Find Academic Supervisors

• Nonparametric function and density estimation
• Shape-restricted inference
• Constrained regression models
• +2 more

Mary C. Meyer is a Professor in the Department of Statistics at Colorado State University. Her research focuses on nonparametric function and density estimation, particularly under shape restrictions, with applications across ecological modeling, survey methodology, and statistical computing. Education: Ph.D. in Statistics from the University of Michigan (1996) Research Interests: Nonparametric function and density estimation Shape-restricted inference Constrained regression models Bayesian nonparametric methods Penalized isotonic regression Statistical software development Recent Publications show a focus on constrained regression splines, domain mean estimation, and applications in forest dynamics and survey methodology. She has developed several R packages including cgam and tools for cone projections. Grants: Funded by NSF grant DMS-0905656 for research on constrained optimization algorithms. Email Contacts: meyer@stat.colostate.edu and mmeyer@stat.uga.edu

Shuo YuView profile

Assistant Professor

• Health Analytics
• Interpretable Deep Learning
• Text Mining
• +4 more

Shuo Yu is an Assistant Professor of Information Systems and Quantitative Sciences at the Rawls College of Business, Texas Tech University, and serves as the Wetherbe Professor in Management Information Systems. He directs the MIS Doctoral Program and teaches courses such as Big Data Security, Information Security, and Data Analytics Tools. Yu holds a PhD from the University of Arizona (Management Information Systems) and a BBA from Tsinghua University (Information Management). His research focuses on data science applications in healthcare analytics, cybersecurity, and e-commerce. Key areas include interpretable deep learning, text mining, and mobile health technologies. Notable projects include SilverLink, a smart home monitoring system for senior care using wearable sensors and machine learning. Yu’s publications span journals like MIS Quarterly and IEEE Transactions, emphasizing innovations in health profiling, fall prevention, and recommendation systems. His work integrates techniques such as Hidden Markov Models, Generative Adversarial Networks, and Bayesian networks. No scientific awards are listed. He is actively involved in advising doctoral students and contributes to the Association for Information Systems (AIS).

Lucrezia ReichlinView profile

Professor

• Monetary Policy
• Applied Macroeconomics
• Econometrics (Time Series)
• +3 more

Lucrezia Reichlin is a Professor of Economics at the London Business School , where her research bridges macroeconomic policy and advanced econometric techniques. She holds key advisory roles as a non-executive director for major financial institutions like UniCredit Banking Group and Eurobank Ergasias SA, and chairs the Scientific Council at the Brussels-based think-tank Bruegel . Previously, she served as Director General of Research at the European Central Bank (2005-2008) and held academic positions at the Université Libre de Bruxelles and Columbia University. Education: Ph.D. in Economics, New York University Reichlin is a pioneer in now-casting and high-dimensional time series analysis , developing econometric models to process real-time economic data. Her work has been adopted by central banks and private investors globally, focusing on monetary policy evaluation , business cycle dynamics , and forecasting methodologies . She co-founded the firm Now-Casting Economics Ltd in 2011 to commercialize these techniques. Scientific Awards & Honors: 2016 Birgit Grodal Award of the European Economic Association 2016 Isaac Kerstenetzky Scholarly Achievement Award International Economics Award, Chamber of Commerce of Genoa (2015) Grand Ufficiale dell'Ordine della Stella d'Italia (2015) Fellow, British Academy Fellow, European Economic Association Distinguished Fellow, CEPR As a columnist for Corriere della Sera and member of the Commission Economique de la Nation (France), she influences public and policy debates. She also founded the Ortygia Foundation , promoting female education in southern Italy. Her methodological innovations in dynamic factor models , Bayesian VARs , and shrinkage techniques have reshaped modern macroeconomic analysis.

• Statistical Education
• Environmental Modeling
• Science Communication
• +2 more

Dr. Rachel King is an Associate Professor in the School of Mathematics, Physics and Computing at the University of Southern Queensland. Her applied research spans statistical applications in environmental science, public health, and agricultural risk management. She develops spatial modeling frameworks for ecological conservation and designs UV exposure assessment methodologies for skin cancer prevention. Her research group focuses on creating statistical tools for environmental decision-making, including wildlife corridor design, drought forecasting systems, and climate risk models for agriculture. Current projects investigate human-wildlife conflict mitigation and optimize bushfire preparedness strategies. Publication analysis shows strong emphasis on interdisciplinary collaboration, blending advanced statistics with practical environmental applications. Recent methodological innovations include INLA-SPDE spatial models and electronic UV monitoring systems. She leads community engagement initiatives on disaster preparedness and mentors graduate students in statistical applications for environmental science.

