Find Academic Supervisors

• Data-driven building performance
• Energy demand reduction
• Sustainable design
• +7 more

Dr Virginia Gori is a Lecturer in Buildings and Energy at the Bartlett School Env, Energy & Resources, University College London. She leads Subtask D of the IEA Annex 81 'Data-Driven Smart Buildings' programme, focusing on mapping technology landscapes and evaluating data-driven smart building policies. PhD in Energy and the Built Environment (UCL, 2017) MRes in Built Environment Energy Demand Studies (UCL, 2014) BSc in Architectural Engineering (Universita degli Studi di Firenze, 2010) Her research combines building physics , in-situ monitoring , and Bayesian statistics to advance energy demand reduction in the built environment, particularly for historic buildings . She has developed novel Bayesian frameworks for thermophysical property estimation and whole-building performance analysis. Data-driven smart buildings Thermophysical property modeling Risk management for energy-moisture balance Conservation-compatible retrofit Scientific recognition includes: EPSRC Doctoral Prize Fellowship (2018-2020) UCL BEAMS Overseas Doctoral Award (2013) DAAD/DLR Research Fellowship (2021) Teaching contributions: Module lead: Introduction to Smart Energy Data and Statistics (2023-) Module lead: Smart Energy Solutions in the Built Environment (2023-) Former module lead: Building Physics & Energy (2019-2023) Co-designed Fundamentals of Building Physics course (6 years) Professional roles: Associate Editor, Buildings & Cities journal Technical committee member, CEN TC89WG13 Visiting Scholar, UC Berkeley (2019) Active in IEA Annex 71, Task 59, and Annex 81

Raquel PradoView profile

Professor

• Statistics
• Bayesian Statistics
• Time Series Analysis
• +3 more

Raquel Prado serves as a Professor of Statistics in the Department of Statistics at the Baskin School of Engineering, University of California, Santa Cruz. Her office is located in E2 521 of the Baskin Engineering building, and she can be contacted via telephone at 831-459-1488 or email at raquel@soe.ucsc.edu. Her research spans foundational and applied statistical methodologies with emphasis on: Bayesian inference and modeling Time series forecasting and analysis Machine learning algorithms Data science applications Computational statistics Statistical computing As a core faculty member in Statistics, she contributes to advancing quantitative research methodologies across interdisciplinary domains while maintaining active engagement in academic instruction and departmental leadership within the Baskin School of Engineering ecosystem.

• Multidimensional Scaling
• Market Segmentation
• Market Structure
• +4 more

Joonwook Park serves as a Full-Time Professor of Practice in the Marketing Department at Southern Methodist University, where he teaches Managerial Statistics (MAST 6201). Education PhD in Marketing, Penn State University Research Focus His scholarly work centers on advanced statistical methodologies for marketing analysis, with particular emphasis on: Multidimensional Scaling for visualizing market structures Market Segmentation techniques to identify consumer clusters Modeling Market Structure dynamics Quantifying Consumer Preference Heterogeneity Analyzing Consumer Purchase Behavior patterns Applying Bayesian statistics to marketing problems Developing innovative Research Methods These interconnected domains demonstrate a cohesive research program bridging statistical theory and practical marketing applications.

Ford RamseyView profile

Associate Professor

• Agricultural Economics
• Econometrics
• Crop Insurance
• +4 more

Ford Ramsey serves as an Associate Professor in the Department of Agricultural and Applied Economics at the University of Georgia's College of Agricultural and Environmental Sciences. Based at Conner Hall in Athens, Georgia, his research program addresses critical challenges in agricultural economics through advanced quantitative methods, with particular focus on risk management systems and market dynamics in food production sectors. Research Focus: Dr. Ramsey's scholarly work spans multiple interconnected domains: Econometric modeling of agricultural risk and insurance mechanisms Labor market volatility in meat processing and food manufacturing Climate change impacts on crop yield stability and adaptation strategies Historical analysis of agricultural market integration and price formation Bayesian statistical applications in yield forecasting and policy evaluation Regulatory impacts on livestock and grain market efficiency His methodological approach frequently combines traditional econometric techniques with machine learning innovations to analyze complex agricultural systems. Publication Trends: Examination of his 2023-2025 publications reveals three dominant research streams: (1) Climate-agriculture interactions, particularly warming effects on wheat production and adaptation through genetic diversity; (2) Supply chain resilience, with emphasis on labor dynamics during disruptions in food manufacturing; and (3) Crop insurance innovation, focusing on Bayesian forecast combination methods and historical weather integration. These strands consistently demonstrate policy relevance through analysis of regulatory impacts, market integration, and risk management solutions for agricultural producers. Professional Activities: While specific advising relationships and grant details aren't documented in available sources, Dr. Ramsey maintains active engagement through the Agricultural & Applied Economics department's research initiatives. His work connects with broader university efforts in agricultural risk management and food system analysis, as evidenced by departmental affiliations and publication themes.

