جست‌وجوی استاد و پژوهشگر

Xiaofeng Zhuمشاهده پروفایل

استاد

• Genetic Epidemiology
• Statistical Genetics
• Genetic Association Analysis
• +۵ مورد دیگر

Xiaofeng Zhu, PhD is a Professor in the Department of Population and Quantitative Health Sciences at Case Western Reserve University School of Medicine. He also serves as faculty at the Cleveland Institute for Computational Biology. His research focuses on developing statistical methods and computational tools for analyzing genetic and genomic data, particularly in the context of association analysis, admixture mapping, and rare variant identification. He leads the Continental Origins and Genetic Epidemiology Network (COGENT) consortium and has identified multiple genetic loci associated with blood pressure, sleep disorders, and Alzheimer's disease. Education: Ph.D. in Biostatistics, Epidemiology and Biostatistics, Case Western Reserve University (1998) M.S. in Statistics, University of Cincinnati (1994) M.S. and B.Sc. in Mathematics, Peking University (1989, 1986) Research Interests: Dr. Zhu's work emphasizes statistical genetics, including the development of methods to control population stratification, admixture mapping in admixed populations, and cross-phenotype association analyses. His lab integrates deep learning and bioinformatics to study complex traits such as cardiovascular disease, obesity, and sleep disorders. Recent efforts include analyzing whole-genome sequencing data to uncover rare variants influencing phenotypic variation. Publications Trends: His recent work spans Mendelian randomization methods (e.g., MRBEE), gene-sleep interaction studies, and multi-ancestry genomic analyses. Key themes include causal inference, pleiotropy analysis, and leveraging diverse genetic datasets to improve polygenic risk scores. Awards & Memberships: Fellow of the Royal Statistical Society Member, American Society of Human Genetics Member, International Genetic Epidemiology Society Advising & Grants: He has mentored 3 Master’s students, 9/10 PhD graduates, and 11/12 postdoctoral researchers. Notable mentees hold positions at Harvard, Mayo Clinic, and NIH. Active grants include studies on sleep health metrics, lipid loci discovery, and cardiovascular disease genetics. Labs & Teams: Leads COGENT consortium and collaborates with the NHLBI Trans-Omics for Precision Medicine (TOPMed) program. His lab develops software tools for ancestry inference, rare variant analysis, and epigenetic association studies.

Kylie Gorneyمشاهده پروفایل

استادیار

• Test Security
• Item Response Theory
• Computerized Adaptive Testing
• +۳ مورد دیگر

Kylie Gorney is an Assistant Professor of Measurement and Quantitative Methods in the Department of Counseling, Educational Psychology and Special Education at Michigan State University (MSU). She holds a Ph.D. in Educational Psychology (Quantitative Methods) from the University of Wisconsin-Madison and served as the 2022–23 Harold Gulliksen Psychometric Research Fellow at Educational Testing Service. Her research focuses on test security, item response theory, and computerized adaptive testing (CAT), with publications in leading journals such as British Journal of Mathematical and Statistical Psychology and Psychometrika . Dr. Gorney’s work addresses challenges in maintaining testing integrity, including detecting cheating through methods like answer similarity analysis and response time analysis. She has developed an R package called aberrance for automated detection of aberrant test-taker behavior. Her contributions span both theoretical advancements in psychometrics and practical applications in secure testing environments. Her recent publications (2022–2025) emphasize innovations in CAT algorithms, person-fit statistics, and the integration of response-time data into security frameworks. These studies highlight advancements in detecting item compromise, preknowledge, and test speededness while improving subscore interpretation for polytomous items. Dr. Gorney is affiliated with MSU’s College of Education and maintains an active research agenda bridging statistical theory with real-world testing scenarios. She is accessible via email at kgorney@msu.edu .

