جست‌وجوی استاد و پژوهشگر

Luo Xiaoمشاهده پروفایل

دانشیار

• Functional Data Analysis
• Nonparametric Regression
• Biostatistics
• +۵ مورد دیگر

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 Verrallمشاهده پروفایل

استاد

• Actuarial Science
• Actuarial Statistics
• Risk Management
• +۷ مورد دیگر

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 Powersمشاهده پروفایل

استادیار

• Sport Analytics
• Statistics
• Machine Learning
• +۵ مورد دیگر

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 Takمشاهده پروفایل

استادیار

• Astrostatistics
• Bayesian Inference
• Computational Statistics
• +۳ مورد دیگر

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 Crambمشاهده پروفایل

دانشیار

• Biostatistics
• Spatial Epidemiology
• Bayesian Modeling
• +۳ مورد دیگر

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 Baratiمشاهده پروفایل

استادیار

• Electric Power Systems Modeling
• Quantum Computing
• Optimization and Simulation
• +۸ مورد دیگر

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 Chowمشاهده پروفایل

استاد

• High-Performance Computing
• Scientific Computing
• Numerical Linear Algebra
• +۷ مورد دیگر

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.

Thordis Linda Thorarinsdottirمشاهده پروفایل

استاد

• Statistics
• Data Science
• Environmental Science
• +۵ مورد دیگر

Thordis Linda Thorarinsdottir is a Professor of Statistics and Data Science at the University of Oslo's Department of Mathematics, affiliated with the Faculty of Mathematics and Natural Sciences. She previously worked as a Chief Research Scientist and Research Leader for Climate and Environment at the Norwegian Computing Center (2006–2023). Her research focuses on developing stochastic models for environmental sciences, emphasizing uncertainty quantification and probabilistic prediction in climate, weather, and hydrology. Key areas include spatial and spatio-temporal modeling, Bayesian frameworks, and forecast evaluation. **Education**: PhD in Mathematical Statistics from Aarhus University (2006). **Research Interests**: Environmental applications in climate science, spatial modeling, probabilistic forecasting, and extreme event analysis. She collaborates with experts in meteorology, hydrology, and climate science to address data deficiencies and real-world challenges. **Projects**: Leads initiatives like CONFER (Climate Futures) and is part of the Integreat research group. Her work integrates statistical theory with environmental process understanding to enhance predictive accuracy and decision-making under uncertainty. **Awards**: Not explicitly listed, but her contributions to climate and environmental statistics are recognized internationally. **Grants & Labs**: Active in interdisciplinary collaborations, including the Norwegian Research Council-funded projects. Her team focuses on probabilistic modeling and ensemble forecasting techniques.

Mark Leungمشاهده پروفایل

استاد

• Business Forecasting
• Financial Markets
• Artificial Intelligence in Business
• +۵ مورد دیگر

Mark Leung is a Professor of Management Science and the Associate Dean for Undergraduate Studies at the University of Texas at San Antonio (UTSA), Carlos Alvarez College of Business, Department of Management Science & Statistics. He has been a faculty member at UTSA since 1999, establishing himself as a prominent researcher and educator in operations management and business analytics. University: University of Texas at San Antonio College: Carlos Alvarez College of Business Department: Department of Management Science & Statistics Position: Professor and Associate Dean for Undergraduate Studies Leung's research spans multiple domains at the intersection of operations research, finance, and artificial intelligence. His primary research interests include business forecasting and modeling, financial market and investment strategies, neural network and artificial intelligence techniques in business and data mining, and supply chain and operations management. His work demonstrates a consistent focus on applying advanced quantitative methods to solve complex business problems, particularly in financial engineering and operational optimization. His recent publications reveal a strong emphasis on machine learning applications for financial hedging, particularly in energy markets, where he has developed innovative cross-learning machine approaches that outperform traditional methods. Leung has also published extensively on supply chain optimization, auction bidder behavior, and production system management. Col. Jean Migliorino and Lt. Col. Philip Piccione Endowed Research Award (2001) University of Texas System Chancellor's Council Outstanding Teaching Award (2002, 2005) University of Texas System Regents' Outstanding Teaching Award (2013) Endowed 1969 Commemorative Award for Overall Faculty Excellence (2016) Undergraduate Research Faculty Mentorship Award (2018) As Associate Dean for Undergraduate Studies, Leung has played a significant role in shaping business education at UTSA. His teaching excellence has been consistently recognized through multiple awards including the All-Star Professor Award (2013-2016) and the Best Undergraduate Professor Award (2012). He teaches across all levels (undergraduate, graduate, and doctoral) in business forecasting, production and service operations, supply chain management, and applied business statistics. Leung's research program demonstrates strong industry relevance, particularly in financial engineering applications where his work on machine learning approaches for energy market hedging has provided practical solutions for risk management. His collaborations with researchers across multiple disciplines have resulted in a substantial publication record in high-quality journals.

