جست‌وجوی استاد و پژوهشگر

Giuliano Bianchiمشاهده پروفایل

دانشیار

• Law and Economics
• Corporate Governance
• Forecasting
• +۳ مورد دیگر

Giuliano Bianchi is an Associate Professor of Economics at EHL Hospitality Business School, specializing in law and economics, corporate governance, and forecasting. He holds a PhD in Economics from the University of Bologna, a Master's in Economics from the University of Edinburgh, and a Master in Law from the University of Fribourg. His research focuses on hospitality economics, asset-light business models, and macroeconomic forecasting in the Swiss hotel sector. Education: PhD in Economics, University of Bologna (Italy) Master's Degree in Economics, University of Edinburgh (UK) Bachelor's in Economics, University of Lugano (Switzerland) Bachelor in Law (BLaw), UniDistance Master in Law (MLaw), University of Fribourg His research interests include empirical analysis of hotel industry dynamics, regulatory frameworks affecting hospitality operations, and corporate governance mechanisms. He pioneered the Swiss Hospitality Macroeconomics Forecasting Index (2018-2020), a project funded by HES-SO to develop demand forecasting tools for Swiss hotels. Recent work explores legal aspects of hotel rate parity, brand affiliation impacts on asset values, and the socio-economic implications of hospitality employment. Awards: Wertheim Fellowship, Harvard Law School Bologna University Economics Department Scholarship Marco Polo Fellowship Program Dr. Bianchi serves on EHL's academic board and teaches Macroeconomics and Microeconomics in the BSc International Hospitality Management program. His research has been published in Tourism Economics, Journal of Property Research, and Applied Economics.

Silia Vitoratouمشاهده پروفایل

دانشیار

• Psychometrics
• Biostatistics
• Latent variable models
• +۶ مورد دیگر

Dr. Silia Vitoratou is an Associate Professor in Psychometrics and Measurement at King's College London's Department of Biostatistics & Health Informatics within the Institute of Psychiatry, Psychology & Neuroscience (IoPPN). She leads the Psychometrics & Measurement Lab and the S-Five research project on misophonia. Her research focuses on psychometric methodology, latent variable models, Bayesian statistics, and applications in mental health and neurology. She holds degrees from the University of Crete (BSc Mathematics), National and Kapodestrian University of Athens (MSc Biostatistics), and Athens University of Economics (PhD Bayesian model comparison). Her expertise spans misophonia mechanisms, autism assessment, and cross-cultural validation of psychological tools. She teaches advanced statistical modeling courses and supervises over five MSc/BSc projects annually. She is a member of the Psychometric Society and co-founded the #tutorpool education initiative. Current projects include autism phenotype measurement bias and NIHR fellowships in neurology and psychology. Recent research highlights include UK misophonia prevalence studies, ALS caregiver distress models, and validation of mental health questionnaires across cultures. Her work addresses measurement invariance in autism screening and develops psychometric tools for sound sensitivity disorders.

Samuel Wongمشاهده پروفایل

دانشیار

• Protein Structure Prediction
• Bayesian Inference
• Stochastic Systems
• +۲ مورد دیگر

Dr. Samuel Wong is Associate Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. His research develops statistical methods for complex data science problems in protein structure analysis, dynamic systems inference, and materials reliability. Education includes PhD from Harvard Statistics Department (2013). Research addresses challenges in conformational sampling for protein folding, inference for differential equation models, and uncertainty quantification in materials science. Leads development of MAGI software for manifold-constrained Gaussian processes. Publications showcase innovations in Sequential Monte Carlo methods, spatial data fusion, and Bayesian approaches to industrial problems. Recent work focuses on protein structure variability and COVID-19 transmission modeling. Supervises graduate students in Bayesian analysis and computational statistics. Teaches courses including Analysis of Spatial Data and Applied Linear Models.