Andrew B. Nobelمشاهده پروفایل

استاد

• Statistical Genomics
• Machine Learning
• Network Analysis
• +۳ مورد دیگر

Andrew B. Nobel is the Robert Paul Ziff Distinguished Professor of Statistics and Operations Research and Professor of Biostatistics at the University of North Carolina at Chapel Hill (UNC). He holds affiliations with the Lineberger Comprehensive Cancer Center and the Computational Genomics Program. His research focuses on statistical genomics, machine learning, network analysis, and inference from dynamical systems. Nobel has collaborated extensively with researchers in Biology, Computer Science, Genetics, and Mathematics. Dr. Nobel earned his PhD in Electrical Engineering from Stanford University (1992), an MS from Stanford (1988), a Certificate of Study in Mathematics from Cambridge University (1986), and a BS in Electrical Engineering from Cornell University (1985). He has taught courses ranging from undergraduate discrete mathematics to advanced graduate-level theoretical statistics and machine learning. His research interests include developing methodologies for analyzing complex biological and network data, with applications to cancer genomics, systems biology, and medical informatics. Recent work emphasizes optimal transport methods for network analysis and statistical approaches for genomic data integration. Awards: Elected Fellow of the Institute of Mathematical Statistics (2008) National Science Foundation CAREER Grant (1995) Beckman Institute Fellow (1992–1995) Churchill Scholar (1985–1986) Dr. Nobel serves on the editorial board of the Journal of the Royal Statistical Society, Series B and has previously contributed to the Annals of Statistics and IEEE Transactions on Information Theory . His work bridges theoretical statistical foundations with practical applications in public health and biomedical research.

Liudas Giraitisمشاهده پروفایل

استاد

• Econometrics
• Time Series Analysis
• Quantitative Methods
• +۳ مورد دیگر

Liudas Giraitis is a Professor of Econometrics at Queen Mary University of London, affiliated with the School of Economics and Finance. He holds a PhD from Vilnius University and has extensive research experience at Heidelberg University, Boston University, and the London School of Economics. His research focuses on econometrics and quantitative methods, emphasizing long memory processes, ARCH models, semiparametric inference, and time-varying random coefficient models. He has published extensively in leading statistical and econometric journals. His work bridges econometrics, statistics, and probability theory, with a strong emphasis on time series analysis and asymptotic theory. Recent research explores forecasting under ongoing structural change, heteroscedasticity testing, and dynamic stochastic general equilibrium (DSGE) models with financial frictions. Education: PhD in Econometrics from Vilnius University Key Research Themes: Long memory processes, ARCH/GARCH models, time-varying models, nonstationary time series, and asymptotic theory Affiliations: Queen Mary University of London, School of Economics and Finance Research Interests: Dr. Giraitis’s work addresses theoretical and applied challenges in econometric modeling, including robust inference for time series with general heterogeneity, cyclical time series analysis, and the development of asymptotic frameworks for dependent data. His contributions span both foundational theory and practical applications in finance and macroeconomics. Publications: Over 50 peer-reviewed articles in journals such as Journal of Econometrics , Annals of Statistics , and Journal of Time Series Analysis . Key works include the monograph Large Sample Inference for Long Memory Processes (2012) and recent studies on time-varying volatility and structural change. Lab/Team: Active in the School of Economics and Finance’s research group on econometric theory and applied time series analysis.

Arnab Maityمشاهده پروفایل

دانشیار

• Functional Data Analysis
• Kernel Machine Regression
• Semiparametric Regression
• +۵ مورد دیگر

Arnab Maity is an Associate Professor in the Department of Statistics at North Carolina State University (NC State). His research focuses on functional data analysis, kernel machine regression, and semiparametric methods with applications in environmental epidemiology and epigenetics. He holds a Ph.D. in Statistics from Texas A&M University (2008), an M.S. from the same institution (2005), and a B.Stat. (Honors) from the Indian Statistical Institute (2003). Education: Ph.D. in Statistics, Texas A&M University, 2008 M.S. in Statistics, Texas A&M University, 2005 B.Stat. (Honors), Indian Statistical Institute, 2003 Research interests span functional data analysis, kernel methods, semiparametric inference, and their applications in genomic studies, environmental health, and epigenetics. He has contributed to statistical tools for analyzing high-dimensional gene-environment interactions and longitudinal data. Recent work includes advancements in robust kernel association testing and functional concurrent models. His publications reflect interdisciplinary collaboration, addressing challenges in genetic epidemiology, biostatistics, and environmental health. Notable grants include NIH-funded projects on imprint regulatory regions in childhood obesity and functional data integration in genomics. Awards include the ASA Noether Young Researcher Award (2014) and the Cavell Brownie Mentoring Award (2020). He has advised十余名博士生 and served on numerous editorial and review boards. Active in academic service, he chairs committees and organizes seminars on functional data analysis and statistical genetics.

