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Ioannis SgouralisView profile

Assistant Professor

• Data Science
• Applied Mathematics
• Mathematical Biology
• +7 more

Ioannis Sgouralis is an Assistant Professor in the Department of Mathematics at The University of Tennessee, Knoxville. He holds a Ph.D. from Duke University and specializes in Data Science and Applied Mathematics with a focus on interdisciplinary research in Physics, Chemistry, Biology, and Medicine. His research group integrates computational methods with experimental data to address challenges in biophysics, biochemistry, and biomedicine. His educational background includes a Ph.D. in a relevant field (specific details not provided). His research interests span Bayesian nonparametrics, computational statistics, mathematical biology, and multiscale modeling. He leads projects on single-molecule biophysics, image analysis, and cardiovascular system modeling. Key research areas include: Single molecule experiments and analysis Super-resolution microscopy and multi-particle tracking Biomedical systems modeling (cardiovascular/urinary) Statistical methods for noisy experimental data Ioannis actively recruits graduate/undergraduate students for projects in Data Science, Mathematical Biology, and Applied Mathematics. His group uses advanced computing techniques such as Hamiltonian Monte Carlo and topological data analysis. Labs/Teams: Sgouralis Research Group focuses on cross-disciplinary computational methods for biological systems. Their work involves collaborations across departments and institutions.

Jeremy GaskinsView profile

Associate Professor

• Bayesian Statistics
• Longitudinal Data Analysis
• Missing Data Methods
• +3 more

Jeremy Gaskins, PhD, is an Associate Professor in the Department of Bioinformatics & Biostatistics at the University of Louisville's School of Public Health and Information Sciences (SPHIS). He joined UofL in 2013 as an Assistant Professor, earning tenure and promotion to his current rank in 2019. His expertise lies in Bayesian statistical methods for complex data structures, including longitudinal analysis, missing data imputation, and joint modeling of mixed data types. He collaborates with researchers across multiple departments, including OB/GYN, Radiation Oncology, and Surgery. Education: Ph.D. (2013) in Statistics, University of Florida B.S. (2007) in Mathematics and Applied Mathematics, Auburn University Research Interests: Development of Bayesian methods for longitudinal and clustered data Computational strategies for complex model inference Applications in medical and public health research Missing data mechanisms and imputation techniques Teaching: PHST 661: Probability PHST 662: Mathematical Statistics Collaborations & Labs: Active collaborations with UofL medical departments on applied health research Focus on translational statistics for biomedical and public health problems

David SpiegelhalterView profile

Professor

• Public Understanding of Risk
• Bayesian Statistics
• Biostatistics
• +2 more

David Spiegelhalter is Professor of the Public Understanding of Risk at the University of Cambridge's Faculty of Mathematics. His research focuses on Bayesian statistics, public communication of uncertainty, biostatistics, and performance assessment in healthcare. He has contributed extensively to risk perception studies, particularly during the COVID-19 pandemic, and develops tools for statistical communication. Spiegelhalter's work bridges technical statistics with public understanding, emphasizing transparent communication of scientific uncertainty. His research interests include: Developing frameworks for trust in scientific advocacy Quantifying pandemic risks and public responses Creating accessible statistical tools for healthcare decisions His publications show consistent focus on: Public health communication strategies Philosophical foundations of probability Epidemiological modeling with recent emphasis on COVID-19 data interpretation.

John D. AlbertsonView profile

Professor

• Fluid Dynamics and Rheology
• Energy and the Environment
• Computational Fluid Dynamics
• +8 more

John D. Albertson is a Professor in the School of Civil and Environmental Engineering at Cornell University, where he has been since 2015. Previously, he served as Department Chair of Civil and Environmental Engineering at Duke University. He holds visiting appointments at the University of Cork, University of Cagliari, University of Padova, and EURAC (Italy). His research focuses on mass, energy, and momentum exchange between land and atmosphere, with applications in hydrometeorology, air quality, and model-data fusion. B.S. in Civil Engineering (SUNY Buffalo, 1985) M.B.A. in Finance (University of Hartford, 1989) M.E.S. in Hydrology (Yale University, 1993) Ph.D. in Hydrology (UC Davis, 1996) Research interests include fluid dynamics, environmental fluid mechanics, remote sensing, and sustainable energy systems. His work integrates computational methods with field measurements to advance environmental science and engineering solutions. Awards: Croll Fellow Professor (2015), Natural Resource Institute Award (2000), NASA New Investigator Award (1999) Teaching includes courses on hydrology, environmental transport processes, and smart cities. His lab (Albertson Lab) develops innovative approaches for environmental monitoring and sustainable systems.

