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Harlan CampbellView profile

Adjunct Professor

• Bayesian Statistics
• Meta-Analysis
• Causal Inference
• +3 more

Harlan Campbell is an Adjunct Professor in the Department of Statistics at the University of British Columbia (UBC), affiliated with the Faculty of Science. His research focuses on Bayesian statistics, causal inference, meta-analysis, and statistical methodology, with applications in medical and public health contexts. Campbell collaborates internationally, such as leading the Zika virus individual participant data consortium to study pregnancy outcomes. He has advised at least one student: Nathaniel Wu Dyrkton. His work integrates evidence synthesis, clinical trial analysis, and methodological innovation to address gaps in data reporting and analysis across disciplines. Education details are not explicitly provided, but his career reflects advanced training in statistical theory and applications. Research interests include developing robust statistical techniques for observational studies, addressing measurement error in meta-analyses, and improving methodologies for indirect treatment comparisons. He has contributed to frameworks for estimating infection fatality rates during the COVID-19 pandemic and advancing Bayesian approaches to evidence synthesis. Publications span topics like structural uncertainty in evidence synthesis, doubly robust estimators for clinical trials, and causal inference in longitudinal studies. His work emphasizes rigorous statistical methods to enhance reliability in medical and epidemiological research. Grants and collaborations are implied through his involvement in global consortia and methodological reviews but are not explicitly detailed in the provided text. Campbell maintains an active Google Scholar profile with extensive citation activity.

• Renewable Energy
• Energy Storage
• GIS Analysis
• +3 more

Dr. Cheng Cheng is a Senior Research Officer at the Australian National University's School of Engineering. He holds a Bachelor of Engineering (Honours) and PhD from ANU. His research focuses on renewable energy integration, decarbonization pathways, energy system modeling, and GIS analysis. He specializes in optimizing off-river pumped hydro energy storage systems. Cheng leads multiple renewable energy projects funded by government and industry grants, and has developed methodologies for energy demand forecasting and least-cost optimization. His work bridges theoretical energy modeling with practical implementation through collaborative outreach activities. Education: B.Eng(Hons) & PhD (Australian National University) Research interests emphasize sustainable energy transitions, with particular attention to geospatial analysis for energy infrastructure planning. His recent projects include developing frameworks for integrating renewable generation with storage solutions. Over 150 publications span machine learning applications in energy systems, astrophysics, and bioinformatics. Awards include academic fellowships supporting his interdisciplinary work. Cheng collaborates with industry partners to translate research into policy and technology. He directs the ANU's Renewable Energy Integration Lab (RE100), advancing grid stability solutions through innovative storage technologies. His work has been recognized in special issues of journals focusing on robust machine learning applications.

Nigel ClarkeView profile

Professor

• Theoretical Polymer Physics
• Soft Matter Physics
• Rheology
• +3 more

Professor Nigel Clarke is a faculty member at the University of Sheffield, affiliated with the School of Mathematical and Physical Sciences. He holds the academic rank of Professor of Condensed Matter Physics Theory. His research focuses on theoretical polymer physics, soft matter physics, rheology, and neutron scattering techniques applied to polymer nanocomposites. Key interests include understanding microscale structures in nanocomposites, charge transport in organic photovoltaics, and the dynamics of polymer blends and interfaces. Professor Clarke has contributed extensively to the field through publications analyzing polymer dynamics, nanoparticle migration, and the application of machine learning in materials science. His work bridges theoretical modeling with experimental techniques like neutron scattering and SANS. He is a Fellow of the American Physical Society and serves as Chair of the IOP Polymer Physics Group, reflecting his leadership in polymer physics research. His research trends emphasize interdisciplinary approaches, combining computational modeling with experimental validation to study material properties and dynamics. Notable contributions include studies on carbon nanocomposite structures, polymer blend morphologies, and the role of nanoparticles in altering polymer behavior. Awards: Fellow of the American Physical Society Professional Roles: Chair of the IOP Polymer Physics Group, Member of EPSRC e-infrastructure Strategic Advisory Group His work often explores the interplay between structure and functionality in materials, with applications in energy materials, optoelectronics, and advanced composites.