Erin Schliepمشاهده پروفایل

دانشیار

• Environmental Statistics
• Bayesian Statistics
• Spatial Statistics
• +۳ مورد دیگر

Erin Schliep is an Associate Professor in the Department of Statistics at North Carolina State University (NCSU), with a joint appointment as a Research Associate at Duke University. She holds a Ph.D. in Statistics from Colorado State University (2013). Her research focuses on Bayesian statistics, spatiotemporal modeling, and environmental applications, including ecological dynamics and sports analytics. Her work addresses challenges in climate change impacts, species distribution modeling, and marine conservation. Key research areas include spatiotemporal analysis of ecological systems, data fusion methods for environmental monitoring, and causal inference in spatial contexts. She has contributed to studies on lake productivity responses to climate, whale calling patterns, and NBA player performance metrics. Recent projects include NSF-funded work on modeling and uncertainty quantification in life sciences. Her publications span high-impact journals like JASA, PNAS, and Ecology, addressing topics such as marine mammal abundance estimation, algal biomass changes in lakes, and spatial causal inference for marine protected areas. She advises students and collaborates widely, exemplified by her role in the ContLimno team analyzing freshwater ecosystems. Erin’s expertise extends to methodological advancements, including hierarchical modeling, Hawkes processes, and machine learning integration. She emphasizes interdisciplinary applications, bridging statistics with ecology, environmental science, and sports analytics.

Giuseppe Vinciمشاهده پروفایل

استادیار

• Astrostatistics
• Bayesian Statistics
• Computational Neuroscience
• +۸ مورد دیگر

Giuseppe Vinci is an Assistant Professor at the Department of Applied and Computational Mathematics and Statistics (ACMS) at the University of Notre Dame, within the College of Science. His research focuses on probabilistic graphical models, particularly in neuroscience and genomics applications. He holds a Ph.D. in Statistics from Carnegie Mellon University (2017), and completed postdoctoral research and lecturing at Rice University (2017–2020). Vinci’s expertise spans astrostatistics, forensic science, and geometric data analysis. Education: Ph.D. in Statistics, Carnegie Mellon University (2017) M.Sc. in Statistics, Carnegie Mellon University (2013) M.Sc. in Economics and Social Sciences, Bocconi University (2012) B.Sc. in Economics, University of Catania (2009) Research Interests: High-dimensional statistical theory of graphical models, matrix completion, astrostatistics, neuroscience, genomics, forensic science, and geometric data analysis. His work addresses challenges in neuronal functional connectivity, genomic networks, and climate science. Awards: NeuroNex Postdoctoral Trainee (NSF) Rice Academy Postdoctoral Fellow Three-minute thesis competition (Top-10, Carnegie Mellon University) Advising & Grants: Vinci mentors multiple Ph.D., MSc, and undergraduate students in projects spanning forensic statistics, genomics, and astrostatistics. He has secured funding for undergraduate research programs and participated in NIH-funded interdisciplinary training. Labs/Teams: Involved in collaborative projects with institutions like Baylor College of Medicine and Rice University, focusing on neurotheory and statistical methods in neuroscience.

