Find Academic Supervisors

Liqun DiaoView profile

Associate Professor

• Statistical Methodology
• Machine Learning
• Causal Inference
• +7 more

Liqun Diao is an Associate Professor (Tenured) in the Department of Statistics and Actuarial Science at the University of Waterloo. He holds affiliations with the Health Data Science Lab and the Waterloo Artificial Intelligence Institute. His research focuses on developing statistical methods and machine learning algorithms for applications in medicine, public health, and insurance. Education: B.Econ. in Statistics, Renmin University of China (2007) M.Math. in Statistics (Biostatistics), University of Waterloo (2009) Ph.D. in Statistics (Biostatistics), University of Waterloo (2013) Research Interests: Recursive partitioning and tree-based methods for survival and health data Causal inference and missing data methodologies Bayesian nonparametric models and copula dependence structures Mortality forecasting and actuarial science applications Awards: 2013 Pierre Robillard Award (best doctoral thesis in Canadian statistics) 2024 Outstanding Performance and Teaching Awards at UW Professional Activities: Led research groups in health data science and AI Recipient of multiple grants from NSERC and industry partners Advises graduate students in statistics and actuarial science Labs/Teams: Active contributor to the Health Data Science Lab and Waterloo AI Institute, focusing on applying statistical innovations to real-world health and insurance challenges.

• 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.

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.

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.

Yun-Hee ChoiView profile

Professor

• Biostatistics
• Statistical Genetics
• Survival Analysis
• +3 more

Yun-Hee Choi is a Professor at the Department of Epidemiology and Biostatistics, Schulich School of Medicine & Dentistry, Western University. Her research focuses on advanced statistical methodologies for complex biomedical data, particularly in genetic and cancer epidemiology contexts. BSc in Statistics MA in Statistics PhD in Biostatistics She specializes in joint modeling , dynamic prediction , correlated survival data analysis , and statistical genetics , with applications to Lynch syndrome and familial colorectal cancer . Her methodological work includes copula models , frailty models , and multistate models for analyzing competing risks and recurrent events. Recent publications demonstrate expertise in genetic risk estimation , cancer progression modeling , and family-based survival analysis . She develops computational tools like the FamEvent R package for time-to-event data in genetic studies. NSERC: Statistical methods for joint modeling (2019-2024) CIHR: Risk prediction models for HBOC (2019-2023, Co-PI) CANSSI: Genetic data analysis (2019-2022, Co-I) NSERC: Competing risks modeling (2014-2019) CIHR: Multistate models for Lynch syndrome (2013-2016) CBCF-Ontario: BRCA1/2 family screening (2014-2017) NSERC: Correlated survival data (2009-2014) Her work bridges biostatistical theory with cancer prevention applications , particularly in hereditary cancer syndromes.

Yongcheng QiView profile

Professor

• Probability Theory
• Applied Probability
• Bootstrap
• +5 more

Yongcheng Qi is a Professor at the Department of Mathematics and Statistics, Swenson College of Science and Engineering, University of Minnesota - Duluth. His research focuses on probability theory, extreme-value statistics, and random matrix theory. PhD, University of Georgia (2001) PhD, Peking University (1992) B.S., Peking University (1987) Qi’s work explores applied probability, bootstrap methods, copulas, and high-dimensional statistics. Recent publications analyze spectral radii of random matrices, tail dependence functions, and empirical likelihood techniques. His research has been supported by NSF and NSA grants, including projects on extreme value theory, high-dimensional normal distributions, and asymptotic distributions for branching processes. Qi is affiliated with the American Statistical Association and the Institute of Mathematical Statistics.