• Bayesian statistics
• High-dimensional inference
• Statistical machine learning
• +۳ مورد دیگر

Mai Dao is an Assistant Professor in the Department of Mathematics, Statistics, and Physics at Wichita State University's Fairmount College of Liberal Arts and Sciences. She earned her Ph.D. from Texas Tech University under the mentorship of Professors Min Wang and Souparno Ghosh. Research Focus: Bayesian statistics, high-dimensional inference, and statistical machine learning Contact: mai.dao@wichita.edu | Jabara Hall 319 | Office hours: Tue & Thu 3:30-4:30 p.m. Research Interests include: Bayesian quantile regression High-dimensional data analysis Statistical machine learning algorithms Variable selection techniques Computational statistics Econometric modeling Recent Article Trends : Mai Dao's publications (2021-2025) emphasize Bayesian quantile regression methods, focusing on variable selection, high-dimensional inference, and computational approaches. Key themes include handling non-ignorable missing data, macroeconomic stress testing, and bridge-randomized regression techniques. Academic Expertise spans: Bayesian statistical modeling High-dimensional inference Machine learning applications Quantile regression methodologies Statistical computing

Pedro Galeanoمشاهده پروفایل

دانشیار

• Outlier Detection
• Time Series Analysis
• Bayesian Inference
• +۳ مورد دیگر

Pedro Galeano is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid (UC3M) since 2009. He holds a PhD in Statistics (2004) under Prof. Daniel Peña, focusing on multiple time series. Previously, he served as Visiting Assistant Professor of Statistics and Econometrics at the University of Chicago’s Graduate School of Business and as a Postdoctoral Fellow at the Department of Statistics and Operations Research at Universidade de Santiago de Compostela. His research focuses on time series analysis, outlier detection, Bayesian inference in financial models, and functional data analysis with applications to missing data. He is an Associate Editor of the Journal of Time Series Analysis and advises the Heliyon journal. Key contributions include developing methodologies for detecting structural breaks, modeling systemic risk via copula approaches, and advancing robust statistical techniques for high-dimensional data. Active in academic leadership, Galeano co-organized the NICDA Workshop 2025 and has published extensively on topics like dynamic factor models, sequential parameter change detection, and functional data applications in energy markets. His work bridges theoretical statistics with practical applications in finance, economics, and environmental science.

• Quantitative Methods
• Equivalence Testing
• Robust Statistics
• +۲ مورد دیگر

Robert A Cribbie is a Professor in the Department of Psychology at the Faculty of Health, York University. His work focuses on quantitative methods for psychological data analysis, particularly equivalence testing, multiplicity control, and robust statistical procedures. Current research emphasizes negligible effect testing, Bayesian statistics, and longitudinal data modeling Active in teaching graduate/undergraduate courses: Statistical Methods, ANOVA, Regression, Multivariate Analysis PI of multiple SSHRC grants (2020-2026) for equivalence testing and statistical modeling research Advisor to 12+ graduate students including Victoria Celio, Naomi Martinez Gutierrez, and Udi Alter Leads Robust Statistics Lab which developed the 'negligible' R package for equivalence analysis Recent publications address: • Equivalence testing in structural equation modeling (2024-2025) • Methodological improvements for Bayesian statistical guides (2025) • Multiplicity control practices in psychological research (2025) • Effect size interpretation standards (2023)

Joost-Pieter Katoenمشاهده پروفایل

استاد

• Model Checking
• Formal Verification
• Probabilistic Models
• +۵ مورد دیگر

Joost-Pieter Katoen is a full Professor at RWTH Aachen University and Head of its Computer Science Department since 2012. He also holds a part-time (20%) Professorship at the University of Twente . His research focuses on model checking , probabilistic verification , formal semantics , and software verification , with applications in aerospace systems. His work has led to significant tools like MRMC (probabilistic model checker), COMPASS (AADL analysis tool-set), and libalf (learning automata library). He has authored over 18 international projects (total €5.2 million) and graduated 12 PhD students. Scientific Awards : Member, German National Academy of Sciences (Leopoldina), 2024 ACM Fellow, 2020 ERC Advanced Grant, 2018 Honorary doctorate, Aalborg University, 2017 Teaching Award, RWTH Aachen, 2010 Philips Early Career Development Award, 1988 Research Trends (from articles): His recent work spans probabilistic program verification , quantitative game theory , Markov chain analysis , and parameter synthesis for stochastic systems, with applications in AI, quantum computing, and fault tree analysis. Leadership & Service : Katoen co-founded the QEST conference , chairs ETAPS steering committee, and has led numerous program committees (CONCUR, TACAS, QEST). He has served on editorial boards and organized conferences/seminars globally.

