Find Academic Supervisors

Markus ÅdahlView profile

Associate Professor

• Spatial Statistics
• Ambulance Call Forecasting
• Prehospital Care Optimization
• +4 more

Markus Ådahl serves as Associate Professor in the Department of Mathematics and Mathematical Statistics at Umeå University, Sweden. His research focuses on developing advanced statistical methodologies for emergency medical services optimization, with primary office location at MIT-huset, plan 3, Matematik och matematisk statistik, MIT.B.349, Umeå. His work specializes in spatial statistics and machine learning applications for prehospital care systems. Key research areas include spatio-temporal modeling of ambulance demand, composite likelihood intensity estimation, and large-scale forecasting of emergency calls using log-Gaussian Cox processes. This research directly addresses operational challenges in northern Sweden's emergency response infrastructure through data-driven approaches. Recent publications demonstrate consistent focus on ambulance call pattern analysis, with methodological innovations in regularized semi-parametric modeling (2023) and large-scale spatio-temporal forecasting (2020). These studies establish critical frameworks for optimizing ambulance resource allocation in geographically complex regions. Ådahl currently leads the long-term research initiative Data-driven optimization of prehospital care via statistics and machine learning (2018-2028), which integrates statistical modeling with machine learning to enhance emergency medical service efficiency. While no formal advisees are listed in available materials, this project coordinates cross-disciplinary collaboration between statisticians, healthcare providers, and emergency response authorities.

Jonathan D. MahnkenView profile

Professor

• Categorical data analysis
• Statistical consulting
• Analysis of insurance claims data
• +4 more

Jonathan D. Mahnken is a Professor in the Department of Biostatistics & Data Science at The University of Kansas. His research focuses on statistical methodologies with applications in medical research, including categorical data analysis, study design, and survival analysis. He holds a Ph.D. in Biometry from the University of Texas Health Science Center and has extensive experience in clinical trial design and epidemiological studies. Education: B.A. in Mathematics, Concordia University, Seward, NE M.S. in Preventive Medicine, University of Texas Medical Branch, Galveston, TX Ph.D. in Biometry, University of Texas – Health Science Center, Houston, TX His research interests emphasize practical statistical solutions for healthcare challenges, such as optimizing clinical trial designs, analyzing insurance data, and addressing biases in screening programs. He has contributed to studies on stroke recovery algorithms, antihypertensive medication usage in dialysis patients, and smoking cessation interventions. His work frequently integrates survival analysis and categorical data techniques to address real-world medical problems. Notable publications span journals like Annals of Internal Medicine and Journal of Biopharmaceutical Statistics , with a focus on applying statistical rigor to improve patient outcomes and healthcare practices. His awards include the Delta Omega Honorary Society in Public Health and NIH Loan Repayment Program support. Dr. Mahnken is an Accredited Professional Statistician and contributes to initiatives at The University of Kansas Cancer Center. His expertise bridges statistical theory and applied medical research, addressing critical questions in public health and clinical practice.

Haipeng XingView profile

Professor

• Financial Statistics
• Time Series Analysis
• Change-Point Detection
• +2 more

Haipeng Xing is Professor of Applied Mathematics and Statistics at Stony Brook University, specializing in financial statistics and time series analysis. His research develops statistical methods for change-point detection, financial market modeling, and risk analytics with applications in quantitative finance and econometrics. His publications demonstrate expertise in: High-frequency financial data modeling and volatility measurement Structural break detection in credit markets Stochastic control approaches for trading strategies Multivariate GARCH models with regularization Statistical methods for financial risk management He has authored influential textbooks on statistical models for financial markets and time series analysis.