• Applied Mathematics
• Mathematical Physics
• Condensed Matter Physics
• +9 more

Henrik Jensen is a Senior Research Investigator in the Department of Mathematics at Imperial College London's Faculty of Natural Sciences. His work spans applied mathematics, mathematical physics, and interdisciplinary complexity science. He holds affiliations with the CFM-Imperial Institute of Quantitative Finance, Centre for Complexity Science, and the Georgina Mace Centre for the Living Planet. His research focuses on emergent phenomena in systems ranging from biological populations to financial networks. Notable contributions include studies on metapopulation dynamics, epigenetic regulation in birds, and information-theoretic approaches to complex systems. Dr. Jensen has presented globally at conferences like the Brazilian Meeting of Condensed Matter Physics and the Complex 2012 conference. His expertise bridges theoretical frameworks with empirical data, particularly in evolutionary ecology and climate modeling. His current work explores multi-generational fitness effects, genomic prediction in wild populations, and the interplay between environment and genetic architecture. He has pioneered methodologies for analyzing cosmological data using group entropies and developed computational tools for genetic assignment in metapopulations. His lab integrates mathematical modeling with field studies to address challenges in conservation biology and systems biology. While no formal students are listed, his collaborative approach has generated extensive interdisciplinary publications.

Natesh PillaiView profile

Professor

• Applied Probability
• Computational Methods
• Markov Chain Monte Carlo (MCMC)
• +5 more

Natesh Pillai is a Professor in the Department of Statistics at Harvard University and a Distinguished Engineer at LinkedIn, focusing on Responsible AI. He holds a Bachelors from IIT Madras, a PhD from Duke University's Department of Statistical Science (2008), and completed a postdoc at the University of Warwick's CRiSM (2008-2010). His research spans applied probability, computational methods, MCMC theory, algorithmic fairness, and climate science. He serves on editorial boards for journals like SIAM Journal on Mathematics of Data Science and Harvard Data Science Review . Key awards include the 2018 Young Statistical Scientist Award and 2021 Fellowship in the Institute of Mathematical Statistics. His work emphasizes bridging theory and practice, with contributions to statistical methodology, causal inference, and scalable computational techniques. Recent collaborations include industry roles at Amazon (2021-2023) and interdisciplinary climate science projects analyzing agricultural yield predictability. Education: Bachelor's: Indian Institute of Technology (IIT) Madras PhD: Duke University, Department of Statistical Science Research Focus: MCMC mixing times, Bayesian methodology, reinforcement learning, climate data modeling Publications emphasize algorithmic efficiency, fairness in AI, and probabilistic frameworks for complex systems. His lab integrates theoretical rigor with real-world applications, including climate modeling and healthcare analytics.

Noah SimonView profile

Associate Professor

• Machine Learning
• High-dimensional Estimation
• Clinical Trial Design
• +4 more

Noah Simon is an Associate Professor in the Department of Biostatistics at the University of Washington School of Public Health. His research focuses on high-dimensional statistical methods, machine learning, and their applications in biomedicine. He develops computational tools for genomic and clinical data analysis, including penalized regression techniques and adaptive clinical trial designs. Education: B.A. Mathematics, Pomona College (2008) Ph.D. Statistics, Stanford University (2013), advised by Robert Tibshirani Research Interests: Dr. Simon specializes in high-dimensional estimation, algorithm optimization, and clinical trial methodology. His work addresses challenges in biomarker discovery, imaging-based diagnostics, and genomic data analysis. Key areas include sparse-group lasso regularization, adaptive enrichment designs for personalized medicine, and scalable computational methods for big data. Grants & Funding: NIH Director's Early Independence Award ($250k/year, 2014–2019) Amazon and Google Cloud Computing Grants for biomarker research Awards: Forbes 30 Under 30 in Science (2015) NSF Graduate Research Fellowship Honorable Mention (2010) Weiland Fellowship (2011–2013) Advising: He mentors PhD and MS students in biostatistical methodology and data science, with current advisees including Jean Feng, Brayan Ortiz, and Jeremy Roth. Notable collaborations include work on neural activity detection via calcium imaging (SCALPEL) and nonparametric variable importance assessment using neural networks. Lab & Affiliations: Based at the Hans Rosling Center for Population Health, his group develops open-source software (e.g., sgl , standGL ) and contributes to biomedical data science initiatives at UW.