• High-Dimensional Statistics
• Portfolio Optimization
• Bayesian Analysis
• +3 more

Taras Bodnar is a Professor at the Department of Management and Engineering, Linköping University. His research focuses on high-dimensional statistical methodologies with applications in finance, portfolio optimization, and econometrics. He specializes in developing and analyzing advanced statistical models for asset allocation, risk management, and multivariate meta-analysis. Bodnar's work often involves Bayesian methods, shrinkage estimation techniques, and copula modeling to address challenges in financial data analysis. His recent contributions include the HDShOP package for portfolio selection and advancements in nonlinear shrinkage tests for large-dimensional covariance matrices. His research bridges theoretical statistics with practical financial applications, addressing issues such as dark uncertainty and efficient frontier estimation in high-dimensional settings. Key research interests include: High-Dimensional Portfolio Optimization Bayesian Analysis in Financial Contexts Covariance Matrix Estimation and Testing Uncertainty Quantification in Multivariate Analyses Statistical Software Development for Finance Recent publications (2024-2025) emphasize methodological innovations in portfolio selection, copula modeling, and robust statistical inference. His work has implications for both academic theory and practical investment strategies, particularly in managing large and complex financial datasets.

• Wireless Communications
• Satellite Communications
• Cybersecurity
• +5 more

Athanasios G. Kanatas is a Professor at the Department of Digital Systems, University of Piraeus, Greece, and Director of the Telecommunication Systems Laboratory. He holds a Ph.D. in Mobile Satellite Communications from NTUA (1997), and has held roles such as Dean of the School of Information & Communication Technologies (2013–2017) and IEEE Communications Society Chairperson (1999). Education: Diploma in Electrical Engineering (NTUA, 1991), M.Sc. in Satellite Communication Engineering (University of Surrey, 1992), Ph.D. in Mobile Satellite Communications (NTUA, 1997). Research focuses on V2X communications, UAV-assisted networks, antenna design, stochastic geometry, and cybersecurity. He has published over 200 papers and authored 6 books, and leads projects in 5G/6G systems and integrated sensing-communication networks. His work includes pioneering contributions to aerial relay placement, fluid antenna systems, and hybrid beamforming techniques. He serves as Editor of IEEE Transactions on Wireless Communications, Associate Editor of IEEE Transactions on Antennas and Propagation, and is a Senior IEEE Member since 2002. Recent activities include extending the deadline for M.Sc. program applications and promoting internships for students. Labs/Teams: Directs the Telecommunication Systems Laboratory, collaborating on projects like ARGOS RFI monitoring and UAV corridor-assisted IoT networks. His research emphasizes practical implementation through prototyping and experimentation, with applications in aerospace, IoT, and intelligent transport systems.

K.C. ChangView profile

Professor

• Data Fusion
• Bayesian Networks
• Probabilistic Reasoning
• +9 more

K.C. Chang is a Professor in the Department of Systems Engineering and Operations Research at George Mason University's Volgenau School of Engineering. He is the Director of the Sensor Fusion Lab and the coordinator of the Financial Engineering Program at GMU, reflecting his interdisciplinary research focus on data fusion and probabilistic reasoning. Education: PhD, Electrical Engineering, University of Connecticut MS, Electrical Engineering, University of Connecticut BS, Communication Engineering, National Chiao-Tung University Dr. Chang's research centers on distributed data fusion, Bayesian networks, and decision-making under uncertainty, with applications in sensor networks, wireless communications, satellite systems, and financial engineering. His work integrates statistical modeling, machine learning, and systems engineering principles to solve complex, real-world problems involving uncertainty and large-scale information processing. The recent publications reflect a strong trend in applying probabilistic reasoning to dynamic spectrum access, satellite communication, and electronic warfare. His research leverages copula functions and information genealogy to enhance fusion accuracy in non-Gaussian and dependent environments. Applications span defense, aerospace, and finance, demonstrating the versatility of his methodological contributions. Scientific Awards: IEEE Fellow (2010) for contributions to sensor data fusion and Bayesian probabilistic inference Dr. Chang has advised numerous graduate students and led major research initiatives funded by the Office of Naval Research (ONR), National Science Foundation (NSF), Army Research Office (ARO), Air Force Office of Scientific Research (AFOSR), and industry partners like Intelligent Fusion Technology Inc. and Impact Technologies, LLC. His editorial roles include serving as Editor for Tracking/Navigation Systems and Large Scale Systems in IEEE Transactions on Aerospace and Electronic Systems, and as Associate Editor for IEEE Transactions on Systems, Man, and Cybernetics. He was also Technical Program Co-Chair of the 2009 International Conference on Information Fusion. He leads the Sensor Fusion Lab at George Mason University, a research group focused on advancing the theory and application of multi-sensor, multi-source information fusion. The lab works on foundational algorithms as well as real-world implementations in defense, communication, and financial systems. The lab collaborates with government agencies and private sector organizations to translate theoretical advances into operational technologies.