Kalanka JayalathView profile

Associate Professor

• Bayesian Statistics
• Spatial Statistics
• Survival Analysis
• +3 more

Kalanka Jayalath is an Associate Professor and Program Director of Statistics at the University of Houston-Clear Lake (UHCL), within the College of Science and Engineering. He holds a Ph.D. in Statistical Science from Southern Methodist University (SMU), an M.S. in Statistics from Sam Houston State University, and a BSc in Mathematics from the University of Peradeniya, Sri Lanka. His research focuses on Bayesian statistics, spatial statistics, survival analysis, and applications of statistical methods in engineering, archaeology, and sports analytics. Dr. Jayalath has authored books and peer-reviewed articles, including Statistics and Probability with Applications for Engineers and Scientists and contributions to spatial analysis of archaeological sites. His work bridges theoretical statistics with practical applications, such as analyzing cricket performance using machine learning and improving variance homogeneity tests. He has secured grants from the University of Houston-Clear Lake’s Faculty Development Fund (FDF) for research on spatial data modeling and graphical statistical tests. His presentations span international conferences, including the Conference of Texas Statisticians and the International Conference on Statistical Distributions and Applications (ICOSDA). Dr. Jayalath serves on editorial boards for journals like Austin Statistics Journal and reviews for Journal of Statistical Computation and Simulation . His professional affiliations include the American Statistical Association and the International Statistical Engineering Association.

Dongfeng WuView profile

Professor

• Probability Modeling in Cancer Screening
• Bayesian Inference
• Statistical Decision Theory
• +5 more

Dongfeng Wu, PhD is a tenured Professor in the Department of Bioinformatics and Biostatistics at the University of Louisville's School of Public Health and Information Sciences (SPHIS). He holds positions in both academic and research capacities, focusing on probability modeling and statistical inference in cancer screening. His work integrates Bayesian methods, statistical decision theory, and epidemiological analysis to address challenges in medical research. Education: B.S. in Probability and Statistics, Peking University (1986–1990) M.S. in Probability and Statistics, Peking University (1990–1993) M.S. in Computer Science, University of California, Santa Barbara (1997–1999) Ph.D. in Statistics, University of California, Santa Barbara (1995–1999) Research Interests: Dr. Wu specializes in developing statistical methodologies for optimizing cancer screening protocols, including dynamic scheduling algorithms, lead time estimation, and over-diagnosis analysis. His research extends to Bayesian inference, time series analysis, and applications in lung, breast, and colorectal cancer screening. Key contributions include a published book on probability modeling in screening and collaborative work with institutions like the National Lung Screening Trial (NLST). Funding: He leads the NIH/NCI-funded project R15CA242482 (2019–2023), totaling $435,309, focused on dynamic screening scheduling using statistical methods and software development. Teaching: At SPHIS, Dr. Wu teaches advanced courses such as Bayesian Inference, Clinical Trials, Survival Analysis, and Biostatistical Methods. Recent courses include PHST691-Bayesian Inference and PHST683-Survival Analysis. Advising: He has mentored multiple graduate students, including Farhin Rahman (Ph.D. expected 2022), Ruiqi Liu (Ph.D. 2017), and Xiaohong Li (Ph.D. 2017), focusing on topics like parameter estimation in screening and RNA-seq data analysis.

Qi ZhengView profile

Associate Professor

• High-dimensional data analysis
• Semiparametric and nonparametric models
• Varying effects survival analysis
• +4 more

Qi Zheng, PhD, is an Associate Professor in the Department of Bioinformatics and Biostatistics at the University of Louisville's School of Public Health and Information Sciences (SPHIS) . Joined in Fall 2015, Dr. Zheng's methodological research focuses on high-dimensional data analysis, semiparametric models, survival analysis, personalized treatments, tree-based learning, and statistical applications in epidemiology and biomedical sciences. Postdoctoral Fellow (2015), Bioinformatics and Biostatistics, Emory University PhD (2013), Mathematical Science, Clemson University BS (2006), Mathematics, Nanjing University, China Research interests span statistical methodology development for complex biomedical data. Key areas include high-dimensional regression, quantile regression, survival analysis, and machine learning applications in public health. Recent publications (2022–2013) demonstrate expertise in statistical hypothesis testing, survival modeling, and high-dimensional data methods. Articles address observational studies, censored quantile regression, time series change-point detection, and generalized linear models with complex covariates. Contact: qi.zheng@louisville.edu | Personal Website

Chunling NiuView profile

Assistant Professor

• Data Science in Education
• Psychometrics
• Artificial Intelligence Applications
• +2 more

Dr. Chunling Niu is an Assistant Professor at the University of the Incarnate Word's Dreeben School of Education, specializing in quantitative methods, AI applications, and educational assessment. Her interdisciplinary research bridges data science, psychometrics, and program evaluation to address educational equity challenges. Key research areas include educational data mining, machine learning applications in assessment, and AI-driven decision-making frameworks. With approximately 40 peer-reviewed publications and leadership in developing AI curriculum, she pioneers technological innovations in educational research. Honors include the UIW Provost's Legacy Teaching Award and AEA Minority Serving Institute Fellowship. She directs major evaluation projects including UIW's QEP Assessment initiative.

