonparametric prediction of time series
• Learning theory
• Nonlinear dynamics
• +۱۵ مورد دیگر

Cosma Shalizi is an Associate Professor in the Statistics Department and Machine Learning Department at Carnegie Mellon University, and an External Professor at the Santa Fe Institute. His work bridges statistics, machine learning, and complex systems theory, with applications spanning neuroscience, statistical mechanics, and social networks. Shalizi's research focuses on nonparametric prediction of time series, learning theory, information theory, and causal inference. He has made significant contributions to computational mechanics, developing algorithms like CSSR (Causal State Splitting Reconstruction) for identifying optimal predictive states in complex systems. His work extends to heavy-tailed distributions, network analysis, and the statistical foundations of complex systems. He has pioneered methods for quantifying self-organization and developing nonparametric approaches to spatio-temporal prediction. His recent publications reveal a trend toward increasingly interdisciplinary work, connecting network science with causal inference, statistical learning theory with macroeconomic forecasting, and information theory with biomedical applications. His work consistently emphasizes rigorous statistical methodology applied to complex, dependent data structures across diverse scientific domains. Winner of the Best Student Paper and Best Poster awards Shalizi has advised students including Georg Goerg, who extended spatio-temporal prediction techniques to continuous-valued fields, and George Montañez, who developed fast approximate algorithms for prediction and explored information-theoretic explanations for machine learning. His collaborative network spans statistics, physics, neuroscience, and social sciences, reflecting his interdisciplinary approach to complex systems. His work with collaborators has led to significant contributions in network analysis, causal inference in social networks, and the development of nonparametric methods for complex data structures. He maintains active research programs in statistical network modeling, time series analysis, and the application of information-theoretic approaches to diverse scientific problems.

Youngdeok Hwangمشاهده پروفایل

استادیار

• Spatio-Temporal Statistics
• Bayesian Modeling
• Uncertainty Quantification
• +۳ مورد دیگر

Youngdeok Hwang is an Assistant Professor in the Paul Chook Department of Information Systems and Statistics at Baruch College's Zicklin School of Business. He holds a PhD in Statistics from the University of Wisconsin-Madison and specializes in Bayesian modeling, spatio-temporal statistics, and machine learning applications for business and environmental challenges. His research integrates statistical theory with practical applications across healthcare, environmental science, and technology. Key interests include uncertainty quantification, inverse problems, and data fusion techniques for improving decision-making in complex systems. Recent work focuses on AI ethics, pollution modeling, and pandemic-related behavioral analytics. Hwang's publications demonstrate consistent emphasis on Bayesian methods and interdisciplinary problem-solving , with frequent applications in environmental monitoring (33% of recent papers), healthcare analytics (27%), and computational statistics (20%). This reflects his focus on socially impactful statistical innovation. Awards & Honors: Teaching Excellence Award (Zicklin School, 2025) Statistics in Physical Engineering Sciences Award (ASA, 2018) Career Development Award (Korean International Statistical Society, 2016) He actively contributes to academic committees including the Graduate Curriculum Committee and serves as Associate Editor for Technometrics and Applied Stochastic Models in Business and Industry.

Yu Yueمشاهده پروفایل

استاد

• Bayesian statistics
• Nonparametric Regression
• Spatial Statistics
• +۲ مورد دیگر

Yu Yue is a Professor at the Zicklin School of Business, Baruch College, The City University of New York, in the Paul H. Chook Department of Information Systems and Statistics. He holds a PhD and MA in Statistics from the University of Missouri, Columbia, and a BS in Statistics from Shanghai University of Finance and Economics. His research focuses on Bayesian statistics, nonparametric regression, spatial statistics, quantile regression, and functional data analysis. His recent publications include innovations in Bayesian trend filtering for neuroimaging data spatio-temporal modeling of muscle activity adaptive smoothing techniques for fMRI analysis with applications in neuroscience, public health, and computational statistics. Scientific Awards Teaching Excellence Award (Zicklin School of Business, 2011) Eugene M. Lang Junior Faculty Research Fellowship (Baruch College, 2012) SAMSI workshop travel awards (2009, 2014, 2017) R. L. Anderson Award (Southern Regional Council on Statistics, 2007) G. Ellsworth Huggins Fellowship (University of Missouri, 2003) He has advised PhD students Bin Ma (2016-2017) and Zhu Zhu (2015-2016) and received grants from PSC-CUNY and Indiana University for Bayesian modeling of neuroimaging data and statistical methods development.

Andrew Mugglinمشاهده پروفایل

دانشیار

• Biostatistics
• Health Data Science
• Clinical Trials
• +۳ مورد دیگر

Andrew Mugglin is a Research Associate Professor in the Division of Biostatistics & Health Data Science at the University of Minnesota School of Public Health. His expertise spans clinical trials, particularly in cardiovascular medical device applications, Bayesian hierarchical modeling, spatio-temporal modeling, and computational methods. PhD in Biostatistics, University of Minnesota School of Public Health MS in Mathematics, Rensselaer Polytechnic Institute BE in Electrical Engineering, The Cooper Union Dr. Mugglin specializes in innovative clinical trial design using Bayesian approaches, with applications in public health and biomedical research. His work emphasizes statistical modeling and computational techniques for analyzing complex data structures. Contact: amugglin@umn.edu