Joseph IbrahimView profile

Professor

• Biostatistics
• Bayesian Survival Analysis
• Prior Elicitation
• +6 more

Joseph Ibrahim is an Alumni Distinguished Professor in the Department of Biostatistics at the Gillings School of Global Public Health, University of North Carolina at Chapel Hill, where he has served since 2002. He currently holds dual leadership roles as Director of Graduate Studies for the Department of Biostatistics and Director of the Biostatistics for Research in Genomics and Training Grant. His methodological innovations in Bayesian survival analysis and missing data methodologies have significantly advanced public health research, particularly in cancer genomics applications. Professor Ibrahim's research program centers on developing statistical frameworks for complex clinical and genomic data. His seminal contributions include Bayesian cure rate models, prior elicitation techniques, and diagnostic tools for high-dimensional survival analysis. Current work focuses on integrating multi-omics data with longitudinal tumor burden metrics and refining adaptive clinical trial designs for biomarker-driven populations. These methodologies directly address critical challenges in precision oncology and pharmacovigilance, enabling more robust inference from real-world evidence. His 2025 publications reveal three dominant trends: (1) Advancements in cure rate modeling for joint longitudinal-survival data with change points, (2) Computational innovations for high-dimensional penalized models using autoencoders and R packages like hdbayes, and (3) Methodological refinements for Bayesian trial design incorporating historical controls. These works consistently bridge theoretical statistics with cancer research applications, particularly in tumor phylogeny inference and signal detection for adverse events. Scientific awards include: Samuel S. Wilks Memorial Award (2024) from the American Statistical Association for distinguished contributions to biostatistics Professor Ibrahim has mentored 48 pre-doctoral students and 8 postdoctoral fellows, with exceptional thesis publication records in top statistical journals. As principal investigator of the T32 Cancer Genomics Training Grant since 2004, he has secured funding for 35 doctoral students. His curriculum leadership includes modernizing eight graduate courses and establishing new data science computing sequences since 2015. Current advising focuses on Bayesian methodology development for cancer genomics applications. He directs the department's Biostatistics for Research in Genomics initiative and leads the T32 Cancer Genomics Training Grant team, which integrates statistical methodology development with translational cancer research across UNC's Lineberger Comprehensive Cancer Center and clinical partners.

Claudia KlüppelbergView profile

Professor

• Applied Probability
• Mathematical Statistics
• Financial Risk Management
• +3 more

Claudia Klüppelberg is a Professor and Chair of Mathematical Statistics at the Center for Mathematical Sciences, Technische Universität München (TUM). Her academic journey includes positions at ETH Zurich, University of Mainz, and TUM since 1997. She holds a Carl von Linde Senior Fellowship at TUM-IAS, focusing on Risk Analysis and Stochastic Modeling. Her research bridges applied probability, statistics, and their applications in finance and insurance, emphasizing extreme value theory, risk processes, and stochastic networks. She has received prestigious awards such as the New Frontiers in Risk Management Award (2007) and Cross of Merit (2001). Her work addresses real-world challenges in financial risk management, including systemic risk in networks and operational risk modeling. Her recent publications explore causal analysis of extreme risks in networks, max-linear models, and Bayesian networks for extreme events. She contributes to academic leadership as an editorial board member and advisor, promoting interdisciplinary stochastic sciences.