Daniela Castro CamiloView profile

Senior Lecturer

• Extreme Value Theory
• Environmental Risk Analysis
• Spatial Statistics
• +3 more

Dr. Daniela Castro Camilo is a Senior Lecturer in Statistics at the University of Glasgow's School of Mathematics & Statistics. Her research focuses on extreme value theory applied to environmental hazards, including landslides, climate extremes, and risk assessment. She leads projects like the Mitigating Landslides Impacts in Scotland (MLIS) and Geostatistical Binary Models for Extremes (GEOBEx) , funded by the Scottish Government and EPSRC. She collaborates with institutions such as the British Geological Survey and the Met Office. Her work bridges statistical methodology and environmental applications, with notable contributions to landslide hazard modeling and spatial extremes. She actively participates in international conferences and organizes events like the ESS-sponsored session at the 2024 RSS Conference. She is a core member of GLE²N (Glasgow-Edinburgh Extremes Network), fostering interdisciplinary research in statistical risk analysis. Recent projects include developing probabilistic forecasting tools for weather-driven faults in electricity networks and advancing Bayesian methods for extreme event prediction. Her research emphasizes practical solutions for resilience to environmental disasters, combining cutting-edge statistical techniques with real-world data challenges.

Andreas MurrView profile

Associate Professor

• Political Behavior
• Public Opinion
• Political Methodology
• +3 more

Andreas Murr is an Associate Professor in Quantitative Political Science at the University of Warwick, Department of Politics and International Studies (PAIS). He joined Warwick from the University of Oxford, where he held roles such as Departmental Lecturer in Quantitative Methods and Non-Stipendiary Lecturer. His research focuses on political behavior, public opinion, and political methodology, with a strong emphasis on election forecasting. He holds a PhD in Government from the University of Essex (2013) and an MA in Public Opinion and Polling (2009). Key areas of expertise include citizen forecasting models, Bayesian statistical methods, and the application of Condorcet’s Jury Theorem. His work has been published in journals like International Journal of Forecasting , Electoral Studies , and Political Analysis . He has supervised PhD students in interdisciplinary projects and contributed to software development, including the R package hot.deck . Educations: PhD in Government (Essex, 2013), MA in Public Opinion and Polling (Essex, 2009), Undergraduate in Political Science, Economics, and Statistics (Bamberg, 2008). His research interests span electoral behavior, forecasting methodologies, and the impact of social networks on public opinion. He has secured grants such as the Oxford University Press John Fell Fund and the ESRC award, and has been recognized with fellowships including the Higher Education Academy (2019). He actively participates in academic service roles at Warwick, including Director of Undergraduate Studies and co-director of interdisciplinary degree programs. His work bridges theoretical political science with practical forecasting, influencing both academic discourse and public understanding of elections.

Didong LiView profile

Assistant Professor

• Biostatistics
• Machine Learning
• Geometric Data Analysis
• +7 more

Dr. Didong Li is an Assistant Professor in the Department of Biostatistics at the Gillings School of Global Public Health , University of North Carolina at Chapel Hill. His research focuses on statistical methods for complex data, including manifold learning, nonparametric Bayesian inference, and spatial statistics, with applications in healthcare, genomics, and environmental health. He holds a PhD in Mathematics from Duke University (2020), and degrees from Beijing Institute of Technology (BS 2012, MS 2015). His research interests include dimension reduction, Gaussian processes, geometric data analysis, and network analysis. He has developed novel methods for analyzing electronic health records, single-cell RNA sequencing data, and spatial omics datasets. Notable contributions include the spherelet manifold approximation framework and the STimage-1K4M dataset. Dr. Li has received the Inaugural IMS Lawrence D. Brown PhD Student Award (2019) . He serves on the editorial board of the Journal of Machine Learning Research and organized sessions at JSM 2021-2022 on geometric Bayesian methods. His lab supports interdisciplinary collaborations, with ongoing projects in spatial transcriptomics, healthcare access analysis, and AI-driven medical informatics. Advising highlights include guiding students through successful PhD defenses (e.g., Dr. Sam Hawke, Dr. Jiawen Chen) and postdoctoral placements. His team secured a NVIDIA Academic Grant (2024) for multi-modal AI in spatial transcriptomics. The Li Lab maintains active partnerships with institutions worldwide, advancing both statistical theory and real-world applications.