• Machine Learning
• Computer Vision
• Medical Imaging
• +۷ مورد دیگر

Ali Gooya is a Senior Lecturer (Associate Professor) in Machine Learning at the School of Computing Science, University of Glasgow, UK. His research focuses on probabilistic deep learning applied to medical imaging, particularly in cardiology and oncology, emphasizing semi/unsupervised methods due to sparse expert annotations. He holds a PhD in medical image analysis from the University of Tokyo (2007) and has held academic positions at the University of Leeds and Sheffield before joining Glasgow in 2022. Affiliations: Senior Lecturer in Machine Learning, University of Glasgow (2022–present) Lecturer in Computing, University of Leeds (2018–2022) Lecturer in Computing, University of Sheffield (2016–2018) Postdoctoral Researcher, University of Pennsylvania (2008–2011) Research Interests: Deep learning for medical imaging, probabilistic modeling, cardiac and cancer imaging, computational anatomy, and marker discovery. Key applications include motion analysis, segmentation, and predictive modeling in healthcare. Key Achievements: Won prestigious fellowships including Allen Touring Institute (2022), JSPS Short-Term (2020), Marie-Curie IIF (2014), and JSPS-PDRA (2008). Pioneered Bayesian deep learning frameworks for cardiac motion assessment and generative models in medical imaging. Grants & Supervision: EPSRC Impact Acceleration Award (PI) EPSRC New Investigator Grant (EP/S012796/1) Actively supervising PhD students in areas like Bayesian deep atlases for cardiac motion analysis. Labs & Teams: Leads research in medical AI within the School of Computing Science, collaborating on projects integrating imaging and patient metadata for clinical decision support.

Swati Biswasمشاهده پروفایل

استاد

• Biostatistics
• Statistical Genetics
• Genetic Epidemiology
• +۳ مورد دیگر

Dr. Swati Biswas is a Professor and Associate Department Head in the Department of Mathematical Sciences at the University of Texas at Dallas (UTD). She holds a Ph.D. in Biostatistics from The Ohio State University (2003) and has held academic positions at UTD since 2012, including roles at the University of North Texas Health Science Center prior to that. Her research focuses on biostatistical methods for genetic epidemiology, cancer genetics, and risk prediction modeling, with a particular emphasis on Bayesian approaches. Education: B.Sc. (1994), M.Sc. (1996) in Statistics from the University of Delhi, followed by a Ph.D. in Biostatistics from The Ohio State University (2003). She completed a postdoctoral fellowship at MD Anderson Cancer Center (2004–2005). Research interests include statistical genetics, rare haplotype analysis, Bayesian clinical trials, and personalized risk prediction models for diseases like breast cancer and substance use disorders. She has developed tools such as CBCRisk for contralateral breast cancer prediction and Bayesian hierarchical models for pathway analysis. Her publications span over 40 peer-reviewed articles, focusing on methodological advancements in genetic association studies and clinical risk modeling. Notable awards include the 2016 Young Researcher Award from the International Indian Statistical Association and the 2011 President’s Award for Educational Excellence from UNT HSC. Dr. Biswas has secured grants totaling over $10M, including NIH funding for projects like multifrequency ultrasound imaging for breast cancer monitoring and Bayesian meta-analysis of cancer risk. She mentors doctoral students and has advised nine completed dissertations, with several current students.