Johan SegersView profile

Professor

• Extreme Value Theory
• Dependence Modelling via Copulas
• Statistical Learning
• +5 more

Johan Segers is a Full Professor at the Department of Mathematics, KU Leuven , and a Visiting Professor at the Institut de statistique, biostatistique et sciences actuarielles (UCLouvain). His research bridges extreme value theory , copulas , statistical learning , and optimal transport , with applications in finance, environmental risk analysis, and multivariate statistics. Research Trends : Focus on extremal dependence, vine copulas, and Monte Carlo methods. Recent Article Trends : Combines extreme value theory with graphical models and optimal transport for high-dimensional data analysis. Awards & Honors : Prix Adolphe Wetrems (2012–2013) from the Académie Royale des Sciences, des Lettres et des Beaux-arts de Belgique Fellow of the Institute for Mathematical Statistics (IMS) Elected Member of the International Statistical Institute (ISI) Editorial Roles : Associate Editor for Advances in Applied Probability , Bernoulli , Electronic Journal of Statistics , and others. He also contributes to software development, including the R-package spatialTailDep .

Wouter KagerView profile

Assistant Professor

• Probability Theory
• Discrete Systems
• Random Spatial Behavior
• +6 more

Wouter Kager is an Assistant Professor of Probability in the Department of Mathematics at Vrije Universiteit Amsterdam. His research focuses on discrete systems with random spatial behavior, including lattice aggregation models, random fields, and self-interacting random walks. He is particularly interested in the limiting behavior and scaling limits of these models, contributing to Probability Theory and related disciplines. Research Interests: Kager's work explores the intersection of probability theory and statistical physics. His research includes studies on stochastic processes, lattice models (e.g., Ising model), aggregation phenomena, and network stability. He has developed tools for analyzing stochastic systems, such as rotor-router aggregation and diamond aggregation models. His recent work also addresses applications in coordination games and queueing theory. Tools & Contributions: Kager has created open-source tools for converting EPS/FIG files to PDF/PNG, optimizing n-up printing, and LaTeX presentation themes. These tools are widely used in academic and technical contexts. His contributions to computational methods reflect his interdisciplinary approach to research. Publications: His articles span topics like stochastic domination, critical phenomena in random graphs, and stability analysis of stochastic networks. Recent works include advancements in likelihood ratio analysis, convex cone theory, and coordination game models.

Werner PlobergerView profile

Professor

• Statistics
• Econometric Methodology
• Time-Series Econometrics
• +3 more

Werner Ploberger is the Thomas H. Eliot Distinguished Professor of Economics at Washington University in St. Louis. He holds a PhD in Applied Mathematics from Vienna University of Technology (1981) and a Habilitation in Econometrics (1993). His research focuses on Statistics, Econometric Methodology, and Time-Series Econometrics. He has held faculty positions at Vienna University of Technology, University of St. Andrews, and University of Rochester, achieving tenure in 1993 and promotion to full professor in 1995. His work spans structural break detection, Bayesian methods, and asymptotic theory. Recent publications explore infinite-dimensional parameter spaces, nonstandard estimation, and functional limit theorems. He serves on PhD admissions and committees. Research interests emphasize rigorous methodological advancements in econometrics, with a focus on time-series analysis and statistical foundations. His contributions include optimal tests for structural breaks, AIC-based Bayesian estimation, and the analysis of multivariate time-series breaks. His articles reflect a blend of theoretical depth and applied econometric challenges.