Zhiyong ZhangView profile

Professor

• Bayesian methods
• Network analysis
• Big data analysis
• +4 more

Zhiyong (Johnny) Zhang is a Professor of Quantitative Psychology at the University of Notre Dame and serves as the Quantitative Area Director. He is the director of the Lab for Big Data Methodology and a Fellow at the Institute for Educational Initiatives. His primary research areas include Bayesian methods, network analysis, structural equation modeling, and statistical computing. Dr. Zhang holds a Ph.D. in Quantitative Psychology from the University of Virginia. His work focuses on developing advanced statistical methodologies and software for education, health, management, and psychology. He is the Editor of the Journal of Behavioral Data Science and an Associate Editor of Multivariate Behavioral Research. His research trends span Bayesian inference, neural network applications, mediation analysis, and methodological innovations in factor analysis and latent growth models. He has contributed to software tools like WebPower for statistical power analysis and RAMpath for structural equation modeling. Zhang has received prestigious awards, including Fellow status in the American Psychological Association and election to the Society of Multivariate Experimental Psychology. His advising and grants include mentoring students in quantitative methodologies and leading research on text mining, emotion recognition, and big data applications. The Lab for Big Data Methodology under his direction advances interdisciplinary statistical approaches.

• Macroecology
• Plant trait evolution
• Community dynamics
• +3 more

Meelis Pärtel is a full Professor of Botany at the University of Tartu’s Institute of Ecology and Earth Sciences, affiliated with the Faculty of Science and Technology. He has held this position since 2002, with continuous academic service at the university since 1991. His career includes roles such as Editor-in-Chief of prominent journals like Journal of Vegetation Science and leadership in national and international ecological organizations. Education: PhD in Botany (1997, University of Tartu), postdoctoral studies at the University of Regina (Canada) and Uppsala University (Sweden). Honors: Estonian State Science Awards (2024, 2009), Order of the White Star (2023), and Academia Europaea Young Scientist Award (1996). Research focuses on macroecology , plant trait evolution , and dark diversity theory . He explores how environmental heterogeneity and historical processes shape biodiversity. Key themes include community assembly rules, conservation strategies for plant diversity, and impacts of global change. Recent work emphasizes dark diversity —species expected but absent in local communities—and its implications for conservation. His studies span global grasslands, tropical forests, and temperate ecosystems, addressing biodiversity loss mechanisms. Notable projects include analyzing drought impacts on grasslands and quantifying functional diversity changes. Publications highlight methodological advancements in dark diversity modeling and collaborative global ecology initiatives. Supervised over a dozen postdoctoral researchers, fostering international collaborations in biodiversity research.

Ajay SatputeView profile

Associate Professor

• Affective Neuroscience
• Social Neuroscience
• Neuroimaging
• +4 more

Ajay Satpute is an Associate Professor of Psychology at Northeastern University's College of Science, leading the Affective and Brain Science (ABS) Lab. His research integrates computational modeling, neuroimaging, and behavioral experiments to study affective and social neuroscience, focusing on emotion, fear, and brain architectures. He uses predictive processing theories and machine learning to explore neural correlates of affective experiences and cognitive control. Key areas of investigation include the neural basis of pleasure/pain, emotion regulation, and large-scale brain networks. His lab employs advanced techniques like 7-Tesla fMRI to map subcortical regions such as the periaqueductal gray. He has contributed to understanding context-dependent fear responses and the role of language in emotion construction. In 2023, he was awarded a TIER1 Seed Grant for innovative research. His work has been featured in media discussions on fear psychology, emotion regulation, and societal anxiety (e.g., 'murder hornets').

• Clinical Pharmacology
• Pharmacogenomics
• Pharmacometrics
• +7 more

Associate Professor Sophie Stocker is affiliated with the Sydney Pharmacy School (University of Sydney), St Vincent’s Hospital (Honorary Senior Hospital Scientist in Clinical Pharmacology and Toxicology), and the Garvan Medical Research Institute . She also serves as an Adjunct Senior Lecturer at the St Vincent’s Clinical School, University of New South Wales. Research Focus: Her work spans clinical pharmacology , pharmacogenomics , pharmacometrics , and therapeutic drug monitoring (TDM) . Key areas include optimizing diabetes therapies through metformin pharmacogenomics, managing gout via urate-lowering strategies, and improving antimicrobial dosing in critically ill patients. Recent Trends: 2025 publications highlight her leadership in Bayesian model selection for antibiotic precision dosing Saliva-based TDM innovations Pharmacogenomic tools for allopurinol and clopidogrel Evaluating SGLT2 inhibitors in chronic kidney disease Global standards for antifungal TDM Scientific Awards: Victor Armstrong Young Investigator Award (2022) Emerging Leader Award (2020) CERTARA Young Investigator Award (2020) St Vincent's Rising Star (2018) ASCEPT Presidential Trainee Award (2013) Supervision: She mentors research students working on projects such as voriconazole TDM strategies , saliva-based tuberculosis monitoring , and CYP2C19-guided antiplatelet therapy . International Links: Collaborates with institutions in Germany (University of Hamburg) and New Zealand (University of Otago) on projects like model-informed precision dosing and gout management.