Christopher BrunsdonView profile

Professor

• Spatial Statistics
• Data Science
• Spatial Analysis
• +3 more

Christopher Brunsdon is Professor of Geocomputation and Director of the National Centre for Geocomputation at Maynooth University's Faculty of Social Sciences. Previously, he held positions at the University of Liverpool, Leicester, Glamorgan, and Newcastle. His academic credentials include a BSc in Mathematics from Durham University and an MSc in Medical Statistics and PhD in Geography from Newcastle University. Research interests focus on spatial statistics, data science, and spatial analysis, with emphasis on open-source software (especially R) and reproducible methods. Applied domains include crime pattern analysis, health geography, and environmental applications. Current projects include 'Mapping Lineages: Quantifying the Evolution of Maps of the British Isles' and 'Building city dashboards'. Brunsdon's recent publications (2018-2020) demonstrate strong methodological contributions to geocomputation, spatial regression, and geodemographics. Trends include reproducibility in spatial science, multiscale modeling, and applications in health geography and urban analytics. He leads the National Centre for Geocomputation, fostering innovation in spatial data science. Grant leadership includes an ERC Advanced Grant (RIGAR) and industry collaborations like consultancy for SiteZeus.

• Statistics
• Psychology
• Bayesian Statistics
• +2 more

Suzanne Hoogeveen serves as an Assistant Professor in the Department of Methodology and Statistics within the School of Social Sciences at Utrecht University's Faculty of Social and Behavioural Sciences. Her primary institutional affiliation and contact email is s.hoogeveen@uu.nl. Her research expertise centers on quantitative methodologies with specific emphasis on: Bayesian Statistics Cognitive Modelling Open Science practices Psychological research methods Statistical methodology She actively promotes reproducible research through Open Data initiatives and advanced statistical modeling techniques applicable to social sciences. No scientific awards or honors were documented in the provided institutional records. Regarding academic mentorship and research funding, the source materials contain no references to supervised students, grant acquisitions, or sponsored research projects. Information regarding laboratory facilities, research teams, or collaborative groups was not disclosed in the available university profiles.

Yajuan SiView profile

Researcher

• Bayesian statistics
• survey inference
• missing data analysis
• +5 more

Yajuan Si is a Researcher at the University of Michigan , affiliated with the School of Public Health and the Department of Biostatistics . Her work focuses on advancing statistical methodologies in survey inference and data analysis. Education: PhD from Duke University (2012) Email: yajuan@umich.edu Address: ISR 4014, 426 Thompson St, Ann Arbor, MI 48104 Research Interests Dr. Si specializes in Bayesian statistics , survey inference , missing data analysis , and confidentiality protection techniques . Her projects address critical challenges such as: Statistical adjustments for nonresponse bias in complex surveys Enhancing synthetic data methods for privacy-preserving analysis Population heterogeneity correction in neuroimaging studies Unifying multilevel regression and poststratification frameworks Selected Publications Her recent work spans Bayesian modeling applications in education surveys, massive data imputation , and healthcare data analysis . Key themes include survey weighting, computational scalability, and bias correction. Collaborative Efforts Dr. Si collaborates with multidisciplinary teams across public health, economics, and social sciences, applying her statistical expertise to real-world problems in COVID-19 transmission modeling and demographic research .

