Find Academic Supervisors

Dong-Hyuk KimView profile

Senior Lecturer

• Industrial Organization
• Econometrics
• Microeconomics
• +3 more

Dr. Dong-Hyuk Kim is a Senior Lecturer in the School of Economics at the University of Queensland, within the Faculty of Business, Economics and Law. He holds a PhD in Bayesian Econometrics for Auction Models from the University of Arizona (2010). His research focuses on Industrial Organization, Econometrics, and Microeconomics, with emphasis on auction theory, risk aversion, and development economics. Dr. Kim is affiliated with the Centre for Efficiency and Productivity Analysis and has published in top journals such as Journal of Econometrics and European Economic Review . His work bridges theoretical econometrics with applied economic problems, including optimal reserve pricing strategies, bidder behavior analysis, and the empirical relevance of ambiguity in auction models. Recent projects include studies on drought-resistant maize varieties in Malawi and government expenditure impacts in Sri Lanka. Dr. Kim is available for PhD supervision and actively contributes to methodological advancements in structural estimation techniques. Publications span topics like procurement mechanisms, risk-averse bidding strategies, and demand modeling using random coefficients frameworks. Ongoing research explores carbon emission trading in China's coal sector and managerial impacts on firm performance post-privatization in Vietnam.

• Nonstationary time series econometrics
• Nonparametric statistics
• Econometric Theory
• +2 more

Professor Qiying Wang is a faculty member in the School of Mathematics and Statistics at the University of Sydney, holding the rank of Professor. His research focuses on advanced statistical theory and econometrics, particularly in nonstationary time series analysis, cointegrating regression, and asymptotic methods. He has published extensively in top journals such as Econometric Theory and Journal of Econometrics . Research Interests: Nonstationary time series econometrics, nonparametric statistics, econometric theory, local time theory, and self-normalized limit theory. He is a leading expert in nonlinear cointegrating regression, developing methodologies for handling endogeneity and nonstationarity in economic and financial data. Selected Grants: 2016: Nonlinear cointegrating regression with endogeneity (Australian Research Council DP) 2012: Asymptotics in nonlinear cointegrating regression (Australian Research Council DP) Key Contributions: Authored the monograph Limit Theorems for Nonlinear Cointegrating Regression (2015) and pioneered techniques for optimal bandwidth selection in cointegrating models. His work bridges theoretical advancements with practical econometric applications.

• Machine Learning
• Artificial Intelligence
• Bayesian Methods
• +5 more

Mark van der Wilk is an Associate Professor in the Department of Computer Science at the University of Oxford and a Tutorial Fellow at Hertford College. His research focuses on machine learning, particularly Bayesian methods, equivariance in neural networks, and applications in science and industry. He leads a research group addressing challenges in generalization, architecture design, and efficient learning. Education: PhD in Machine Learning from the University of Cambridge (2017), undergraduate degree in Engineering. Previously held academic positions at Imperial College London and worked in industry with Dr. James Hensman. Research Interests: Automatic discovery of inductive biases, architecture optimization, Bayesian deep learning, causal inference, and applications in bioengineering and chemistry. Collaborates with industry and academia on practical machine learning solutions. Awards: Best Paper Award at NeurIPS/ICML conferences, Qualcomm Innovation Fellowship (PhD). Active in open-source contributions, including GPflow and GPflux libraries. Key Projects: Development of Competitive Amplification Networks for molecular diagnostics, turbulence testing for LLMs, and sparse Gaussian process approximations. Supervises PhD students focusing on alignment of mathematical and practical machine learning challenges.