Young Ki ShinView profile

Professor

• Econometrics
• Causal Inference
• Quantile Regression
• +3 more

Young Ki Shin is a Professor of Economics at McMaster University, where he has established himself as a leading researcher in econometrics and economic theory. His work bridges theoretical developments with practical applications in statistical methods for economic analysis, with significant contributions to quantile regression, causal inference, and economic modeling. His educational background includes a Ph.D. in Economics from the University of Rochester (2002-2007) and a B.A. from Seoul National University (1996-2001). Professor Shin's research focuses on several key areas: causal inference and identification , where he develops methods to determine cause-effect relationships; modern computational algorithms and inference , creating efficient statistical methods for large datasets; and applications of statistical decision theory to economic problems. His work often addresses computational challenges in applying econometric methods to massive datasets. His recent publications demonstrate a strong focus on quantile regression methods, with several papers addressing computational challenges in applying these techniques to large datasets. He has made significant contributions to generalized method of moments (GMM) estimation and developed innovative approaches to monotone signaling equilibria in matching markets. His research spans from theoretical econometrics to applied economic questions, including analyses of government spending multipliers during economic crises. As an educator, Professor Shin has taught a comprehensive range of econometrics courses at both undergraduate and graduate levels, including Econometrics I, Econometrics II, and Applied Econometrics, reflecting his research expertise in both theoretical foundations and practical applications of econometric methods.

Kay GieseckeView profile

Professor

• Artificial Intelligence in Finance
• Financial Technology (FinTech)
• Risk Management
• +7 more

Kay Giesecke is Professor of Management Science & Engineering at Stanford University, where he has been on the faculty since 2005. He serves as the Founder and Director of Stanford's Advanced Financial Technologies Laboratory, Director of the Mathematical and Computational Finance Program, and is a member of the Institute for Computational and Mathematical Engineering. He has held visiting positions at Cornell, UCLA, and the International Monetary Fund, and serves on the Governing Board and Scientific Advisory Board of the Consortium for Data Analytics in Risk and the Council of the Bachelier Finance Society. Dr. Giesecke received his doctorate in 2001 from Humboldt Universität zu Berlin where he was a fellow of the Deutsche Forschungsgemeinschaft. His educational background forms the foundation for his interdisciplinary work at the intersection of finance, technology, and quantitative methods. Giesecke's award-winning research sits at the intersection of technology and finance, transforming risk intelligence, market oversight, and investment management. He pioneers stochastic models, statistical machine learning methods, computational algorithms, and software to better understand risk, identify opportunities, and support decision-making. His key application areas include risk management, market surveillance, fair lending, and sustainable investing. His work informs financial regulation, guides institutional practices, and contributes to more transparent, resilient, and equitable financial systems. His research spans blockchain technology, mortgage risk analysis, and computational methods for financial systems, demonstrating both theoretical depth and practical relevance. Professor Giesecke has been recognized with multiple prestigious awards for his research contributions: JP Morgan AI Faculty Research Award (2019) SIAM Financial Mathematics and Engineering Conference Paper Prize (2014) Fama/DFA Prize for the Best Asset Pricing Paper in the Journal of Financial Economics Gauss Prize of the Society for Actuarial and Financial Mathematics of Germany (2003) Giesecke has supervised 29 doctoral dissertations, with graduates going on to faculty positions at institutions such as UC Berkeley, Oxford, Wharton, and NYU; leadership roles at firms including Goldman Sachs, Google, JPMorgan, Amazon, and Morgan Stanley; and founding successful technology startups. His research has been supported by the National Science Foundation and several leading financial institutions including JP Morgan, Swiss Re, BBVA, Royal Bank of Scotland, State Street, and Amazon Web Services. As an academic leader, Giesecke is Editor of Management Science (Finance Area) and Associate Editor for Operations Research, Mathematical Finance, Journal of Financial Econometrics, SIAM Journal on Financial Mathematics and several other leading journals. He founded and organizes Stanford's annual AI in Fintech Forum, which brings together academic researchers and industry practitioners to discuss cutting-edge developments in financial technology. His Advanced Financial Technologies Laboratory serves as a hub for interdisciplinary research at the intersection of finance, computer science, and engineering.