• Systemic Risk
• Financial Risk Management
• Credit Scoring
• +4 more

Christophe Hurlin is a Professor at the University of Orleans specializing in finance, risk management, and econometrics. His research spans systemic risk measurement, credit scoring models, machine learning applications in finance, and computational reproducibility in financial research. With over 30 scholarly publications, he has established himself as a significant contributor to financial econometrics and risk management literature. His primary research interests focus on systemic risk measurement methodologies, credit scoring models incorporating machine learning techniques, and the reproducibility of financial research findings. Hurlin's work often bridges theoretical econometric frameworks with practical financial applications, particularly in banking regulation and risk modeling. He has developed innovative tools such as the Risk Map for validating risk models and has contributed significantly to understanding systemic risk through comprehensive surveys and comparative analyses of risk measures. Hurlin's publication record shows a clear evolution from traditional risk measurement techniques toward integrating machine learning approaches in finance, with recent work focusing on the fairness of credit scoring models, Bayesian approaches to default probability calibration, and the intersection of machine learning with regulatory capital requirements. His research demonstrates consistent engagement with both theoretical underpinnings and practical applications in financial risk management. Hurlin has collaborated extensively with researchers across Europe, particularly with colleagues from HEC Paris (notably Christophe Pérignon), creating a substantial body of work on systemic risk, credit scoring, and computational reproducibility. His most cited works include 'Where the Risks Lie: A Survey on Systemic Risk' and 'Nonstandard Errors,' reflecting his influence in both risk management and methodological research. His research has practical implications for banking regulation, financial stability monitoring, and the implementation of machine learning in credit assessment systems. Through his work on computational reproducibility, Hurlin has also contributed to improving research standards in financial economics, advocating for greater transparency and verification in published findings.

• Financial Econometrics
• Volatility Modeling
• Time Series Analysis
• +4 more

Andrew J. Patton is a Professor in the Department of Economics at Duke University's Trinity College of Arts & Sciences, with a secondary affiliation at Singapore Management University's School of Economics. His office is located at 213 Social Sciences Building, Box 90097, Durham, NC 27708-0204, United States, and his professional website is available at http://econ.duke.edu/~ap172/. Dr. Patton's research focuses on financial econometrics, with particular expertise in volatility modeling, high-frequency data analysis, and dependence structures in financial markets. His work spans several key areas including realized variance methods, copula modeling for financial time series, risk management techniques, and forecast evaluation methodologies. He has made significant contributions to understanding market microstructure, hedge fund risk exposures, and the dynamics of financial correlations. His publication record demonstrates consistent output in top finance and econometrics journals, with recent work exploring machine learning applications in volatility forecasting, granular risk measures, and the impact of high-frequency data on asset pricing models. The research shows a clear trajectory toward increasingly sophisticated modeling of financial dependencies and risk structures. Dr. Patton has received recognition for his scholarly contributions, ranking among SSRN's top 1,427 authors by total paper downloads and top 1,090 by total paper citations. His work is frequently cited in the field of financial econometrics, indicating substantial influence on contemporary research. He maintains active research collaborations with leading economists including Tim Bollerslev, Kevin Sheppard, Tarun Ramadorai, and Robert Engle, among others. These collaborations span multiple institutions and have produced influential work on volatility modeling, risk measurement, and financial market dynamics.

Anders SkrondalView profile

Professor

• Psychometrics
• Statistics
• Biostatistics
• +3 more

Anders Skrondal is a Professor II at the University of Oslo's Faculty of Educational Sciences, affiliated with the Centre for Educational Measurement (CEMO). He also serves as a Senior Scientist at CEFH (Research Council of Norway Centre of Excellence) at the Norwegian Institute of Public Health and Co-Principal Investigator at CREATE, another Norwegian Centre of Excellence. His academic journey includes roles as Head of the Biostatistics Group at the Norwegian Institute of Public Health and Professor of Statistics at the London School of Economics (LSE), where he directed the Methodology Institute. Skrondal's research focuses on psychometrics, statistics, biostatistics, and econometrics, with a major contribution being the development of the GLLAMM framework. He has authored 14 books and over 200 peer-reviewed papers, achieving an h-index of 63 and 30,000+ citations. His awards include the 1997 Psychometric Society Dissertation Prize and leadership roles in prestigious organizations like the Psychometric Society and Royal Statistical Society. Research Interests: Skrondal specializes in statistical methodologies including latent variable modeling, multilevel modeling, and missing data analysis. His work bridges theoretical advancements and practical applications in medicine, psychology, and social sciences. He is renowned for integrating latent variable and mixed model frameworks to address complex data structures. Recent trends in his publications emphasize methodological solutions for missing data, non-ignorable mechanisms, and psychometric model validation. His articles span statistical theory, medical applications, and educational measurement. Awards: President, Psychometric Society (2016–2017) Elected Member, International Statistical Institute Outstanding Academic Title for 'The Cambridge Dictionary of Statistics' (2011) Fulbright Professor at UC Berkeley (2013–2014) Advising & Grants: Skrondal has led major research initiatives such as CEFH and CREATE, funded by the Research Council of Norway. While no specific advisee list is provided, his collaborations span international institutions. His work on GLLAMM software is used in over 750 journals, reflecting widespread academic impact. Labs/Teams: Active in CEMO and CEFH, he contributes to interdisciplinary teams advancing educational measurement and public health research. His involvement in CREATE focuses on equality in education through statistical innovations.