Maria Giuseppina BrunoView profile

Associate Professor

• Financial and Actuarial Mathematics
• Quantitative Methods for Economics, Finance and Insurance
• Valuation of Derivative Financial Instruments
• +7 more

Maria Giuseppina Bruno is an Associate Professor in the Department of Methods and Models for Economy, Territory, and Finance (MEMOTEF) at Sapienza University of Rome's Faculty of Economics. She has been serving in this capacity since December 29, 2003, specializing in the scientific disciplinary sector STAT-04/A (Mathematical Methods of Economics and Actuarial and Financial Sciences). Her institutional email is Giuseppina.Bruno@uniroma1.it and she maintains office hours at room 149 on the first floor, wing B of the MEMOTEF Department. Her academic background includes a PhD in 'Mathematics for Financial Market Analysis' from the University of Brescia (1995), where she defended her thesis 'Memory functions as a tool for evaluating American options,' and a first-class honors degree in Economics and Commerce from LUISS University of Rome (1990) with thesis 'Valuation of convertible bonds. An interpretation using physics models.' Professor Bruno's research spans financial and actuarial mathematics, quantitative methods for economics and finance, derivative instrument valuation, risk management models, stochastic processes, and computational methods. Her work demonstrates strong interdisciplinary connections between mathematics, finance, insurance, and even physics through her interest in econophysics and applications of physical models to financial problems. Her recent publications show a consistent focus on innovative insurance products, risk modeling, and financial engineering. From Tailor-made CDOs (2024) to Pay-as-you-drive insurance models (2023) and specialized insurance products like ALEA and MICROTAKAFUL (2021), her research addresses contemporary challenges in risk transfer mechanisms and insurance market dynamics. Her work on long-term care annuities (2020) and option pricing with stochastic volatility (2019) demonstrates expertise in both theoretical and practical aspects of financial mathematics. As a respected academic, she serves as Managing Editor of Annali MEMOTEF and referees for prestigious journals including Insurance: Mathematics and Economics. She has been actively involved in professional organizations including AMASES, AAI, and Istituto Italiano degli Attuari. Professor Bruno currently teaches Actuarial Mathematics for Private Insurance (Master's Degree in Finance and Insurance) and Basic Mathematics course (Bachelor's Degree in Economics and Finance). She has extensive experience in curriculum development, serving as reference teacher for the Master's Degree Course FINASS and as tutor for the Bachelor's Degree in Economics and Finance. Her institutional service includes membership on various committees related to quality assurance, publications, and doctoral programs. Her professional activities extend beyond academia, with participation in Bank of Italy examination boards (2016, 2019, 2023) and previous teaching roles for financial professionals at INPDAP and Banca di Roma. Her technical expertise includes programming in C++, VBA, and Python, which supports her computational research in financial mathematics.

