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Changbo ZhuView profile

Assistant Professor

• Optimal Transport
• Functional and Object Data Analysis
• High-Dimensional Statistics
• +6 more

Changbo Zhu is an Assistant Professor in the Department of Applied and Computational Mathematics and Statistics at the University of Notre Dame, part of the College of Science. He is also a Fellow of the Lucy Family Institute for Data & Society. His office is located at 101H Crowley Hall, and he can be reached at czhu4@nd.edu. Dr. Zhu holds a Ph.D. from the University of Illinois at Urbana-Champaign (2020), an M.S. and B.S. from the National University of Singapore (2016 and 2014, respectively). He completed a postdoctoral fellowship at the University of California, Davis from 2020 to 2022. His research focuses on the intersection of statistics, geometry, and optimization, with key interests in optimal transport, functional and object data analysis, time series analysis, high-dimensional statistical inference, and statistical machine learning. His work addresses challenges such as longitudinal data analysis, change point detection, and distance covariance methodologies. Zhu’s recent publications emphasize advancements in optimal transport theory, including barycenter optimization and autoregressive models, as well as applications in neuroimaging and high-dimensional data analysis. He collaborates with leading researchers such as Hans-Georg Müller and Jane-Ling Wang on topics ranging from spherical autoregressive models to brain volume trajectory studies. He advises Kaheon Kim, a Ph.D. candidate focusing on statistical optimal transport. His teaching portfolio includes courses on optimization algorithms for machine learning, computational statistics with R, and statistical computing methods. Zhu is affiliated with the Lucy Family Institute for Data & Society, contributing to interdisciplinary data science initiatives.

• Cardiology
• Coronary Artery Disease
• Myocardial Infarction
• +4 more

Rune Wiseth is a Professor affiliated with the Norwegian University of Science and Technology (NTNU). His research focuses on cardiovascular disease mechanisms, particularly coronary artery disease, myocardial infarction, and inflammatory pathways. He has led and contributed to multiple international clinical trials, including the PROSPECT II and ASSAIL-MI studies, investigating vulnerable plaque identification and interleukin-6 inhibition therapies. Key contributions include advancing near-infrared spectroscopy for plaque characterization, evaluating pharmacological agents like tocilizumab, and optimizing interventional cardiology practices. He has authored over 50 peer-reviewed articles in journals such as The Lancet , Circulation , and Journal of the American College of Cardiology , with a focus on translational research bridging basic science and clinical practice. Wiseth collaborates extensively with institutions globally, contributing to guidelines on STEMI management and perioperative cardiac risk assessment. His work integrates imaging innovations, biomarker discovery, and interventional strategies to improve cardiovascular outcomes.

Liang HuaView profile

Professor

• Partially Linear Models
• High-Dimensional Semi-parametric Modeling
• Model Averaging and Model Selection
• +4 more

Roles and Affiliations: Liang Hua is a Professor of Statistics and Director of Doctoral Programs at the Department of Statistics, The George Washington University. He previously held positions at the University of Rochester Medical Center, St. Jude Children's Research Hospital, and Humboldt University as an Alexander von Humboldt Fellow. Education: Hua holds two Ph.D.s: one in Statistics from Texas A&M University (2001) under Raymond J. Carroll, and another in Mathematical Statistics from the Chinese Academy of Sciences (1992) under Ping Cheng. Professional Service: He serves as an Associate Editor for multiple journals including JASA, Journal of Nonparametric Statistics, and Biostatistics. His expertise spans partially linear models, high-dimensional modeling, and HIV/AIDS clinical trial design. Research Interests: Hua focuses on statistical methodology development, including model averaging, longitudinal data analysis, and measurement error models. His work bridges theoretical advancements and practical applications in biostatistics and public health. Awards and Recognition: Hua is a Fellow of the ASA, IMS, and Royal Statistical Society, and an elected member of the International Statistical Institute. He also received the prestigious H. O. Hartley Award from Texas A&M University. Grants and Advising: While specific grants are not detailed, his extensive editorial roles and high-impact publications reflect significant research leadership. He has advised numerous students through doctoral programs at GWU.

