Find Academic Supervisors

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.

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.

• 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.

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.

Ayşe BaşarView profile

Professor

• Machine Learning
• Bayesian Inference
• Decision Making under Uncertainty
• +7 more

Ayşe Başar is a Professor in the Department of Mechanical, Industrial, and Mechatronics Engineering at Toronto Metropolitan University. She serves as Director of the Data Science Lab and Program Director of the Data Science Masters Program. Her research integrates machine learning and Bayesian methods to build predictive models and recommender systems, with applications in software engineering and healthcare. BSc, Boğaziçi University MASc, University of Alabama in Huntsville PhD, London School of Economics Her research focuses on advancing machine learning through Bayesian inference, deep reinforcement learning, and explainability. She develops models that simulate expert decision-making, particularly in complex environments like emergency rooms and software development. Her work emphasizes uncertainty quantification , social learning in networks , and evidence-based decision support systems . The selected publications reflect a strong trend in applying machine learning to real-world problems—particularly in software engineering defect prediction, healthcare team dynamics, and social network analysis. Her work consistently uses probabilistic modeling and ensemble methods to improve prediction accuracy and interpretability. Paper among top 3 most cited in Empirical Software Engineering Journal (2009–2014) Best Paper Award, AAAI (2010) Best Paper Award, IFIP/IEEE IM Symposium (2019) Best Paper Award, ESEM (2013) Dr. Başar actively supervises students and leads impactful research collaborations with institutions like St. Michael’s Hospital and IBM. She has held leadership roles including Director of Big Data for the Provost’s Office and Program Director for Data Analytics Certificates. She is an affiliated scientist at St. Michael’s Hospital and a research fellow at IBM CAS. She leads the Data Science Lab , which focuses on developing advanced machine learning models for complex decision-making. The lab works on projects involving large-scale data integration, predictive analytics, and human-in-the-loop AI systems.

Somnath GhoshView profile

Professor

• Computational Mechanics
• Multiscale Modeling
• Integrated Computational Materials Engineering (ICME)
• +6 more

Somnath Ghosh is the Michael G. Callas Chair Professor at Johns Hopkins University, holding joint appointments in the Departments of Civil & Systems Engineering, Mechanical Engineering, and Materials Science & Engineering. He directs the Computational Mechanics Research Laboratory (CMRL) and founded the Center for Integrated Structure-Materials Modeling and Simulations (CISMMS). His research focuses on multiscale computational mechanics, materials science, and integrated computational materials engineering (ICME). Key areas include additive manufacturing, fatigue and fracture mechanics, machine learning, and uncertainty quantification. Education includes a B.Tech. from IIT Kharagpur, M.S. from Cornell University, and Ph.D. from the University of Michigan. Ghosh has led major initiatives like NASA’s Space Technology Research Institute for Additive Manufacturing (IMQCAM) and the Air Force-funded Center of Excellence in Integrated Materials Modeling (CEIMM). He has authored over 300 peer-reviewed publications, three books, and is a Fellow of multiple societies, including the AAAS, ASME, and TMS. Award highlights include the Theodore von Karman Medal (2025), J.N. Reddy Medal (2024), and Nathan M. Newmark Medal (2013). His work bridges theory and industry applications in aerospace, automotive, and defense sectors. Labs under his leadership (CMRL and CISMMS) develop digital twins and advanced modeling tools for materials qualification and design.

Tara MangalView profile

Research Fellow

• Infectious Disease Epidemiology
• Health Systems Modelling
• Global Health
• +7 more

Dr Tara Mangal is a Research Fellow in Infectious Disease Epidemiology at the School of Public Health within Imperial College London's Faculty of Medicine . Her work focuses on developing mathematical models to evaluate health interventions, particularly vaccines and resource allocation strategies in low-income settings. Dr Mangal has conducted impactful research in global health, including polio eradication efforts in Nigeria, HIV/AIDS research in Brazil, and health systems analysis in Malawi through the Thanzi la Onse project . She contributed to Imperial College's COVID-19 Response Team , analyzing pandemic management strategies and vaccination impacts. Her work integrates big data, Bayesian inference, and mixed-methods research to inform policy decisions. Research Interests: Modelling disease transmission dynamics and health system resilience Evaluating interventions for HIV, TB, malaria, and maternal health Health resource allocation using simulation models Climate change impacts on healthcare access Health system strengthening in low-income countries Recent Contributions: Thanzi la Onse model for Malawi's integrated health systems analysis WHO LENS Working Group analysis on children's roles in pandemic transmission Modelling vaccine efficacy and pandemic lockdown effects Labs/Teams: Active in Imperial College's Centre for Global Infectious Disease Analysis and collaborates with the Malawi Ministry of Health and UNAIDS.

John HughesView profile

Associate Professor

• Biostatistics
• Health Data Science
• Statistical Computing
• +6 more

John Hughes, PhD, is an Associate Professor and Chair of the Department of Biostatistics and Health Data Science at Lehigh University's College of Health. With nearly 30 years of experience in higher education, he has held positions at institutions including Frostburg State University, the University of Minnesota, and Pennsylvania State University. His methodological research focuses on statistical models for dependent data, Bayesian methods, and spatial and spatiotemporal analysis. He has developed numerous software packages for R and Perl, including copCAR , ngspatial , and krippendorffsalpha . Dr. Hughes' interdisciplinary work spans environmental health, bioimaging, vaccine hesitancy, and spatial epidemiology of HPV-related cancers. He has consulted for organizations such as the Courage Kenny Research Center and Temple University. His education includes a PhD in Statistics from Penn State University and an MS in Applied Computer Science from Frostburg State University. His research emphasizes statistical computing and the application of advanced models to health data. Recent work includes methodologies for agreement coefficients, environmental noise measurement, and spatial analysis of vaccination refusal patterns. Dr. Hughes teaches courses in biostatistics, data science, and programming, reflecting his dual role as a teacher-scholar. Professional contributions include software development, collaborative research projects, and academic leadership. His work bridges computational innovation and real-world health challenges, positioning him as a key figure in modern biostatistical research.