Andrea Meilán-Vilaمشاهده پروفایل

استادیار

• Nonparametric Estimation
• Directional Statistics
• Kernel Smoothing
• +۳ مورد دیگر

Andrea Meilán-Vila is an Assistant Professor in the Department of Statistics at Universidad Carlos III de Madrid since 2021, holding a Juan de la Cierva Fellowship since 2023. She earned her PhD in Statistics from Universidade da Coruña (2021) and previously served as a Postdoctoral Fellow at Universidade de Santiago de Compostela's Department of Statistics, Mathematical Analysis and Optimisation. Her research focuses on nonparametric methods for analyzing complex data types, including directional, spatial, and functional data. Key areas include kernel smoothing techniques, goodness-of-fit testing for regression models, and spatial trend estimation. She serves as an Associate Editor for the Journal of Nonparametric Statistics . Recent work emphasizes applications in climate science (temperature curve modeling), fluid dynamics (wake flow control), and biomedical imaging (hippocampus shape analysis). Her methodologies address challenges like sparse data estimation and spatial correlation in regression frameworks. Key Projects: STENED (Stein-based goodness-of-fit tests for non-Euclidean data) Awards: Juan de la Cierva Fellowship (2023) Publications span journals like Journal of Fluid Mechanics , Statistical Papers , and TEST , with a focus on methodological advancements in statistical modeling and computational validation.

• Biomedical Informatics
• Natural Language Processing
• Large Language Models
• +۳ مورد دیگر

Weipeng Zhou is a Postdoctoral Associate at the Yale School of Medicine within the Department of Biomedical Informatics and Data Science . Working under Professor Hua Xu , he specializes in pre-training and evaluating large medical language models using electronic health records and medical claims data. His work bridges Natural Language Processing , Biomedical Data Science , and Clinical Informatics to address critical healthcare challenges. PhD in Medical Informatics from the University of Washington (2025) Bachelor's in Computer Science and Statistics from the University of Wisconsin (2019) His research involves NLP/LLM applications in healthcare domains such as: Long COVID characterization and prediction Cardiovascular disease analysis Suicide prevention through clinical text mining Clinical note section identification Emerging water contaminant detection via PubMed article analysis His publications focus on model transferability , automated cohort discovery , and contextual health research tools . Notable collaborations include work with teams at University of Washington and Yale .

• Algorithms for Big Data Analytics
• Computational Geometry
• Machine Learning
• +۴ مورد دیگر

Jeff M Phillips is a Professor in the Kahlert School of Computing at the University of Utah, specializing in algorithms for big data analytics, computational geometry, and machine learning. He holds a BS in Computer Science and Mathematics from Rice University (2003) and a PhD in Computer Science from Duke University (2009). He serves as Director of the Utah Center for Data Science, Director of the Data Science Program in the Kahlert School of Computing, and Faculty Co-Director of the One U Data Science Hub. His research focuses on geometric data analysis, coresets, sketches, and handling uncertainty in data. Education: BS/BA (Rice University, 2003), PhD (Duke University, 2009) CI Postdoctoral Fellow at University of Utah (2009–2011) His research interests include algorithms for big data analytics, computational geometry, machine learning, spatial statistics, and AI. He has led NSF-funded projects on spatial data analysis, cosmic origins via AI, and reactive flow data modeling. Phillips has advised numerous PhD and master’s students, contributing to topics like trajectory classification and bias mitigation in word embeddings. His publications span computational geometry, data science, and machine learning. Notable work includes coresets for kernel density estimates, bias mitigation in language models, and scalable spatial scan statistics. Phillips is also active in academic service, serving as co-PC chair for SoCG 2024 and on program committees for major conferences like NeurIPS and ICML.