Yuejiao Cindy FuView profile

Professor

• Statistical Modeling
• Mixture Models
• Empirical Likelihood
• +2 more

Yuejiao Cindy Fu is a Full Professor in the Department of Mathematics and Statistics at York University's Faculty of Science. Her office is located in the Ross Building. Research specializes in mixture models, empirical likelihood, density ratio models, and statistical analysis of high-dimensional spatial, genetic, and DNA methylation data. Methodologies include homogeneity testing, dimension reduction techniques, and robust inference for genomic applications. Education: Ph.D. in Statistics from University of Waterloo (2004). Contact: (647) 831 1208.

Scott CrawfordView profile

Associate Professor

• Statistical Consulting
• Regression Analysis
• Missing Data Methods
• +3 more

Scott Crawford is an Instructional Associate Professor in the Department of Statistics at Texas A&M University, serving as Director of the Statistical Consulting Center. He holds a PhD in Statistics from Texas A&M University (2012), a Master's from Brigham Young University, and a Bachelor's from Southern Utah University. His expertise spans statistical consulting, regression analysis, and online education methodologies. He has taught extensively, including introductory statistics courses and specialized consulting seminars, and has advised on numerous research projects across disciplines. Education: PhD in Statistics, Texas A&M University, 2012 Master's in Statistics, Brigham Young University Bachelor's in Mathematics, Southern Utah University Teaching Roles: STAT 211 (Intro to Engineering Statistics) STAT 212 (Intermediate Statistics) STAT 312 (Methods) STAT 659 (Categorical Statistics) STAT 681 (Seminar) STAT 692 (Consulting) His research focuses on addressing missing data in regression models and developing educational resources for statistics. He has directed the Statistical Consulting Center since 2020, supporting faculty and graduate students in research design and analysis. Beyond academia, he prioritizes family and enjoys creative pursuits like writing murder mystery scenarios and board games.

• Multivariate Statistics
• Statistical Decision Theory
• Asymptotic Methods
• +3 more

Martin Bilodeau is a full professor in the Department of Mathematics and Statistics at Université de Montréal. He holds a Ph.D. from the University of Toronto (1986) and is an Associate of the Society of Actuaries (ASA) since 1993. His research focuses on multivariate statistics, statistical decision theory, asymptotic methods, and robust statistics. Bilodeau has authored a notable textbook, *Theory of Multivariate Statistics* (Springer, 1999), co-authored with David Brenner, which provides a rigorous treatment of modern multivariate statistical theory. His work emphasizes foundational topics such as multivariate regression, principal components analysis, and robust statistical methods. Bilodeau has also contributed to computational statistics through R packages like *groc* and *IndependenceTests*, which implement advanced statistical techniques for regression analysis and independence testing. Recent publications include methodological advancements in variance component models (2021), independence testing across scales (2017), and applications of meta-elliptical distributions (2014). Bilodeau's research integrates theoretical developments with practical applications, spanning fields from ecology to actuarial science. His work on nonparametric independence tests (2007) and robust SUR models (2000) demonstrates his commitment to both statistical methodology and real-world problem-solving. He maintains an active academic profile with contributions to statistical education and ongoing collaborations in multivariate analysis.

Kelly BedardView profile

Professor

• Economics of Education
• Health Economics
• Gender Test Score Gaps
• +3 more

Kelly Bedard is a Professor of Economics at the University of California, Santa Barbara (UCSB), affiliated with the Department of Economics within the College of Letters & Science. Her research focuses on the Economics of Education and Health , including studies on military service impacts, gender test score gaps in OECD countries, and immigrant health dynamics. Notably, her work on kindergarten student age effects was highlighted in Malcolm Gladwell's bestselling book. Her research has been supported by grants from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK). Her work explores policy-relevant topics such as paid family leave programs, classroom competition dynamics, and ADHD diagnosis disparities. Bedard's recent studies analyze California's paid family leave policies using administrative data, revealing insights into benefit utilization and demographic disparities. She also investigates gender equity in academic tenure policies and feedback mechanisms influencing student retention in economics. Key Research Themes: Education Policy, Health Economics, Labor Markets, Gender Disparities Grants: Two NIDDK grants supporting her research Notable Collaboration: Consortium for Applied Research in Economics Bedard's work bridges academic rigor with real-world policy implications, particularly in education and labor markets. Her experimental approaches to studying classroom competition and team incentives provide actionable insights for educational and organizational design.