Rancoita Paola Maria VittoriaView profile

Associate Professor

• Biostatistics
• Bioinformatics
• Survival Data Analysis
• +6 more

Rancoita Paola Maria Vittoria is an Associate Professor of Medical Statistics (MED/01) at the Faculty of Medicine and Surgery, Vita-Salute San Raffaele University (UniSR), since 2022. She is actively involved in research activities at the University Center for Statistics for Biomedical Sciences (CUSSB) and serves on the Board of Directors of CUSSB (since 2016) and the Scientific Committee of CeNRI (Center for Nursing Research and Innovation, since 2016). She also participates in academic governance through multiple committees in the International MD Program (since 2020) and the Doctoral School in Public Health at the University of Milan-Bicocca (since 2020). Research Focus : Her work centers on developing and applying statistical methodologies in biostatistics and bioinformatics, including survival analysis with frailty models, repeated measures and clustered data analysis, genomic data integration via Bayesian regression, and stochastic geometry for angiogenesis image analysis. These techniques address clinical challenges like missing data handling and prognostic index identification. Academic Background : PhD in Mathematics and Statistics for Computational Sciences (2010), University of Milan Post-doctoral Researcher (2010-2011), IDSIA/IOSI Switzerland Research Fellow (2012-2013), Statistics SECS-S/01, Faculty of Psychology, UniSR Fixed-Term Researcher (2014-2021), Medical Statistics MED/01, UniSR Scientific Recognition : WHO Statistical Consultant (2022) Task Force Member, Stop TB Partnership (2020) ISBA Travel Award (2010) INdAM Fellowship (2004-2006) Leadership Roles : She served as Treasurer in the Italian Region of the International Biometric Society (2018-2021) and held leadership roles in Società Italiana di Statistica Medica ed Epidemiologia Clinica (2020-2021). Her teaching spans statistics, bioinformatics, and clinical data science across undergraduate and postgraduate programs at UniSR and Milan-Bicocca University.

Yi-Hsin ChenView profile

Professor

• Educational Measurement
• Psychometrics
• Cognitive Diagnostic Modeling
• +3 more

Dr. Yi-Hsin Chen is a Professor in the Department of Educational Measurement and Research at the College of Education, University of South Florida (USF), located on the Tampa campus in office EDU 362. Her teaching responsibilities include: EDF 6432: Foundations of Measurement (Online) EDF 6407: Statistical Analysis for Educational Research I (Online) EDF 7439: Item Response Theory EDF 7469: Computer-Based Testing EDF 7436: Rasch Models Her primary research focuses on cognitive psychometric models including GDINA, LCDM, and DINA, which integrate cognitive attributes into measurement frameworks. She specializes in diagnostic classification modeling, cross-cultural validation of educational constructs, and development of computer-based assessments. Her work bridges theoretical psychometrics with practical applications in mathematics and literacy assessment. Analysis of her recent publications reveals consistent innovation in cognitive diagnostic modeling, particularly in mathematics education (TIMSS, van Hiele geometry), orthographic processing, and cross-cultural validation. Her research increasingly incorporates computational methods like Bayesian estimation and R programming while addressing emerging challenges such as AI-generated test items. The interdisciplinary nature of her work spans educational psychology, statistics, and international assessment.

• Forensic Science
• Epigenetics
• Genetics
• +3 more

Professor Graham Williams (BSc, MSc, PhD, SFHEA, FCSFS, FRSB) is a Professor of Forensic Science and Programme Director for Forensic Science at the University of Hull since May 2022. Previously served as Head of Department at Staffordshire University (2017-2019) and held academic roles at University of Huddersfield (2007-2017). Key affiliations include Centre for Biomedicine and Chartered Society of Forensic Sciences . Core Research Areas : Forensic Epigenetics, Body Fluid Identification, Bloodstain Pattern Analysis, AI in Forensic Science, Space Forensics Scientific Awards : Fellow of the Royal Society of Biology (2014) Senior Fellow of Advance HE (2017) Fellow of Chartered Society of Forensic Sciences (2018) UKCGE Recognised Research Supervisor (2021) Teaching Leadership : Directs BSc Forensic Science programs and teaches Crime Scene to Court Room modules. Supervises 8 PhD projects exploring: AI-based bloodstain analysis Forensic strategies for sexual assault cases DNA on counterfeit banknotes Microgravity bloodstain dynamics Epigenetic hearing loss research