Brian Reichمشاهده پروفایل

استاد

• Spatial Statistics
• Bayesian Methods
• Environmental Science
• +۶ مورد دیگر

Brian Reich is the Gertrude M Cox Distinguished Professor of Statistics at North Carolina State University. His research focuses on geospatial analytics, spatial statistics, and extreme value analysis, with applications to environmental science, climate change, and public health. He leads the Environmental Statistics Lab, developing computational methods for big spatial data and spatiotemporal modeling. Research Interests: Spatial and Bayesian statistical methods Extreme value analysis and machine learning applications Environmental applications in ecology, epidemiology, and climate science Awards: Gertrude M Cox Distinguished Professorship Grants & Programs: NSF Research Training Group (UQ4Life) NSF REU Site (DRUMS for undergraduates) Carolina Thyroid Cancer Study Labs/Teams: Environmental Statistics Lab Collaborations on marine conservation, wildfire management, and PFAS exposure studies

Bruce A. Craigمشاهده پروفایل

استاد

• Diagnostic Testing
• Bayesian Hierarchical Modeling
• Markov Chain Monte Carlo
• +۷ مورد دیگر

Bruce A. Craig is a Professor and Director of Purdue University's Statistical Consulting Service in the Department of Statistics. His roles include faculty mentorship and teaching online courses like STAT 582 and STAT 522. He holds a Ph.D. in Statistics from the University of Wisconsin-Madison (1996), an M.S. from the same institution (1991), and a B.S. in Mathematics and Economics from Washington University in St. Louis (1989). His research interests span diagnostic testing methodologies, Bayesian hierarchical modeling, protein structure determination, microarray analysis, statistical education, spatial statistics, and population dynamics. Craig has secured grants from NIH and NSF for projects such as antimicrobial susceptibility testing and protein structure inference. His work also includes collaborations on disease modeling, biodiversity, and nonlinear mixed models in agriculture. Over 20 students have completed their Ph.D. under his advisement, many now leading roles in academia and industry. Notable publications include advancements in statistical methods for pathogen resistance testing and protein backbone assignment. Craig’s research bridges theoretical statistics with applications in biomedicine, engineering, and environmental science.

• Bayesian Statistics
• Statistical Modeling
• Operations Research
• +۷ مورد دیگر

David Valentín Conesa Guillén is a Full Professor in the Department of Statistics and Operations Research within the Faculty of Mathematics at the University of Valencia. He is an active researcher and a key member of the Valencia Bayesian Research Group (VABAR), focusing on advanced statistical methodologies and their applications. His primary research interests encompass Bayesian Statistics , Statistical Modeling , Operations Research , Epidemiological Modeling , Spatial Statistics , and Ecological Statistics . His work bridges theoretical development with practical applications in public health, environmental science, and financial systems. The trends in his recent publications reveal a strong emphasis on developing and applying Bayesian hierarchical models to complex real-world problems. Key areas include dynamic forecasting of influenza outbreaks, modeling spatial distributions of ecological and bioclimatic data, correcting biases in ecological survival estimates, and analyzing efficiency in sectors like banking and higher education. His methodology frequently involves advanced computational techniques like the Integrated Nested Laplace Approximation (INLA). PhD in Statistics, University of Valencia (2000) Thesis: "Inferencia y predicción en colas con ingresos o servicios en grupos" Supervised by Dr. Carmen Armero Cervera David Conesa has supervised academic work, as indicated by his role as a thesis advisor. His research has been supported by collaborative projects, leading to publications in top-tier journals such as Bayesian Analysis , European Journal of Operational Research , and Journal of Agricultural, Biological, and Environmental Statistics . He has extensive collaborations with researchers across Spain and internationally. His research is conducted within the VABAR (Valencia Bayesian Research Group) , a collaborative team dedicated to Bayesian methodology and its applications. This group provides a platform for interdisciplinary projects and the training of future statisticians.