Valérie CourchesneView profile

Adjunct Professor

• Autism Spectrum Disorders
• Neuropsychology
• Child Development
• +3 more

Valérie Courchesne is an Assistant Professor in the Department of Psychology at Université de Montréal, affiliated with the Faculté des arts et des sciences. Her research focuses on autism, neuropsychology, and mental health interventions. She holds a PhD in clinical psychology (2017) and completed postdoctoral training at McGill University and the Centre for Addiction and Mental Health (Toronto). Her work emphasizes strength-based assessments, mental health risk/resilience factors, and tailored psychotherapy for autistic populations. Education : PhD in Research & Intervention Clinical Psychology (2017), Université de Montréal Bachelor's in Psychology (2011), Université de Montréal Research Interests : Advocating for autism neurodiversity, developing inclusive assessment tools, exploring mental health challenges in autistic youth, and designing interventions aligned with autistic cognitive strengths. Her work combines clinical practice with translational research, aiming to improve diagnostic accuracy and therapeutic outcomes. Recent Article Trends : Focus on diagnostic certainty, calendar calculation abilities in autism, mental health risk factors, and longitudinal language development in autistic children. Her studies highlight the need for context-specific assessments and emphasize autistic individuals' unique cognitive profiles. Awards : Over 20 awards including the Dialogue Scholarship (2020), Governor General's Medal finalist (2018), and multiple best presentation awards at international conferences. Grants & Teams : Leads the CREIA (Centre de recherche, d’évaluation et d’interventions en autisme) and collaborates on Quebec Integrative Cohort (QUIC). Supervises over 20 graduate students and has coordinated clinical teams in autism research. Labs & Affiliations : Associated with the Azrieli Center for Autism Research (ACAR) and the CIUSSS Nord-de-l’île-de-Montréal research center. Co-directs the Moveo Psychology Clinic and contributes to public outreach via the magazine Sur le spectre .

Chad A. ShawView profile

Professor

• Statistics
• Genomics
• Systems Biology
• +3 more

Chad A. Shaw is a Professor at Baylor College of Medicine and a Joint Professor of Statistics at Rice University. He serves as Director of the D2K Laboratory at Rice, and holds additional roles including Investigator at the Jan and Dan Duncan Neurological Research Institute and Faculty Member at the Baylor College of Medicine’s STaR Center and Quantitative & Computational Biosciences program. His expertise spans statistical genomics, bioinformatics, and molecular genetics. Education: BS in Mathematics (Duke University, 1995); PhD in Statistics (Rice University, 2001). Research Interests: Focuses on statistical methods for genomics, including next-generation sequencing analysis, structural variation studies, and clinical exome sequencing. Key contributions include probabilistic models for mutation transmission in human genetic diseases, copy-number variation analysis, and functionalization of genetic variants. His work bridges computational statistics with clinical applications, addressing challenges in rare disease diagnostics and Mendelian disorder modifiers. Publications: Over 200 peer-reviewed articles, including foundational studies on recurrence risk in genetic disorders and mechanisms of structural variation. Recent work emphasizes Bayesian modeling in sequencing assays, CNV-driven genetic diseases, and prenatal genomic analysis. Awards/Grants: No explicit awards mentioned, but his research has been cited ~17,000 times. Active in grant-funded projects across computational and clinical genomics. Advising: Trained 5 PhD students and advised over 10 others, with students contributing to areas like high-dimensional regression and software tools for variant prioritization. Labs/Teams: Leads the D2K Laboratory at Rice, fostering interdisciplinary research in data science and computational biology.