• Machine Learning
• Artificial Intelligence
• Model Visualization
• +3 more

Martin Mozina serves as an Assistant Professor and Researcher at the Faculty of Computer and Information Science, University of Ljubljana, where he has been affiliated with the Artificial Intelligence Laboratory since 2004. His primary institutional role focuses on advancing machine learning methodologies integrated with domain knowledge. His research centers on developing machine learning algorithms that incorporate prior knowledge alongside training data, with significant contributions to model visualization through nomograms and automated chess position analysis. Key innovations include argument-based machine learning frameworks and techniques for resolving knowledge acquisition bottlenecks in intelligent systems. Analysis of his 2004-2009 publications reveals consistent emphasis on interpretable AI, combining symbolic reasoning with statistical learning. His work bridges theoretical machine learning with practical applications in medical diagnostics, game AI, and decision support systems, particularly through nomogram-based classifier visualization and chess tutoring systems. He actively participates in major research initiatives including: DRIFT (L2-4436): Real-time optimization of low-voltage networks using deep incentivized learning (2022-2025) Umetna inteligenca in inteligentni sistemi: Agency-funded AI research program (2015-2020) Strojno učenje v gradnji inteligentnih sistemov: Machine learning for intelligent tutoring systems (2011-2014, 2013-2014) Molecular markers for lung cancer research (2011-2014, 2016-2018) X-MEDIA: Large-scale knowledge sharing across media (EU project, 2006-2009) Mozina's work within the Artificial Intelligence Laboratory demonstrates sustained focus on making machine learning more transparent and applicable to complex real-world problems through hybrid symbolic-statistical approaches.

Benedikt EhingerView profile

Assistant Professor

• Computational Cognitive Science
• Visual Cognitive Science
• EEG Analysis
• +5 more

Benedikt Ehinger is a Tenure-Track Professor for Computational Cognitive Science at the Stuttgart Center for Simulation Science (SC SimTech) and the Institute for Visualization and Interactive Systems (VIS) at the University of Stuttgart. His research bridges cognitive neuroscience, computational modeling, and visualization techniques to understand visual perception and decision-making processes. Education 2018: PhD in Cognitive Science from University of Osnabrück with thesis "Predictions, Decisions and Learning in the visual sense" 2013: Master of Science in Cognitive Science from University of Osnabrück with thesis "Filling in Blind-Spots: A psychophysical and an EEG study" 2011: Bachelor of Science in Cognitive Science from University of Osnabrück with thesis "Electrophysiological Correlates of Category Learning" Research Interests Ehinger's research focuses on the intersection of visual cognitive science, computational modeling, and neuroimaging techniques. His work primarily investigates predictive coding mechanisms in visual perception, statistical learning in visual scenes, eye movement control, method development for combined EEG and eye-tracking analyses, visual completion phenomena like the blind spot, and category learning and neural plasticity. His approach combines behavioral experiments, EEG recordings, eye-tracking, and advanced statistical modeling to uncover the computational principles underlying human visual cognition. Publication Trends Ehinger's publication record shows a clear evolution from foundational work on visual perception and category learning toward methodological innovations in neuroimaging analysis. His early work focused on visual completion phenomena, category learning, and melanopsin modeling. More recently, he has pioneered techniques for analyzing combined EEG and eye-tracking data, developing toolboxes like "unfold" that address critical challenges in temporal overlap correction and regression-based analysis. His research demonstrates a consistent thread of applying computational approaches to understand visual cognition while simultaneously advancing the methodological toolkit of cognitive neuroscience. Scientific Contributions Development of the "unfold" toolbox for overlap correction and regression-based EEG analysis Creation of the EEGVIS toolbox for EEG visualization Establishment of comprehensive eye-tracking test batteries for validating mobile eye-tracking devices Innovative approaches to modeling fixation durations and eye movement patterns Research Environment Ehinger leads the Computational Cognitive Science group within the Institute for Visualization and Interactive Systems at the University of Stuttgart. His work is situated at the intersection of cognitive science, neuroscience, and computer science, collaborating with researchers across these disciplines. His lab utilizes behavioral experiments, EEG, eye-tracking, and computational modeling to investigate visual cognition, with emphasis on open science practices and methodological transparency.