• Explainable Artificial Intelligence
• Machine Learning
• Data Clustering
• +4 more

Nicolas Labroche serves as a Researcher in the Department of Computer Science at the Polytechnic School of the University of Tours. He is affiliated with the Fundamental and Applied Computer Science Laboratory of Tours (LIFAT) within the UFR of Sciences and Technology. His academic work spans multiple research domains with significant contributions to machine learning methodology and applications. His primary research focuses on Explainable Artificial Intelligence , particularly for medical applications where model interpretability is critical. He has pioneered methods in clustering of local attributive explanations and discernibility in medical ML models . Additional expertise includes exploratory data analysis through mathematical programming for comparison queries, time series forecasting for environmental monitoring, and preference-based explanations in recommender systems. His work bridges theoretical computer science with practical applications in healthcare, environmental science, and business intelligence. Analysis of his publication trends reveals consistent innovation in making data exploration more efficient and interpretable. Recent work emphasizes user-centric explainability (2024-2025), mathematical optimization for analytics (2022-2023), and synthetic data generation (2022). His research demonstrates strong interdisciplinary connections between computer science, medicine, and environmental science. His laboratory affiliation with LIFAT provides the infrastructure for his computational research. While specific grant details aren't public, his publication record indicates sustained research productivity across multiple European-funded projects focused on data science applications.

Andrew TitmanView profile

Professor

• Survival Analysis
• Multi-State Modeling
• Medical Statistics
• +3 more

Andrew Titman is a Professor in the School of Mathematical Sciences at Lancaster University . His research focuses on survival analysis and multi-state modeling with applications in medical statistics and quantitative methodology. Primary Affiliation: School of Mathematical Sciences, Lancaster University Contact: a.titman@lancaster.ac.uk Research Interests include: Development of non-Markov multi-state models for complex survival data Latent variable modeling for health-related quality of life questionnaires Methodology for informative observation times in clinical trials Health economic decision modeling applications Survival analysis with misclassification and time non-homogeneity Recent Publications demonstrate expertise in: Bayesian state-space models for sports analytics Multi-state model diagnostics and goodness-of-fit testing Medical applications in endocrinology and hematology Simulation methodology with input uncertainty quantification Scientific Contributions include: Development of R packages: nhm, CIFresolve, miscIC Creation of the MarkovTest GitHub repository for multi-state model validation Methodological frameworks for non-homogeneous Markov models

Yen-Yi HoView profile

Associate Professor

• Computational Biology
• Statistical Genetics
• Genetic Data Analysis
• +6 more

Yen-Yi Ho is an Associate Professor in the Department of Statistics at the University of South Carolina, with a joint appointment in Biological Sciences. She holds a Ph.D. in Biostatistics from Johns Hopkins University. Her research focuses on computational biology, statistical genetics, and the development of methods for analyzing high-throughput genetic data. Key interests include gene pathway enrichment analysis and applications in cancer, frailty mechanisms, and Hirschsprung disease genetics. Her work bridges statistical methodology and biological discovery, with notable contributions to liquid association modeling and modular network construction using eQTL data. She has collaborated on studies involving chemopreventive cancer strategies, frailty biomarkers, and genomic analyses of pancreatic adenocarcinoma. Dr. Ho has pioneered software tools like the LiquidAssociation R package and contributed to the fastLiquidAssociation package. Her publications span topics from Bayesian hierarchical modeling in medical research to single-cell RNA-seq analysis of cellular heterogeneity. Beyond academia, she has applied statistical methods to public health challenges, such as optimizing disability applicant prioritization systems.

• Dependence Modeling
• Copula Models
• Multivariate Analysis
• +7 more

Harry Joe is a Professor in the Department of Statistics at the University of British Columbia (Vancouver Campus). His primary research focuses on dependence modeling, copula theory, multivariate analysis, and applications in biostatistics, finance, and psychometrics. He has advised students including Xiaoting Li, Xinyao Fan, and Pavel Krupskiy. Research Interests: - Advanced copula constructions (e.g., vine copulas) - Extreme value theory and tail dependence - Applications in financial risk, biomedical research, and educational measurement - Multivariate time series analysis and non-Gaussian models Publications highlight contributions to copula-based classification methods (2024), factor copula models (2015), and dynamic dependence modeling (2020). His work bridges theoretical developments with practical applications across disciplines. Teaching and advising emphasize methodological innovation. Current research explores high-dimensional dependence structures and computational methods for complex data. No lab/team affiliations explicitly noted in provided materials.