James G. MacKinnonView profile

Professor

• Bootstrap Methods
• Cluster-Robust Inference
• Econometric Theory
• +3 more

James G. MacKinnon is the Sir Edward Peacock Professor of Econometrics at Queen’s University, Department of Economics. He holds a B.A. (York University), M.A., and Ph.D. (Princeton University). His research focuses on bootstrap methods, cluster-robust inference, and econometric theory. He has authored influential textbooks like *Estimation and Inference in Econometrics* and *Econometric Theory and Methods*. MacKinnon has served as Head of the Department of Economics (2003–2013) and President of the Canadian Economics Association (2001–2002). His honors include Fellowships from the Econometric Society, Royal Society of Canada, and International Association for Applied Econometrics. His work spans over 150 publications, emphasizing robust statistical methods and empirical practice. Notable contributions include numerical distribution functions for cointegration tests and cluster-robust inference guidelines. He has advised numerous graduate students and collaborates on software tools for econometric analysis, such as Stata modules for bootstrap inference.

Yining ChenView profile

Associate Professor

• Statistical Methodology
• Change-Point Detection
• Nonparametric Estimation
• +4 more

Yining Chen is an Associate Professor in the Department of Statistics at the London School of Economics and Political Science (LSE). He holds a PhD in Statistics from the University of Cambridge (2014). His research focuses on statistical methodology, particularly in change-point detection, nonparametric estimation, and computational aspects of statistical methods. He also explores shape-constrained estimation and time series analysis. Dr. Chen has taught courses such as ST444 Computational Data Science and ST304 Time Series and Forecasting at LSE. He has contributed to interdisciplinary research in medical statistics, including work on kidney and liver transplantation outcomes. His software contributions include R packages like 'not', 'scar', and 'Sshaped', which implement his methodological advancements in statistical computing. His research has been published in top-tier journals such as the Journal of the Royal Statistical Society and Biometrika. Key areas of application span econometrics, actuarial science, and biomedical research. He actively engages in collaborative projects addressing practical data challenges across disciplines.

Kenichi NagasawaView profile

Assistant Professor

• Econometrics
• Bootstrap Methods
• Statistical Inference
• +1 more

Kenichi Nagasawa is an Assistant Professor in the Department of Economics at the University of Warwick. He specializes in econometrics with a focus on bootstrap-assisted inference, treatment effect estimation, and asymptotic theory. His research develops methodologies for statistical inference in complex econometric models. He teaches EC226: Econometrics 1 and has secured funding through the BA/Leverhulme Small Research Grants (2024-2026). His work frequently involves collaborative research with institutions globally. Research Focus: Development of robust inference techniques for econometric models, particularly using bootstrap methods for nonstandard distributions and treatment effect estimation with imperfect data.

• Mathematical Optimization
• Statistical Modeling
• Machine Learning
• +13 more

Vanesa Guerrero Lozano is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid, affiliated with the Flores de Lemus Institute and the UC3M-Santander Big Data Institute. Her work bridges mathematical optimization, statistical modeling, and data science, with applications across disciplines including biomedicine, fluid mechanics, and sustainable development. Her research focuses on developing advanced statistical methodologies using mathematical optimization. Key interests include shape-constrained regression, P-splines smoothing, sparse modeling, clustering of categorical data, and interpretable machine learning. She applies these techniques to complex datasets in turbulence modeling, biological age imputation, and pandemic forecasting. The recent publications reveal a strong trend in integrating optimization techniques with statistical learning, particularly in nonparametric and semiparametric models. There is a consistent emphasis on interpretability, robustness, and scalability, especially for high-dimensional and dynamic datasets. Applications span from fluid dynamics to public health, demonstrating interdisciplinary impact. Scientific Awards and Recognition: Ayuda adicional within the Juan de la Cierva Incorporación Program (2020), awarded by the State Research Agency (AEI) Research Leadership and Advising: She has served as principal investigator on multiple competitive research projects funded by national and regional agencies, including the State Research Agency (AEI), the Jacques Hadamard Mathematical Foundation, and the Community of Madrid. Her projects cover topics such as constrained additive models, machine learning for sustainable fishing, turbulence control, and ADHD diagnosis using data science. She has supervised at least one doctoral thesis on constrained smoothing models, indicating active mentorship in methodological statistics and optimization. Laboratories and Research Groups: She is a member of the Energy Analytics research group and conducts her work within the UC3M-Santander Big Data Institute, which supports interdisciplinary data science research. Her affiliation with the Flores de Lemus Institute further underscores her engagement with advanced statistical and computational methodologies.