Chad Babcockمشاهده پروفایل

استادیار

• Remote Sensing
• Forest Inventory
• Geospatial Analysis
• +۳ مورد دیگر

Chad Babcock serves as an Assistant Professor at the University of Minnesota, specializing in the integration of remote sensing technologies with geostatistical methodologies for natural resource management. His work focuses on developing statistically rigorous spatial and spatio-temporal analysis frameworks to address critical challenges in forest inventory, environmental monitoring, and sustainable resource management through interdisciplinary collaboration. Education background: PhD in Forest Sciences from University of Washington (2017) MS in Applied Statistics from Michigan State University (2014) MS in Geography from Michigan State University (2014) BS in Forest Management from Michigan State University (2009) His research program centers on advancing geostatistical approaches using remotely sensed data to predict natural resource characteristics. Key focus areas include Bayesian statistical modeling for spatial uncertainty quantification, forest inventory system optimization, and the development of spatio-temporal frameworks for environmental change detection. This work bridges forestry, geospatial science, and statistics to create practical solutions for resource monitoring and management. Dr. Babcock teaches courses in Remote Sensing and Geospatial Analysis of Natural Resources and Environment, emphasizing methodological innovation and real-world application. His collaborative research approach engages scientists across ecology, remote sensing, and statistics to tackle complex environmental challenges through data-driven solutions.

Mabel Morales Oteroمشاهده پروفایل

پژوهشگر

• Statistics
• Bayesian Statistics
• Spatio-Temporal Modeling
• +۳ مورد دیگر

Mabel Morales Otero is a Postdoctoral Researcher at the University of Navarra , affiliated with the Cancer Center Clínica Universidad de Navarra (CCUN) and the Instituto de Ciencia de los Datos e Inteligencia Artificial (DATAI) . She works within the research groups DigiMedLab Digital Medicine Lab and StatData Diseño estadístico y análisis de datos . She earned her PhD in 2023 from the Universidad del País Vasco - Euskal Herriko Unibertsitatea with a thesis titled Bayesian spatio-temporal conditional overdispersion models for count data proposals, estimation and applications , supervised by Dr. Vicente Alfredo Núñez Antón and Dr. María Pilar González Casimiro. Her research focuses on advanced statistical methodologies, including Bayesian spatio-temporal modeling, digital medicine applications, and statistical design for data analysis.

• Machine Learning
• Deep Learning
• Bioinformatics
• +۶ مورد دیگر

Wolf Beat is an Associate Professor at the Fribourg School of Engineering and Architecture, part of the University of Applied Sciences of Western Switzerland (HES-SO). He is affiliated with the Institute of Complex Systems (iCoSys) where he conducts interdisciplinary research at the intersection of machine learning, bioinformatics, and document analysis. His educational background includes: BA HES-SO in Architecture from Fribourg School of Engineering and Architecture MSc HES-SO in Business Administration MSc HES-SO in Engineering PhD in "Reducing the complexity of OMICS data analysis" from University of Würzburg (2017) Dr. Beat's research spans multiple disciplines with a focus on applying machine learning techniques to solve complex problems. His work in bioinformatics has contributed to advancements in DNA sequencing analysis and genomic research, while his more recent work explores time series forecasting, document analysis, and anomaly detection. He has developed innovative approaches for handling historical manuscripts through computer vision techniques and has applied machine learning to geotechnical engineering problems. His research often bridges theoretical machine learning concepts with practical industrial applications. An analysis of his recent publications reveals a strong trend toward foundation models and diffusion models for time series analysis, alongside continued work in bioinformatics and document analysis. His research demonstrates a consistent pattern of applying cutting-edge AI techniques to domain-specific problems across diverse fields including healthcare, engineering, and digital humanities. Dr. Beat is actively involved in multiple research projects including ModIA (2024-2025, 15,000 CHF) and as principal investigator for the Foundation model for time series forecasting project (2024-2025, 100,000 CHF). His past projects include Monitoring des Transformateurs de Puissance (2023-2024, 220,000 CHF) and GREENum (2022, 24,700 CHF), demonstrating his ability to secure funding for interdisciplinary research. He collaborates extensively within the HES-SO network and with external partners, often serving as a co-researcher or principal investigator on projects that combine expertise from engineering, computer science, and domain-specific applications. His research team at iCoSys Institute includes multiple researchers working on AI and complex systems, and he has been involved in numerous projects with colleagues including Montet Frédéric, Pasquier Benjamin, Von Barnekow Alec, and Maillard Philippe. His work demonstrates a consistent commitment to developing practical AI solutions that address real-world challenges across multiple domains.

• Weak Dependence
• Limit Theorems for Stochastic Processes
• Statistical Inference for Mixed Moving Average and Ambit Fields
• +۴ مورد دیگر

Dr. Imma Valentina Curato is a researcher specializing in probability theory, financial econometrics, and stochastic processes. She holds a DFG-funded research grant focused on PAC Bayesian bounds for light cone and trajectory data, with a particular emphasis on high-frequency financial modeling and statistical inference for complex random fields. Education: PhD in Mathematics, University of Pisa (2013) MSc in Mathematics, University of Florence (2009) BSc in Mathematics, University of Florence (2006) Research Focus: Weak dependence in stochastic processes Limit theorems for spatio-temporal models Fourier and Laplace transform methods for volatility estimation Bayesian learning in ambit field models Notable Contributions: Developed Fourier-based analysis for stochastic leverage effects Advanced central limit theorems for mixed moving averages Created nonparametric volatility estimation techniques Scientific Recognition: DFG Research Grant recipient Published in top-tier journals like Annals of Applied Probability and Quantitative Finance Academic Engagement: Regular participant in international conferences (SPA, European Meeting of Statisticians, AMASES) Invited speaker at institutions like University of Florence, Imperial College London, and Aarhus University Educational Leadership: Teaches courses on statistical learning, financial engineering, and stochastic processes Organizes seminars on Gaussian processes and high-frequency econometrics