Christiana CharalambousView profile

Lecturer

• Biostatistics
• Medical Statistics
• Pain Perception
• +4 more

Christiana Charalambous is a Lecturer in Statistics at The University of Manchester. Her research focuses on developing advanced statistical methodologies for medical and health-related applications, particularly in pain perception mechanisms and longitudinal data analysis. She is a core member of the Digital Futures research platform, contributing to statistical innovations in healthcare analytics. Her educational background includes a PhD in Statistics (2011) with a thesis titled Variable Selection in Joint Modelling of Mean and Variance for Multilevel Data , supervised by Professor Jianxin Pan. This work laid the foundation for her subsequent research in joint modelling frameworks. Key research interests span: (1) Biostatistical Modelling including copula-based joint models, longitudinal-survival analysis, and latent class models; (2) Pain Perception Dynamics exploring Bayesian estimation of pain-related traits and their neurophysiological correlates; (3) Infection Dynamics involving stochastic models for within-host disease progression and public health applications. Her methodological work integrates advanced statistical theory with clinical and epidemiological data. Recent articles highlight innovations in Gaussian copula frameworks, Burr distribution applications for infection timing analysis, and boundary effects of expectation in pain perception. Her work frequently bridges statistical theory with practical clinical challenges, such as optimizing biomarker variability analysis in longitudinal studies. As Principal Investigator of the Statistical Advisory Unit (SAU) project, she leads multidisciplinary collaborations advancing statistical methodologies across university departments. No scientific awards are explicitly mentioned in her profile. Her advisory contributions include supervising her own PhD research but no listed advisees. Research outputs are concentrated in statistical methodology with strong interdisciplinary medical applications, reflecting her role in the intersection of statistics and healthcare research.

• Statistics
• Econometrics
• Applied Mathematics
• +3 more

Yanbo Tang is a Lecturer in Statistics at the Department of Mathematics, Faculty of Natural Sciences, Imperial College London. His research focuses on statistical theory and methodology, including Bayesian inference, high-dimensional asymptotics, numerical integration, and applications in astrophysics and social sciences. He is affiliated with the Artificial Intelligence Network and Mathematics research and teaching staff at Imperial College. Education: Not explicitly stated in the provided text. His research interests span statistical theory such as Laplace and saddlepoint approximations, adaptive quadrature methods, and copula models. He also explores applications in areas like extragalactic X-ray jet variability and parental psychological control effects on adolescents. Recent work emphasizes stochastic convergence rates and assumption-lean inference techniques. Selected publications (2020–2024) highlight contributions to high-dimensional statistical problems, asymptotic behavior of likelihood methods, and Monte Carlo integration challenges. His work bridges theoretical advancements with practical computational solutions in complex data analysis. No grants or awards are explicitly listed in the provided information. He contributes to academic discussions, such as commenting on Vansteelandt and Dukes' work on assumption-lean inference.

Liza LevinaView profile

Professor

• High-Dimensional Statistical Inference
• Statistical Network Analysis
• Neuroimaging
• +3 more

Liza Levina is the Vijay Nair Collegiate Professor and Chair of the Department of Statistics at the University of Michigan. She is affiliated with the Michigan Institute for Data and AI in Society and the Center for the Study of Complex Systems. Her expertise lies in high-dimensional statistical inference and statistical network analysis, with applications in neuroscience and imaging. She earned her PhD in Statistics from UC Berkeley in 2002 and has been at the University of Michigan since then. Her research focuses on network analysis, statistical learning, and big data, particularly in neuroimaging. She has received notable awards, including the ASA Junior Noether Award, and is a Fellow of both the ASA and IMS. She was an invited speaker at the 2018 International Congress of Mathematicians and an IMS Medallion lecturer. Levina’s work bridges theoretical advancements and practical applications, such as analyzing brain connectivity networks and developing methods for community detection in complex systems. She has contributed to methodologies for network cross-validation, pseudo-likelihood approaches, and conformal prediction in network-assisted regression. Her affiliations include leadership roles in the Department of Statistics and collaborations across interdisciplinary institutes. Her research has been recognized with prestigious acknowledgments, reflecting her impactful contributions to statistical science and its applications.