• Large-scale nonparametric and kernel methods
• Robust and trustworthy machine learning
• Multiresolution data and data fusion
• +۳ مورد دیگر

Professor Dino Sejdinovic is a faculty member in the School of Computer and Mathematical Sciences at the University of Adelaide, part of the Faculty of Sciences, Engineering and Technology. Previously, he held positions as Lecturer and Associate Professor at the University of Oxford's Department of Statistics (2014–2022). His academic qualifications include a PhD in Electrical and Electronic Engineering from the University of Bristol (2009) and a Diplom in Mathematics and Theoretical Computer Science from the University of Sarajevo (2006). His research focuses on the intersection of statistical methodology and machine learning, encompassing large-scale nonparametric methods, robust machine learning, multiresolution data fusion, and measures of dependence. He has contributed to kernel methods, Bayesian inference, causal discovery, and applications in climate science, quantum computing, and social science data analysis. Education: PhD in Electrical and Electronic Engineering, University of Bristol (2009) Diplom in Mathematics and Theoretical Computer Science, University of Sarajevo (2006) Sejdinovic's work emphasizes bridging theoretical foundations with practical applications, such as cloud type classification using vision transformers and machine learning-driven quantum device optimization. His recent publications explore topics like kernel-based causal inference, Bayesian neural networks, and uncertainty quantification in statistical models. Advising and grants: Eligible to supervise Masters and PhD students in machine learning and statistics, though specific grants or student advisees are not explicitly listed in the provided texts.

Noel Cressieمشاهده پروفایل

استاد

• Spatial Statistics
• Environmental Informatics
• Bayesian Statistics
• +۳ مورد دیگر

Noel Cressie is a Distinguished Professor of Statistics at the University of Wollongong (UOW), Australia, affiliated with the School of Mathematics and Applied Statistics and the National Institute for Applied Statistics Research Australia (NIASRA). He is also the Director of the Centre for Environmental Informatics (CEI). His academic journey includes a PhD from Princeton University (1975) and a B.Sc. with First Class Honours from the University of Western Australia (1972). His research focuses on spatial and spatio-temporal statistics, Bayesian methods, environmental informatics, and applications in climate science. Notable projects include work on atmospheric CO2 flux inversion (WOMBAT framework), Antarctic environmental research (SAEF initiative), and statistical remote sensing for NASA. He has secured over $20 million in research funding and authored four influential books, including Statistics for Spatial Data . Cressie has received prestigious awards such as the COPSS R.A. Fisher Award (2009), Pitman Medal (2014), and Fellowship of the Australian Academy of Science (2018). He leads interdisciplinary teams addressing global challenges like carbon cycle dynamics and biodiversity modeling. His contributions to statistical methodology and environmental science have been recognized through international collaborations and advisory roles.

Alexandros Taflanidisمشاهده پروفایل

استاد

• Uncertainty Quantification
• Disaster Risk Reduction
• Bayesian Model Updating
• +۳ مورد دیگر

Professor Alexandros Taflanidis holds a concurrent faculty position as Professor in the Department of Civil and Environmental Engineering and Earth Sciences and the Department of Aerospace and Mechanical Engineering at the University of Notre Dame's College of Engineering. He serves as the Director of Graduate Studies for CEEES. His research focuses on uncertainty quantification, disaster risk reduction, Bayesian model updating, and enhancing the sustainability and resilience of civil infrastructure systems, particularly in natural hazard contexts like hurricanes and earthquakes. His work integrates computational statistics and surrogate modeling to improve real-time emergency response and long-term risk mitigation strategies. Prof. Taflanidis earned a Ph.D. from the California Institute of Technology (2007), and M.S. and B.S. degrees in Civil and Environmental Engineering from Aristotle University of Thessaloniki (2003 and 2002). He leads projects such as the Coastal Hazards System (CHS) for Louisiana and Puerto Rico, advancing probabilistic coastal hazard analysis frameworks. His research also explores storm surge emulation, seismic response estimation, and innovative protective device designs for structures. He won the ASCE Huber Prize for his contributions to community resilience through scientific computing. His collaborative efforts include advancing machine learning for data imputation in coastal hazards and developing lifecycle assessment workflows for resilient buildings. Current research trends in his publications emphasize computational efficiency, multi-fidelity modeling, and adaptive strategies for real-time predictions. Prof. Taflanidis's work bridges academic and practical domains, addressing challenges such as climate change impacts on coastal regions and earthquake early warning systems. His lab focuses on integrating interdisciplinary approaches to create actionable solutions for infrastructure resilience.