• Medical Physics
• Radiation Oncology
• Proton Therapy
• +3 more

Sara Margareta Cecilia Pilskog serves as an Associate Professor in the Department of Physics and Technology at the University of Bergen, Norway, with dual affiliation at Haukeland University Hospital's Department of Cancer Treatment and Medical Physics. Her research bridges theoretical medical physics and clinical oncology applications, focusing on precision radiotherapy techniques and biological optimization. Her primary research interests center on proton therapy innovation, where she investigates biological optimization strategies using linear energy transfer (LET) and relative biological effectiveness (RBE) modeling. She develops adaptive radiotherapy frameworks to address inter-fractional motion in pelvic cancers, particularly prostate and rectal malignancies. Her work also pioneers neutron-based in-vivo range verification systems and statistical deformation models for dose accumulation. Current projects emphasize reducing treatment margins through anatomical robustness and optimizing biological dose distributions for organ sparing. Analysis of her 15 most recent publications reveals a dominant focus on improving proton therapy precision for pelvic cancers through biological modeling and motion management. Approximately 70% of her work addresses prostate cancer applications, with significant contributions to adaptive strategies for inter-fractional changes and biological optimization techniques. Her research consistently employs Monte Carlo simulations (particularly FLUKA) and clinical data analysis to translate theoretical models into clinically viable solutions. No scientific awards were documented in the provided materials. While specific advising details remain unreported, her collaborative patterns indicate active mentorship within the University of Bergen's medical physics research ecosystem. Her publications consistently involve junior co-authors from clinical physics teams at Haukeland University Hospital, suggesting hands-on supervision of technical staff and research fellows in radiotherapy innovation projects. Grant funding appears primarily channeled through institutional hospital-university partnerships focused on clinical translation of advanced radiotherapy techniques. Dr. Pilskog operates within the University of Bergen's medical physics research cluster that maintains close operational ties to Haukeland University Hospital's radiotherapy department. This integrated academic-clinical environment enables direct implementation of her research on adaptive proton therapy and biological optimization into clinical workflows, with particular emphasis on pelvic cancer treatment protocols. The team utilizes advanced Monte Carlo simulation platforms and clinical treatment planning systems to develop and validate next-generation radiotherapy approaches.

Ruitao LinView profile

Assistant Professor

• Bayesian adaptive design
• Robust Bayesian methods
• Meta-analytic trial design
• +6 more

Dr. Ruitao Lin is an Assistant Professor in the Department of Biostatistics at The University of Texas MD Anderson Cancer Center and an Adjunct Assistant Professor at Rice University. He holds a PhD in Biostatistics from The University of Hong Kong (2012–2016) and completed postdoctoral training at the University of Washington and MD Anderson. His research focuses on Bayesian adaptive designs, robust statistical methods, and clinical trial methodologies, particularly in oncology and precision medicine. He leads the development of novel Bayesian approaches for dose-finding in phase I/II trials, meta-analytic trial designs, and high-dimensional data analysis. Dr. Lin is an Associate Editor for Contemporary Clinical Trials , Biometrical Journal , and Pharmaceutical Statistics , and has contributed to software tools like the BOIN design suite and TITE-BOIN12. His work bridges statistical innovation with practical clinical challenges, including late-onset toxicities in immunotherapy and heterogeneous treatment effects. He has secured NIH funding (e.g., R01CA261978) and collaborated with academic and industry partners to advance trial design and analysis. His software platforms, including R packages and Shiny apps, enable clinicians to implement cutting-edge statistical methods in real-world settings. Education : PhD, Biostatistics, The University of Hong Kong (2016); Postdocs at University of Washington (2016–2017) and MD Anderson (2017–2019). Grants : NIH/NCI R01CA261978 (2021–2025), focusing on Bayesian methods for precision oncology trials. Teaching : Teaches clinical trial design courses at Rice University and UTHealth. Labs/Software : Developed the BOIN design suite, TITE-BOIN12, and other tools for dose-finding and phase I/II trials.