Thomas BeckersView profile

Assistant Professor

• Control Systems
• Machine Learning
• Robotics
• +6 more

Thomas Beckers is an Assistant Professor of Computer Science at Vanderbilt University's School of Engineering, joining in January 2023. He holds a Ph.D. in Electrical Engineering from the Technical University of Munich (2020), and B.Sc./M.Sc. degrees from Technical University of Braunschweig (2010/2013). His research bridges machine learning and control theory to develop safe, robust algorithms for complex systems like autonomous vehicles and soft robotics. He is a DAAD AInet Fellow and recipient of the Rhode & Schwarz Outstanding Dissertation Award. His work focuses on physics-enhanced learning and safe control strategies, leveraging Bayesian methods and port-Hamiltonian systems. Recent projects include developing uncertainty-aware control frameworks and Python tools like PyGpPHs for modeling physical systems. He advises students Peilun Li and Kaiyuan Tan, and has organized workshops at major conferences such as ACC and CDC. His lab explores topics ranging from PDE system identification to geometric control of aerial robots. Notable achievements include organizing large-scale workshops (e.g., 2023 CDC workshop with 80+ participants) and securing RAMP awards for data-driven control research. His publications emphasize safety, interpretability, and integration of physical priors into machine learning models.

Anuj AbhishekView profile

Assistant Professor

• Inverse Problems
• Integral Geometry
• Microlocal Analysis
• +3 more

Anuj Abhishek is an Assistant Professor in the Department of Mathematics, Applied Mathematics, and Statistics at Case Western Reserve University, affiliated with the College of Arts and Sciences. His research focuses on statistical and numerical methods for inverse problems, integral geometry, and microlocal analysis. He holds a faculty position in a top-tier mathematics department and contributes to advancing mathematical techniques for solving complex imaging and data analysis challenges. Contact: axa1828@case.edu. Research Interests: Dr. Abhishek's work bridges theoretical mathematics with applied problems, particularly in developing robust algorithms for inverse problems. His interests include the Radon transform, Bayesian methods, and numerical solutions for partial differential equations. He explores applications in medical imaging (e.g., electrical impedance tomography) and signal processing. Recent Work Trends: His publications (2018–2025) emphasize advancing inversion techniques for Radon transforms, improving neural network-based approaches for inverse problems, and refining Bayesian estimators for imaging modalities. Key themes include noise resilience, adaptive estimation, and interdisciplinary applications in physics and medicine. Advising & Grants: No specific grants or advisees are listed, but his research likely involves graduate students in applied mathematics and statistics. His website (https://sites.google.com/view/anujabhishek/home) may provide further details on collaborative projects and funding.

Hal SternView profile

Professor

• Bayesian Statistical Methods
• Forensic Science
• Psychology
• +5 more

Hal Stern is Chancellor’s Professor and Provost/Executive Vice Chancellor at the University of California, Irvine (UCI), affiliated with the Department of Statistics within the Donald Bren School of Information & Computer Sciences. He co-directs the National Institute of Standards and Technology (NIST)-funded Center for Statistics and Applications in Forensic Evidence, focusing on improving forensic evidence reliability. Additionally, he leads the National Institute of Mental Health (NIMH)-funded Conte Center, studying early-life adversity impacts on neurodevelopment and mental health. His research integrates Bayesian statistical methods with applications in forensic science, psychology, and neuroscience. Key research areas include forensic evidence analysis (e.g., bloodstain patterns, footwear impressions) and the neurobiological consequences of unpredictable early environments. Stern has pioneered statistical frameworks for combining data across studies to address real-world challenges, such as reducing wrongful convictions through rigorous forensic science. His work on maternal sensory signal unpredictability has revealed links between early-life stress and later mental health outcomes, including depression and anxiety. His funded projects bridge disciplines, uniting statisticians, psychologists, and neuroscientists. Notable collaborations include developing biomarkers for early-life adversity and refining methods to assess forensic evidence reliability. Stern’s administrative roles at UCI further emphasize his leadership in advancing interdisciplinary research and academic governance.