Benoit Liquet-WeilandView profile

Professor

• Biostatistics
• Survival Analysis
• Machine Learning
• +10 more

Benoit Liquet-Weiland is a Professor at Macquarie University's School of Mathematical and Physical Sciences, affiliated with the Astrophysics and Space Technologies Research Centre and Data Horizons Research Centre. His research focuses on statistical methodologies applied to biomedical and environmental data, including multi-state models, survival analysis, Bayesian modeling, and machine learning. He teaches advanced courses in statistics such as Generalized Linear Models and The Mathematical Engineering of Deep Learning. Education: HDR (PhD Supervisor diploma) in Statistical Modelling and Biomedical Applications from University of Bordeaux (2009), and a PhD in Statistics from Université de Bordeaux (2002). Research Interests: Model selection and variable selection Survival analysis and multi-state models High-dimensional omics data analysis Machine learning applications in healthcare and ecology Statistical methods for precision medicine Recent work emphasizes interdisciplinary applications, including predicting ICU outcomes for COVID-19 patients, detecting pleiotropic effects in cancer datasets, and coral classification using satellite imagery. He leads or collaborates on projects like FightMND MCR Fellowship and computational analysis in brain tumor imaging. Recipient of the 2023 Faculty of Science and Engineering Award for Inter-School Collaboration. Active in supervising PhD students in statistical methodologies and applications.

Yusha LiuView profile

Assistant Professor

• Biostatistics
• Genomics
• Cancer Research
• +6 more

Dr. Yusha Liu is an Assistant Professor in the Department of Biostatistics at the UNC Gillings School of Global Public Health. She holds a PhD in Statistics from Rice University (2019), an MS in Biostatistics from Yale University (2014), and a BS in Biology from Peking University (2012). Her research focuses on developing statistical methods for analyzing large-scale genomic data, particularly in cancer research and biomarker-driven clinical trials. PhD: Rice University (2019) MS: Yale University (2014) BS: Peking University (2012) Her research interests include single-cell data modeling, omics data integration, Bayesian inference, functional data analysis, and quantile regression. She applies these methods to understand complex diseases such as cancer and develop targeted therapies. Awards include the Ralph Budd Best Engineering PhD Thesis Award (2020) and the Distinguished Student Paper Award (2020, ENAR). Her work bridges statistical innovation with biomedical applications, emphasizing scalable solutions for modern genomic datasets. Liu has contributed to adaptive clinical trial designs for biomarker analysis and pioneered methods for dissecting tumor heterogeneity using single-cell RNA-seq. Her research often addresses translational challenges in precision medicine.

Xianming TanView profile

Professor

• Biostatistics
• Clinical Trials
• Bayesian Methods
• +7 more

Xianming Tan, PhD, is a Professor in the Department of Biostatistics and Research Associate Professor at the Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill. He holds a PhD in Statistics from Nankai University (2005), an MS in Economics from Huazhong University of Science and Technology (1998), and a BS in Mathematics from Beijing Normal University (1995). His research focuses on developing advanced statistical methods for clinical trials and public health, including innovative trial designs, Bayesian methods for drug safety assessment, and applications of AI in healthcare. He collaborates extensively with clinicians at Lineberger on cancer-related studies, particularly in pharmacovigilance, treatment optimization, and high-dimensional data analysis. Key research areas include finite mixture models for population heterogeneity, convolution regression, and interdisciplinary applications in oncology. His work bridges statistical theory and practical healthcare challenges, addressing issues like latent confounder control, treatment effect dynamics, and healthcare equity. He currently serves on the Department of Biostatistics Admissions Committee since 2023. His publications span top journals like Statistics in Medicine , Annals of Applied Statistics , and Systematic Reviews , with a focus on methodological advancements and translational research.