• Statistics
• Spatial Statistics
• Bayesian Inference
• +۱۰ مورد دیگر

Antonio Manuel López Quilez is a Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, Universitat de València, Spain. He is affiliated with several research groups, including POpE (Public Opinion and Elections), VABAR (Valencia Bayesian Research Group), and VIO-STRAT (Advanced research strategies in family and gender violence). His email is antonio.lopez@uv.es. Doctor by the Universitat de València (1997) Thesis: Modelos lineales generalizados espaciales Supervisor: Dr. Juan Ferrándiz Ferragud His research focuses on statistical methodology with applications in spatial statistics, Bayesian inference, public health, environmental science, and social sciences. Key areas include spatial modeling, disease mapping, electoral analysis, and Bayesian hierarchical models. He frequently applies his methods to public health surveillance, such as influenza outbreak detection, and environmental modeling, including acoustic mapping with barriers. His recent publications highlight a strong trend in Bayesian modeling, particularly using integrated nested Laplace approximations (INLA), spatial smoothing, and Dirichlet regression for compositional data. He has contributed to dynamic forecasting of infectious diseases and the statistical analysis of bioclimatic indices. His work bridges theoretical statistical development with practical applications in epidemiology and social policy. No scientific awards are mentioned in the provided text. There is no information available about student advisement or research grants. However, his extensive collaborative work suggests active participation in research projects. He is a member of multiple interdisciplinary research teams, including those focused on public opinion, Bayesian methods, and gender violence research, indicating a strong team-based research approach. He leads or contributes to research in advanced statistical strategies for social and health issues, particularly through the VIO-STRAT group, which focuses on family and gender violence. His methodological expertise supports applied research in public policy and epidemiological decision-making.

• Algorithms
• Machine Learning
• Game-Theoretic Learning
• +۳ مورد دیگر

Dr. Waqas Jamil is a Post Doctoral Research Assistant at Bournemouth University's Faculty of Science and Technology, focusing on algorithmic and game-theoretic learning. He bridges academia, industry, and research through contributions to machine learning, consulting for financial firms, and teaching roles. Research Interests: Algorithmic learning Game-theoretic approaches Online Bayesian regression Time series forecasting Publication Trends: His work emphasizes online learning frameworks, regularization techniques, and scalable algorithms for time series analysis. Key themes include competitive regression, Bayesian shrinkage, and distributed learning systems. Education: PhD in Competitive Regression (2020) Industry Collaboration: Provides algorithmic consulting for financial firms, integrating theoretical and practical applications of machine learning.

• Compositional Data Analysis
• Econometrics
• Statistical Methods in Economics
• +۸ مورد دیگر

Germà Coenders Gallart is a full Professor in the Department of Economics at the University of Girona, where he holds the Chair in Quantitative Methods for Economics and Business. He is affiliated with the Faculty of Economic and Business Sciences and actively contributes to research through the Research Group in Statistics, Econometrics and Health (GRECS), the consolidated Catalan research group COSDA (Compositional and Spatial Data Analysis), and the Campus of Food and Gastronomy. He earned his PhD in Management Sciences from Ramon Llull University in 1996. PhD in Management Sciences, Ramon Llull University (1996) His primary research focus is compositional data analysis (CoDa), a statistical methodology for analyzing parts of a whole, with wide applications in accounting, finance, marketing, tourism, and health. He has pioneered the use of CoDa in financial statement analysis, developing compositional structural equation models, spatiotemporal models, generalized linear models with compositional predictors, and models incorporating totals. His methodological innovations address key statistical issues in traditional financial ratio analysis. His recent publications (2023–2025) reflect a strong trend in applying CoDa to real-world economic and social challenges, including financial resilience during geopolitical crises (e.g., Ukraine-Russia war), post-pandemic recovery in tourism and fisheries, air pollution and child mental health, biodiversity accounting in apiculture, and systemic risk in the Eurozone. His work combines rigorous statistical modeling with practical relevance across multiple domains. Scientific recognition includes: 5 Sexennis (Research Quinquennia) from CNEAI h-index of 29 (Web of Science and Scopus), 44 (Google Scholar) Over 3000 citations (Web of Science) Founding member and former Secretary General of the Association for Compositional Data He has supervised 7 doctoral theses and collaborates extensively with researchers across disciplines. He has secured multiple research grants and leads a dedicated lab on financial statement analysis as compositional data. His teaching spans research methods, statistics, and econometrics across undergraduate, master’s, and doctoral programs at the University of Girona and other institutions including ESADE and the University of Ljubljana. He is a member of editorial boards for journals such as Metodoloski Zvezki , ReHuSo , and Tourism Analysis , and maintains a strong digital presence through ORCID, ResearchGate, Google Scholar, and other academic platforms.