Yuichi KitamuraView profile

Professor

• Econometrics
• Nonparametric Statistics
• Bayesian Methods
• +4 more

Yuichi Kitamura is the Alfred Cowles Professor of Economics and Management at Yale University. He holds a Ph.D. in Economics from Yale and has held faculty positions at the University of Minnesota, University of Wisconsin, and University of Pennsylvania before returning to Yale in 2004. His research focuses on econometrics, including empirical likelihood, inverse problems, nonparametric Bayesian methods, and dynamic discrete choice models. Recently, he developed frameworks for analyzing economic theories using computational science advancements. Editor of Japanese Economic Review Associate/Co-editor for journals like Econometrica , Econometric Theory , and Journal of Business and Economic Statistics Kitamura has received prestigious awards such as the Nakahara Prize and Sloan Fellowship. He has held visiting roles at institutions including the University of Bristol, Cambridge University, and Bar-Ilan University. His research bridges computational science and economic theory, emphasizing methodological innovation. Grants include multiple NSF awards. He actively contributes to academic governance through editorial roles and conferences, including plenary speaking engagements at major international events.

Jennifer HillView profile

Professor

• Causal Inference
• Bayesian Statistics
• Machine Learning
• +3 more

Dr. Jennifer Hill is a Professor of Applied Statistics, Co-Chair of the Department of Applied Statistics, Social Science, and Humanities (ASH), and Co-Director of the PRIISM Center at New York University's Steinhardt School. She holds a PhD in Statistics from Harvard University (2000) and a post-doctoral fellowship in Child and Family Policy from Columbia University (2002). Her research focuses on causal inference methods, Bayesian nonparametric techniques, and addressing challenges in observational studies. Notable contributions include the development of the bartCause software for machine-learning-based causal analysis. Key research areas include causal modeling, policy evaluation, and data science for social impact. Hill co-founded the MS in Applied Statistics for Social Science Research (A3SR), emphasizing social impact through data science. She has been recognized with the 2021 NYU Distinguished Teaching Award and the 2016 Miller Prize for her influential work on bias in grouped data analysis. Her courses cover causal inference, missing data analysis, and data science communication. Hill’s work bridges statistical innovation with real-world applications in health, education, and social policy. The A3SR program, with its dual MPA option, reflects her commitment to training socially responsible data scientists.

Shanshan ZhaoView profile

Adjunct Associate Professor

• Biostatistics
• Environmental Health
• Cancer Research
• +7 more

Dr. Shanshan Zhao is an Adjunct Associate Professor in the Department of Biostatistics at the University of North Carolina at Chapel Hill and a Principal Investigator at the National Institute of Environmental Health Sciences (NIEHS). Her research focuses on developing statistical methods to understand environmental and social influences on health, particularly in cancer epidemiology and risk prediction. She holds a secondary appointment in the NIEHS Epidemiology Branch and leads a group integrating statistical methodology with population-based studies. Her work emphasizes disease risk assessment using survival analysis, longitudinal data, and biomarker evaluation. Key research areas include multivariate failure time data, environmental mixture analysis, and epigenetic biomarkers. She has contributed to studies on breast, prostate, and colorectal cancers, emphasizing spatial-temporal modeling and public health applications. Zhao’s awards include the 2014 ASA David P. Byar Travel Award and recognition for her 2023 NIEHS publication on environmental breast cancer risk factors. Her team develops software tools like mhazard for multivariate survival analysis and STAFT for spatio-temporal modeling. Her lab collaborates across disciplines to advance environmental health sciences, with a focus on translating statistical innovations into actionable insights for clinicians and policymakers.