Martin PetersonView profile

Professor

• Ethics of Technology
• Normative Ethics
• Decision Theory
• +3 more

Martin Peterson is the Sue and Harry Bovay Professor of History and Ethics of Professional Engineering at Texas A&M University's Department of Philosophy. He previously taught at Eindhoven University of Technology and served as a Research Fellow at Cambridge University. He earned his Ph.D. in Philosophy from KTH Royal Institute of Technology, Sweden (2003). His work integrates normative ethics, decision theory, and the ethics of technology, focusing on principles like the precautionary and sustainability principles in technological contexts. Education: Ph.D. in Philosophy, KTH Royal Institute of Technology, Sweden (2003) Research Interests: Peterson emphasizes ethical challenges in engineering and technology, exploring how moral principles can be systematically applied. His normative ethics work includes defending gradualism—the idea that moral rightness exists on a spectrum—and consequentialist frameworks. In decision theory, he examines rational choice under uncertainty and the implications of value theory for technological decisions. He also critiques simplistic ethical binaries in fields like nuclear power and abortion debates. Advising & Grants: Though no formal advisees are listed, his teaching contributions include co-authoring Ethics for Engineers and developing pedagogical modules on diversity and equity in engineering education. His grants likely fund interdisciplinary research on ethics and technology, but specific grants are not mentioned in the text. Labs/Teams: Not explicitly detailed in the text. His research is theoretical and book-focused, suggesting collaboration with academic networks rather than formal lab structures.

Fang LiuView profile

Professor

• Differential Privacy
• Statistical Machine Learning
• Bayesian Statistics
• +7 more

Fang Liu serves as Notre Dame Collegiate Professor and Associate Chair in the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame's College of Science. She holds a Ph.D. from the University of Michigan (2003), M.S. from Iowa State University (1999), and B.S. from Peking University (1997). Ph.D., University of Michigan, 2003 M.S., Iowa State University, 1999 B.S., Peking University, 1997 Her research spans five interconnected domains: 1) Development of modern data privacy protection approaches including differential privacy; 2) Statistical and machine learning methodologies with complex model regularization; 3) Bayesian frameworks for medical, biological and social science data analysis; 4) Advanced missing data analysis techniques; and 5) Biostatistical and epidemiological applications. She pioneered the integration of differential privacy concepts from computer science into statistical methodology, identifying numerous research opportunities at this interdisciplinary intersection. Dr. Liu leads the Trustworthy and Ethical Statistical Learning Lab (TESLLa), which focuses on differential privacy, trustworthy AI, synthetic data generation, and probabilistic machine learning. Her lab has produced significant output in privacy-preserving statistical inference and applications to pandemic response, vector-borne disease control, and social media emotional analysis. Key honors include: Elected Fellow of the American Statistical Association Elected Member of the International Statistical Institute (2024) As an advisor, she has mentored over 40 graduate students and researchers since joining Notre Dame in 2011, with alumni securing positions at institutions including Sandia National Laboratories, Los Alamos National Lab, JP Morgan Chase, TikTok, and academic faculty roles. Her research has received substantial funding from NSF, NIH, private foundations, and Notre Dame internal grants, including critical work on campus COVID-19 surveillance testing strategy and privacy-preserving pandemic data collection.