Ivan SvetunkovView profile

Senior Lecturer

• Forecasting
• Statistical Modelling
• Marketing Analytics
• +5 more

Ivan Svetunkov is a Senior Lecturer at Lancaster University's Management Science Department, specializing in forecasting and statistical modeling. He holds a PhD from Lancaster Centre for Forecasting (2013) and previously worked at Higher School of Economics and Saint-Petersburg State University. His research focuses on state-space models, intermittent demand forecasting, complex-valued models, and marketing analytics. Education: MSc from Saint-Petersburg State University of Economics and Finance (2006), PhD in Economics (2013). Key contributions include the Complex Exponential Smoothing method and the development of R packages 'smooth', 'greybox', and 'legion'. He maintains academic presence through LinkedIn, Twitter, and a forecasting website. Research interests span intermittent demand modeling, multivariate forecasting systems, advanced estimators, time-varying parameters, promotional modeling, and likelihood-based approaches. He supervises PhD students in statistical forecasting, requiring strong math/programming skills (R/Python). Labs/Teams: Active in Lancaster Centre for Marketing Analytics and Forecasting. Collaborations include work with John Boylan on intermittent demand models and Demand Works on ARIMA implementations.

Emily HectorView profile

Assistant Professor

• Data Integration
• Distributed Estimation
• Correlated Data Analysis
• +6 more

Emily Hector is an Assistant Professor in the Department of Statistics at North Carolina State University. She holds a Ph.D. in Biostatistics from the University of Michigan (2020) and serves as an Associate Editor for Reproducibility at the Journal of the American Statistical Association . Her research focuses on distributed inference, data integration of correlated and high-dimensional datasets, and leveraging computational advancements for divide-and-conquer methods. Applications include spatial extremes, metabolomics, neuroimaging of autism, and wearable devices. Key areas of expertise include estimating equations, generalized method of moments, and parallel computing. Methodological innovations address challenges in big spatial data modeling and high-dimensional phenotypes. Her work bridges statistical theory with practical computational solutions for large-scale biomedical data. Hector has been honored with the Dr. Dennis Boos Citizenship Award (2023-2024). Her publications span topics such as neuroconnectivity analysis, genetic regulation of metabolism, and climate model bias correction. She actively collaborates with interdisciplinary teams and contributes to open-source software for statistical analysis.

Luo XiaoView profile

Associate Professor

• Functional Data Analysis
• Nonparametric Regression
• Biostatistics
• +5 more

Luo Xiao is an Associate Professor in the Department of Statistics at North Carolina State University. His research focuses on statistical methodologies such as nonparametric regression, functional data analysis, and high-dimensional data analysis, with applications in neuroimaging, wearable computing, and genetic data. He leads the Functional Data Analysis Lab at NC State and teaches courses like Nonparametric Regression and Statistical Inference. Education: Ph.D. in Statistics from Cornell University (2012). Research interests emphasize statistical theory and applications, including: Nonparametric and semiparametric regression Functional data analysis Biostatistics Machine learning integration with functional data Medical imaging and single-cell genetic data analysis Recent publications highlight advancements in multivariate functional modeling, Bayesian inference for longitudinal data, and applications in Alzheimer's disease research. He has advised students such as Wanying Ma and Stephanie T. Chen. His work is supported by collaborations in medical and computational domains. Lab affiliations include the Functional Data Analysis Lab, with ongoing projects on wearable data analysis and multi-modal data integration. Teaching responsibilities include advanced statistical courses.

Richard VerrallView profile

Professor

• Actuarial Science
• Actuarial Statistics
• Risk Management
• +7 more

Richard Verrall is a Professor of Actuarial Statistics at the Faculty of Actuarial Science and Insurance, Bayes Business School, City St George's, University of London. He has been with the institution since 1987 and holds roles as an Associate Editor for journals such as British Actuarial Journal , North American Actuarial Journal , and Insurance: Mathematics and Economics . He is also a Principle Examiner for the Actuarial Profession. His expertise spans actuarial statistics, stochastic claims reserving, Bayesian methods, and general insurance modeling. Dr. Verrall holds a PhD from City, University London (1985–1989), an MSc from University College London (1981–1982), and an MA from the University of Cambridge (1978–1981). He is an Honorary Fellow of the Institute of Actuaries (since 1999) and a Fellow of the Royal Statistical Society (since 1982). His research focuses on claims reserving methodologies, Bayesian statistical models, and the application of advanced techniques like generalized linear models, regression splines, and stochastic processes to insurance problems. Notable contributions include the development of the Double Chain Ladder reserving model and methodologies for incorporating expert opinion into predictive models. Key awards include the Casualty Actuarial Society's Best Paper in Variance (2007), Highly Commended Paper for 'Stochastic Claims Reserving in General Insurance' (2002), and the 1993 Casualty Actuarial Society Prize Paper for his work on statistical methods for the Chain Ladder Technique. He has also developed influential R-packages like GeDS and DCL for actuarial modeling. Dr. Verrall teaches courses such as 'Statistics for Insurance,' 'Stochastic Claims Reserving,' and 'Bayesian Actuarial Models' to both academic and industry audiences. His work bridges theoretical actuarial science with practical applications in risk management and insurance.