Rebecca KillickView profile

Professor

• Time Series Analysis
• Changepoint Detection
• Wavelet Methods
• +1 more

Professor Rebecca Killick is Chair of Statistics at Lancaster University's School of Mathematical Sciences. She develops statistical models for nonstationary time series with applications in environmental science, health monitoring, and econometrics. Research expertise includes: Changepoint detection algorithms Locally stationary wavelet processes Multivariate changepoint methods Environmental time series forecasting Recent publications apply changepoint methods to soil moisture dynamics, ecosystem services, and economic indicators. Work appears in statistics and interdisciplinary journals with strong computational focus. Current research group includes: 9 PhD students and postdoctoral researchers Projects funded by EPSRC, NERC, and Innovate UK Software contributions: changepoint R package (CRAN) changepoint.np for nonparametric detection EnvCpt for environmental applications

Linglong KongView profile

Professor

• Functional and Neuroimaging Data Analysis
• Statistical Machine Learning
• Robust Statistics
• +4 more

Linglong Kong is a Professor and Canada Research Chair in Statistical Learning at the University of Alberta's Department of Mathematical and Statistical Sciences. He is also a Fellow at the Alberta Machine Intelligence Institute (Amii) and holds a Canada CIFAR AI Chair. His expertise spans statistical machine learning, neuroimaging data analysis, and AI-driven healthcare solutions. Education: PhD in Statistics, University of Alberta (2009) MS in Statistics, Peking University (2002) BS in Probability and Statistics, Beijing Normal University (1999) Research Interests: Kong focuses on functional neuroimaging data analysis, robust statistical methods, and applications of machine learning in healthcare. His work emphasizes fairness, privacy, and interpretability in AI systems. Key areas include differential privacy, quantile regression, and predictive modeling for medical imaging and precision medicine. Recent Trends in Articles: His publications address cutting-edge challenges in privacy-aware machine learning, federated learning frameworks, and neuroimaging biomarkers. Recent work includes advancements in fairness for precision medicine and scalable methods for high-dimensional data analysis. Awards: Fellow of Amii (2021) Great Supervisor Award (2018) Josephine Mitchell Mentoring Award (2017) Grants & Advising: Kong leads major grants on AI ethics, neuroimaging biomarkers, and labor market equality. He has advised numerous students, including PhD candidates and postdocs, in statistical machine learning and healthcare analytics. Labs & Teams: Active in the Alberta Machine Intelligence Institute (Amii) and collaborates with interdisciplinary teams in neuroimaging, precision medicine, and AI ethics initiatives.

Hongbo XieView profile

Research Fellow

• Statistical/Bayesian Inference
• Machine Learning
• Deep Learning
• +3 more

Dr Hongbo Xie is a Vice-Chancellor’s Senior Research Fellow/Lecturer at QUT and an Associate Investigator at ACEMS. He holds a PhD in Biomedical Engineering from Shanghai Jiao Tong University. His research focuses on statistical/Bayesian inference, machine learning, biomedical signal/image processing, and matrix/tensor analysis for signal recovery. With over 70 publications (as of 2022), his work spans IEEE journals, conference proceedings, and book chapters. Education: PhD in Biomedical Engineering, Shanghai Jiao Tong University, China Research Interests: Statistical and Bayesian methodologies for complex data Machine learning applications in biomedical signal processing Image recovery via matrix and tensor analysis Advanced signal processing for healthcare and engineering Publications: His recent work includes EEG-based analysis of adolescent psychological distress, variational Bayesian models for movement control, and kernelized Bayesian matrix factorization. Articles frequently address biomedical signal/image processing, emphasizing robust algorithms for real-world applications. Affiliations: School of Mathematical Sciences (QUT), ACEMS.

Nathaniel JosephsView profile

Assistant Professor

• Networks
• Bayesian nonparametrics
• Causal inference
• +4 more

Nathaniel Josephs is an Assistant Professor in the Department of Statistics at NC State University. His research focuses on networks, Bayesian nonparametrics, and causal inference, with applications spanning social science, materials science, and biostatistics. He holds a Ph.D. in Statistics from Boston University (2021). Education Ph.D. in Statistics, Boston University, 2021 Research Interests Josephs specializes in developing statistical methodologies for network data analysis, including Bayesian approaches for community detection, hypergraph models, and applications in epidemiology, sports analytics, and microbiome studies. His work bridges theoretical statistics with practical challenges in diverse fields such as OLED material prediction and oncology outcomes research. Publications Recent work includes advancements in network scale-up methodologies, hypergraph-based scoring systems, and Bayesian classification for microbiome data. His research demonstrates a strong emphasis on interdisciplinary applications, combining rigorous statistical frameworks with real-world problems in health sciences and engineering. Awards & Grants No awards or grants explicitly listed in available materials. Advising & Labs No advising information or lab affiliations specified in the provided text.