Chris HeatonView profile

Senior Lecturer

• Econometrics
• Latent Factor Models
• Multivariate Time Series Analysis
• +6 more

Chris Heaton is a Senior Lecturer in the Department of Economics at Macquarie University, affiliated with the Emerging Risks Research Centre. His academic career spans over two decades with research outputs from 1998 to 2025, demonstrating sustained scholarly activity in econometrics and applied economic research. Dr. Heaton's research focuses on identification and estimation of latent factor models of multivariate time series, with significant contributions to labor economics, economics of education, forecasting, and financial economics. His methodological expertise intersects with substantive applications examining inequality of opportunity, body mass determinants, cross-border investments, and women's empowerment through cash transfer programs. His research fingerprint reveals strong connections to Factor Models (95% relevance), Education, Quantile analysis, and Inequality of Opportunity. His publication record includes 28 research outputs across prestigious journals including The Econometrics Journal, The Journal of Multivariate Analysis, The International Journal of Forecasting, Economics of Education Review, and Health Economics. Recent work demonstrates continued productivity with five publications in 2024-2025 spanning financial econometrics, health economics, innovation studies, institutional economics, and development economics. Supervised 7 PhD students to completion Supervised 8 Masters students to completion Supervised 14 Honours students to completion Principal investigator on research computing infrastructure project (2007) Dr. Heaton maintains active research collaborations across multiple countries and disciplines, with recent external collaborations spanning international boundaries. His work has garnered attention across academic platforms with presentations referenced by social media users, picked up by news outlets, and read by researchers on platforms like Mendeley.

Marco ErcolaniView profile

Associate Professor

• Applied Econometrics
• Labour Economics
• Health Economics
• +12 more

Marco Ercolani is an Associate Professor in Econometrics at the Department of Economics , Birmingham Business School , University of Birmingham . His research applies econometric methods to socio-economic issues in health, ageing, education, and labour markets. He teaches courses in quantitative methods, labour economics, and time series regression. Education: B.A. in Economics, University of Sussex M.A. in Econometrics, University of Essex Ph.D. in Economics, University of Essex His research interests focus on applied econometrics , particularly using cross-sectional, panel, and time-series data to test theories against real-world data. Key areas include sickness absence behaviour, seasonal economic cycles, Euro adoption effects on prices, and labour market outcomes across age groups. He emphasizes 'bringing data to theories' and develops custom statistical programming for non-standard analyses. The recent publications reflect a strong trend in interdisciplinary applied research, combining econometrics with public health (antibiotic disposal, antimicrobial resistance), environmental economics (climate change and migration), education policy (subject choice, student performance), and development economics (time poverty in Ghana, intergenerational mobility). His work increasingly involves policy-relevant analysis, including commissioned reports for the UK Department for Transport. Scientific Contributions: Developed statistical software (SAVE9) for Stata. Authored book chapters on China's economic dualism and Euro inflation effects. Published book reviews in The Economic Journal . He supervises PhD students in labour econometrics and collaborates widely across disciplines. His work appears in journals such as Economics of Education Review , Feminist Economics , The Energy Journal , and Higher Education . He is actively engaged in current research and policy analysis, contributing to both academic discourse and practical policy solutions. Laboratory/Research Group: Member of the Econometrics Research Group within the Department of Economics at the University of Birmingham.