Lin LinView profile

Associate Professor

• Biostatistics
• Bioinformatics
• Statistical Science
• +7 more

Lin Lin is an Associate Professor of Biostatistics & Bioinformatics at Duke University's Division of Integrative Genomics and an Associate Research Professor of Statistical Science in Trinity College of Arts & Sciences. With appointments dating from 2022 to present, Dr. Lin has established herself as a prominent researcher at the intersection of statistics, bioinformatics, and biomedical applications. Her work spans multiple departments and research centers at Duke, reflecting her interdisciplinary approach to solving complex biological problems. Ph.D. from Duke University (2012) Dr. Lin's research focuses on developing advanced statistical and machine learning methods for analyzing complex biological data, particularly in immunology and transplantation research. Her expertise in single-cell data analysis, cytometry data interpretation, and biomarker discovery has led to significant contributions in vaccine studies, HIV/AIDS research, and organ transplantation. She has pioneered methods for handling small cohort studies, longitudinal data, and multi-modal datasets, addressing critical challenges in modern biomedical research where traditional statistical approaches fall short. Analysis of Dr. Lin's publication record reveals a strong emphasis on developing interpretable computational methods that bridge the gap between complex data and biological insights. Her recent work shows increasing sophistication in handling high-dimensional single-cell data, with a particular focus on creating models that maintain interpretability while achieving high predictive accuracy. The trajectory of her research demonstrates a consistent pattern of addressing methodological challenges in biomedical data analysis, with applications spanning immunology, transplantation medicine, and infectious disease research. Dr. Lin has secured substantial research funding from multiple prestigious sources including the National Institutes of Health, National Institute of Allergy and Infectious Diseases, National Heart, Lung, and Blood Institute, and National Institute of Environmental Health Sciences. Her grants portfolio demonstrates expertise across diverse biomedical domains, from HIV/AIDS research to transplantation immunology and environmental health effects. These projects typically involve developing novel statistical methodologies while addressing pressing clinical questions, showcasing her ability to bridge theoretical statistics with practical biomedical applications. As an educator, Dr. Lin teaches advanced courses in Bayesian statistical modeling and analysis, contributing to the training of the next generation of biostatisticians and data scientists. Her research group likely focuses on developing computational tools that address real-world challenges in biomedical data analysis, with particular emphasis on making complex models interpretable and applicable to clinical settings.

• Bayesian Statistics
• Machine Learning
• Approximate Inference Methods
• +8 more

Dennis Prangle is an Associate Professor in Statistics at the University of Bristol, conducting research at the intersection of Bayesian statistics and machine learning. His academic profile demonstrates expertise in developing novel computational inference methods with applications across multiple scientific domains. Dr. Prangle's primary research interests include: Approximate inference methods such as simulation-based inference and variational techniques Likelihood-free inference through Approximate Bayesian Computation (ABC) Experimental design for high-dimensional problems Applications in population genetics, physics, ecology, and epidemiology Stochastic differential equations and composite likelihood approaches His publication record shows consistent methodological contributions with increasing integration of machine learning techniques. Recent work focuses on normalizing flows with flexible tails for improved density estimation, Bayesian emulation of complex systems, and optimal combination of composite likelihoods. His research bridges theoretical statistics with practical applications in financial modeling, infrastructure engineering, and fair classification algorithms. Dr. Prangle maintains an active academic blog where he discusses technical aspects of Bayesian statistics, experimental design, and computational methods, demonstrating his commitment to scholarly communication. His detailed posts on topics like Fisher information gain versus Shannon information gain in experimental design highlight his theoretical contributions to the field.

Abhraneel SarmaView profile

Researcher

• Uncertainty Visualization
• Data Analysis
• Multiverse Analysis
• +3 more

Abhraneel Sarma is a PhD candidate in Computer Science at Northwestern University, advised by Professors Matthew Kay and Jessica Hullman. His research focuses on addressing uncertainty in data analysis through tools like multiverse (an R package for sensitivity analysis) and Milliways (a visualization system for multiverse analysis validation). Uncertainty visualization Multiverse analysis Bayesian inference Reproducibility in research His work combines empirical studies and system development to improve data-driven decision-making. Publications include best paper awards at CHI 2019 and honorable mentions at CHI 2024, CHI 2023, and VIS 2022. 2025 : CHI 2024 : CHI (Best Paper Honorable Mention) 2023 : CHI (Best Paper Honorable Mention) 2022 : VIS (Best Paper Honorable Mention) 2019 : CHI (Best Paper Award) Email: abhraneel@u.northwestern.edu