Ryan MartinView profile

Professor

• Foundations of statistics
• Foundations of probability
• Generalized Bayes and Gibbs posterior distributions
• +5 more

Ryan Martin is a Professor in the Department of Statistics at North Carolina State University (NC State). He holds a Ph.D. in Statistics from Purdue University (2009) and a B.A. in Mathematics from Franklin College (2003). His research focuses on foundational aspects of statistics and probability, including generalized Bayes methods, imprecise probability, and high-dimensional problems. He co-founded Researchers.One, an innovative peer review platform. Education: Ph.D., Statistics, Purdue University, 2009 M.S., Statistics, Indiana University-Purdue University Indianapolis, 2005 B.A., Mathematics, Franklin College, 2003 Research Interests: His work emphasizes foundational statistical theory, with applications in imprecise probability, Bayesian methods, and high-dimensional inference. Notable contributions include inferential models (IMs), possibilistic frameworks, and regularization techniques for anytime valid inference. He actively explores computational methods for statistical efficiency and reliability. Recent Contributions: Recent work includes advancements in e-processes for efficient uncertainty quantification, variational approximations for possibilistic models, and theoretical developments in Bernstein-von Mises theorems. He also investigates Fisher's underworld and reliability in scientific inference. Awards and Honors: D. D. Mason Award (2022) Van Dantzig Seminar Speaker (2021) Sigma Xi nominee (2020) Thank-a-Teacher Award (2018) I. W. Burr Award (2010) Advising and Grants: He has mentored over 50 graduate students and secured multiple NSF grants. Current projects include Bayesian model selection in high-dimensional settings and imprecise-probabilistic inference. Labs/Teams: Co-leads the Researchers.One initiative, promoting open scholarly communication. Active in collaborative networks on imprecise probability and statistical foundations.

Ana-Maria StaicuView profile

Professor

• Functional Data Analysis
• Biostatistics
• Longitudinal Data Analysis
• +5 more

Dr. Ana-Maria Staicu is a Professor in the Department of Statistics at North Carolina State University (NCSU), where she has been since 2009, progressing from Assistant to Associate Professor. She holds a Ph.D. in Statistics (2007) from the University of Toronto and a B.Sc. in Mathematics from the University of Bucharest (2000). Her research focuses on functional data analysis, longitudinal studies, biostatistics, and applications in medical imaging and social media analytics. She leads grants funded by NIH, NSF, and industry partners, including work on social media disinformation and biomedical imaging. Education: Ph.D., University of Toronto (2007); B.Sc., University of Bucharest (2000) Affiliations: Director of the NIH T32 Biostatistics Training Grant; Associate Editor for Stat , Biometrics , and JABES Research Interests: Functional data analysis, longitudinal modeling, spatial statistics, and applications in neuroscience, environmental science, and healthcare. Notable contributions include methods for analyzing brain imaging data (e.g., DTI in MS) and wearable device data. Awards: NSF CAREER Award (2015), ASA SPES Best Paper (2021), NCSU Faculty Scholar (2016), Cavell Brownie Mentoring Award (2019). Over 60 peer-reviewed publications and 10+ grants totaling $6M+. Grants/Service: Current projects include NSF-funded social media pathway analysis and NIH-funded biostatistical training in cardiovascular research. Served on grant review panels for NSF, NIH, and international foundations. Labs/Teams: Leads the FDA Lab at NCSU; collaborates with Duke-NC State-DCRI training programs and SAMSI initiatives.