Scott PowersView profile

Assistant Professor

• Sport Analytics
• Statistics
• Machine Learning
• +5 more

Scott Powers is an Assistant Professor of Sport Analytics and Statistics at Rice University, affiliated with the Department of Sport Management. He holds a PhD in Statistics from Stanford University (2017) and has held roles as Director of Quantitative Analysis for the Los Angeles Dodgers (2017–2021) and Assistant General Manager for the Houston Astros (2022). His research focuses on measuring individual contributions in team sports, optimizing player talent evaluation, and game-theoretic analysis of league incentives. Powers has consulted for multiple professional teams including the Oakland Athletics and AZ Alkmaar. Research interests include sport analytics, statistical machine learning, Bayesian models, and applications in baseball, soccer, and volleyball. He has published in journals like Journal of Quantitative Analysis in Sports and Statistical Modelling , with recent work on volleyball player contributions and MLB pitch trajectory analysis. Awards include the NSF Graduate Research Fellowship and the Jack Kavanagh Memorial Youth Baseball Research Award. Teaching includes courses like Introduction to Sport Analytics and Soccer Analytics. Grants include a MLB Research Gift ($50k) for studying pitching injury biomechanics and a Rice Internal Grant ($10k) for hosting the Rice Soccer Analytics Conference. Powers advises the Sport Analytics Undergraduate Research Lab and coordinates the American Soccer Insights Summit.

Hyungsuk TakView profile

Assistant Professor

• Astrostatistics
• Bayesian Inference
• Computational Statistics
• +3 more

Hyungsuk Tak is an Assistant Professor of Statistics, Astronomy, and Astrophysics at Pennsylvania State University, with a co-hire at the Institute for Computational and Data Science (ICDS). He holds affiliations in the Eberly College of Science's Department of Statistics and Department of Astronomy and Astrophysics. His research focuses on developing astro-statistical tools to address challenges in analyzing astronomical data, including time-delay cosmography for estimating the Hubble constant and stochastic variability modeling using CARMA processes. Dr. Tak earned a Ph.D. in Statistics from Harvard University (2016), an A.M. in Statistics (2012), and a B.A. in Statistics from Korea University (2009). He has received the Derek C. Bok Award for Excellence in Graduate Student Teaching (2016). His research interests span Bayesian inference, computational statistics, and interdisciplinary applications. Notable work includes robust Bayesian meta-analysis for cosmological parameters, scalable time-delay estimation methods (TD-CARMA), and outlier-resistant statistical techniques. He teaches courses such as Design and Analysis of Experiments, Statistical Computing, and Astrostatistics. Publications highlight contributions to cosmography, stochastic processes, and MCMC algorithms. His work bridges statistical rigor and astronomical data complexity, addressing topics like black hole growth, gravitational lensing, and multiband time-series analysis.

Susanna CrambView profile

Associate Professor

• Biostatistics
• Spatial Epidemiology
• Bayesian Modeling
• +3 more

Susanna Cramb is an Associate Professor and Principal Research Fellow at Queensland University of Technology's School of Public Health and Social Work. As an epidemiologist and biostatistician, she investigates spatial disparities in chronic disease outcomes with particular expertise in Bayesian spatio-temporal modeling of cancer survival inequalities. Her work has directly influenced government policy on health equity. Her research interests focus on: Developing advanced statistical methods for disease mapping Analyzing geographic variations in cancer incidence and survival Examining health inequalities related to residential location Applying Bayesian approaches to public health challenges Her publications demonstrate consistent focus on spatial epidemiology methodologies applied to cancer, diabetes, and injury outcomes. Recent work shows increasing application of machine learning techniques alongside traditional Bayesian approaches for health risk prediction. Awards recognizing her contributions include: Queensland Women in STEM Judges' Award (2024) Queensland Young Tall Poppy Science Award (2020) Superstar of STEM (2019/2020 cohort) JK Barrie Award for Overall Excellence for the Australian Cancer Atlas She leads several major projects including the PLACE for Change initiative examining health inequalities, and novel modeling approaches for neighborhood design impacts on chronic disease risk, collaborating with institutions like Cancer Council Queensland.