Min ChenView profile

Professor

• Statistical Genomics
• Bioinformatics
• Bayesian Methods
• +5 more

Dr. Min Chen is a Professor in the Department of Mathematical Sciences at the University of Texas at Dallas (UTD), affiliated with the School of Natural Sciences and Mathematics. He holds an adjunct professorship at the University of Texas Southwestern Medical Center. His expertise spans statistical genomics, bioinformatics, Bayesian methods, and sampling techniques. He completed his Ph.D. in Statistics and Decision Science at the University of Texas at Austin and a postdoctoral fellowship in statistical genomics at Yale University. Education: B.S. in Computer Science (University of Science & Technology of China, 1994); M.A. in Statistics (University of Pittsburgh, 1999); Ph.D. in Statistics (UT Austin, 2006); Postdoc in Statistical Genomics (Yale University, 2008–2010). Research focuses on statistical methodologies in genomics, including genome-wide association studies, network-based modeling, and Bayesian integrative analysis. He also explores spatial modeling and ranked set sampling. His work addresses challenges in cancer genetics, epigenetics, and single-cell gene regulation. Notable awards include the NIH Career Development Award (2013), David Bruton Fellowship (2006), and R.L. Anderson Student Paper Award (2006). He is a member of the American Statistical Association and International Chinese Statistical Association. He advises graduate students in Data Science, Statistics, and Bioinformatics & Computational Biology (BCBM) programs. His teaching includes courses on advanced statistical methods and data science. Research contributions span over 40 peer-reviewed articles, with recent work in tumor pathology imaging, antibiotic resistance, and Alzheimer’s disease mechanisms.

• Applied probability theory
• Markov chain Monte Carlo methods
• Spatial statistics
• +4 more

Jesper Møller is a Professor in the Department of Mathematical Sciences at Aalborg University's Faculty of Engineering and Science, specializing in Statistics and Mathematical Economics. His research focuses on advanced statistical methodologies with applications across various scientific domains. His educational background includes extensive training in mathematical sciences, though specific degree details aren't provided in the current materials. His research interests span: Applied probability theory Markov chain Monte Carlo methods (MCMC) Spatial statistics Stochastic geometry Stochastic simulation Point process modeling Professor Møller's recent publication record shows consistent productivity with 239 research outputs including journal articles, reports, and book chapters. His work demonstrates strong focus on spatial point processes, Bayesian inference methods, and applications of stochastic geometry. The research trends indicate increasing sophistication in modeling complex spatial patterns and developing computational methods for statistical inference. His scientific contributions have been supported by numerous research projects, with 28 projects documented including the current "Peculiar Distribution Functions and Interesting Stochastic Processes" (2022-2026). His work has generated significant scholarly impact with citations across multiple disciplines. Professor Møller has supervised 8 PhD students and maintains active collaborations across international research networks. His current projects suggest continued research activity in developing novel statistical methodologies for complex spatial data analysis with applications in materials science, neuroscience, and environmental statistics.

Zeynep Işıl KalaylıoğluView profile

Professor

• Bayesian Statistics
• Environmental Statistics
• Health Statistics
• +3 more

Prof. Zeynep Işıl Kalaylıoğlu is a Professor of Statistics at the Department of Statistics, Middle East Technical University (METU), Ankara, within the Faculty of Arts and Sciences. She holds a Ph.D. in Statistics from North Carolina State University (2002) and has held roles including Associate Professor (2014–2022), Assistant Professor (2009–2014), and researcher at the National Cancer Institute (2002–2007). She has served in administrative roles such as Assistant to the Department Head and member of METU’s Wind Energy Research Center and Faculty Board. Ph.D.: Statistics, North Carolina State University, 2002 M.S.: Statistics with Computational Engineering minor, North Carolina State University, 1999 B.S.: Statistics with Computer Engineering minor, Middle East Technical University, 1995 Her research focuses on Bayesian statistical methodologies for environmental and health phenomena. Key areas include spatial/temporal modeling of atmospheric and ecological variables, prediction of migratory patterns (e.g., wind direction, bird movements), and risk modeling for breast cancer using mammogram data. She develops flexible models for circular and extremal data, emphasizing applications in ecology, epidemiology, and environmental science. Her recent publications address topics like goodness-of-fit tests for statistical distributions, predictive model selection for circular data, and Bayesian approaches for missing covariates in generalized linear models. Current projects include mobile apps for olive harvest optimization and frost预警 systems, reflecting her commitment to applied statistical solutions. Labs/Teams: She leads the BayeZian Research Group, collaborating with METU’s Ecosystem Research Center on interdisciplinary projects. Her teaching expertise spans mathematical statistics, Bayesian theory, and statistical inference at undergraduate and graduate levels.