Masoud BaratiView profile

Assistant Professor

• Electric Power Systems Modeling
• Quantum Computing
• Optimization and Simulation
• +8 more

Masoud Barati is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Pittsburgh's Swanson School of Engineering. Previously, he held similar roles at the University of Houston and Louisiana State University. He earned his PhD in Electrical Engineering from the Illinois Institute of Technology (2009-2013) and completed a postdoctoral fellowship at the University of Chicago Booth School of Business in 2014. His research bridges pure and applied mathematics with electric power systems, focusing on resilience, quantum computing, and control systems. Barati’s academic journey includes a diverse range of studies: his doctoral work at Illinois Tech focused on electrical engineering, while his postdoctoral research at the University of Chicago explored business-related systems through a computational lens. His current role at the University of Pittsburgh allows him to advance interdisciplinary research in energy systems. Research interests span algebraic geometry and category theory for computational problem-solving in power systems, alongside applied areas like optimization, quantum computing, Bayesian statistics, and deep learning. He investigates resilience in power grids under natural hazards and cyber-attacks, with specific attention to AC optimal power flow, energy justice, and multi-interdependent infrastructure management. His work integrates smart city technologies, blockchain for energy trading, and data-driven methods such as kernel regression and neural networks for real-time grid analysis. His recent publications emphasize quantum protocols for entanglement detection, smart city energy infrastructure resilience, and machine learning-augmented approaches to power system control. These trends reflect a focus on leveraging advanced computational tools to address modern energy challenges. Barati has contributed to collaborative research projects, including a global algorithm for AC-OPF optimization funded by the National Science Foundation (NSF). While no scientific awards are explicitly listed, his research portfolio demonstrates significant contributions to energy systems and quantum computing. His advisory work includes various projects on grid resilience, though formal student advisee names are not provided in the text. Grants and funding are associated with his collaborative efforts in developing scalable optimization algorithms for power systems. Research activities involve partnerships with institutions like IBMQ for quantum simulations and local case studies in Pittsburgh. He explores innovative solutions such as mobile charging stations for EVs and blockchain-based energy trading models, reflecting a commitment to both theoretical and applied advancements in energy engineering.

Edmond ChowView profile

Professor

• High-Performance Computing
• Scientific Computing
• Numerical Linear Algebra
• +7 more

Edmond Chow is a Professor and Associate Chair in the School of Computational Science and Engineering at Georgia Institute of Technology. His research focuses on numerical methods, high-performance computing, and scientific computing applications in quantum chemistry, molecular dynamics, and machine learning. He holds awards including the 2009 ACM Gordon Bell Prize and 2002 PECASE. He leads the Intel Parallel Computing Center, advancing computational chemistry algorithms for Intel architectures. Education: Ph.D., Computer Science (minor in Aerospace Engineering), University of Minnesota, 1997 Honors B.A.Sc., Systems Design Engineering, University of Waterloo, 1993 Research Interests: Parallel algorithms for sparse linear systems and preconditioning High-performance quantum chemistry simulations Asynchronous iterative methods for extreme-scale computing Kernel-based methods and hierarchical matrices for Gaussian processes Applications in biological macromolecule dynamics and machine learning Awards & Recognition: ACM Gordon Bell Prize (2009) PECASE (2002) SIAM Fellow (2021) SC23 Test of Time Award Advising & Grants: Advised 11+ PhD/MSc students in numerical methods and HPC Funded by NSF, DOE, DARPA, and Intel Developed software tools like H2Pack (hierarchical matrices), GTFock (quantum chemistry), and Simint (electron integrals) Labs & Teams: Leads the Intel Parallel Computing Center and collaborates with institutions like LLNL, Sandia, and Temple University on asynchronous computing and quantum chemistry projects.