• Non-parametric statistics
• High-dimensional statistics
• Bioinformatics
• +۴ مورد دیگر

Professor Jian Zhang is a Professor of Statistics at the University of Kent's School of Mathematics, Statistics and Actuarial Science. His research focuses on non-parametric and high-dimensional statistics, bioinformatics, computational biology, statistical genetics, neuroimaging methods, and Bayesian modeling. He has advised students including Jie Li and Tong Wang. His work spans theoretical advancements and applied methodologies across diverse fields such as genomics, neuroimaging, and biomedical data analysis. Publications highlight contributions to Bayesian inference, neuroimaging techniques, and statistical genetics. Notable collaborations include studies on mixture models for genetic association analysis and beamforming methods for functional connectivity. He holds an ORCID iD and is based at Canterbury Campus, University of Kent.

Douglas Allaireمشاهده پروفایل

دانشیار

• Multidisciplinary design optimization
• Bayesian optimization
• Multifidelity methods
• +۴ مورد دیگر

Douglas Allaire is an Associate Professor and Sallie and Don Davis '61 Faculty Fellow in the J. Mike Walker ’66 Department of Mechanical Engineering at Texas A&M University, part of the College of Engineering. He leads the Computational Design Laboratory, focusing on computational methods for complex engineered systems. His research spans multidisciplinary design optimization, Bayesian optimization, machine learning, and materials design. Education: Ph.D., Aerospace Engineering, Massachusetts Institute of Technology (2009) M.S., Aerospace Engineering, Massachusetts Institute of Technology (2006) B.S., Aerospace Engineering, Massachusetts Institute of Technology (2004) Research Interests: Bayesian optimization and uncertainty quantification Materials design using machine learning Autonomous experimentation and data fusion Predictive analytics for engineering systems Recent Trends in Publications: His work emphasizes integrating Bayesian methods with materials discovery, autonomous systems, and high-fidelity modeling. Key themes include optimizing multifidelity systems, inverse microstructure design, and real-time decision frameworks. Awards: ASEM Fellow (2024) AIAA Associate Fellow (2023) ASME Young Engineer Award (2018) Advising & Grants: Advised students like Jaylen James (Ph.D. 2022) and Danial Khatamsaz. Active in securing grants for computational design and materials research. Collaborates with institutions like the American Institute of Aeronautics and Astronautics. Labs & Teams: Directs the Computational Design Laboratory, part of the Engineering Systems Design Group. Engages in interdisciplinary projects with Texas A&M’s Multidisciplinary Engineering program.

Daniel Kowalمشاهده پروفایل

دانشیار

• Bayesian Statistics
• Environmental Health and Policy
• Public Health
• +۹ مورد دیگر

Daniel Kowal is an Associate Professor in the Department of Statistics and Data Science at Cornell University, effective July 2024. Previously, he served as the Dobelman Chair Assistant Professor at Rice University. His research focuses on Bayesian methodology for complex dependent data, including functional, time series, and spatial datasets, with applications in environmental health, epidemiology, finance, and astronomy. He develops scalable algorithms for high-dimensional data and interpretable uncertainty quantification. His work has been recognized with the Blackwell-Rosenbluth Award (2021) and ARO Young Investigator Award (2020). Education: Ph.D. in Statistics (Cornell University), M.S. in Statistics (Cornell University), B.A. in Mathematics (Washington University in St. Louis). Research interests include Bayesian models for prediction/inference, decision theory, discrete data analysis, and scalable approximations. Key areas of application: environmental health policy, wearable devices, economics, biomedical engineering, and astronomy. Recent grants include NSF-funded projects on adaptive dependent data models and Army Research Office initiatives on Bayesian approximations. He has supervised multiple Ph.D. students, including Yunan Gao, Thomas Sun, and Brian King. His software contributions include R packages like countSTAR, SeBR, and lmabc for Bayesian regression and data synthesis. Awards include Lindley Prize Honorable Mention (2024), Arnold Zellner Thesis Award (2018), and numerous student paper awards from ASA sections.

Sze Ming Leeمشاهده پروفایل

پژوهشگر

• Social Statistics
• Large-Scale Data Analysis
• Latent Variable Modelling
• +۳ مورد دیگر

Sze Ming Lee is a Researcher in the Department of Statistics at the London School of Economics and Political Science (LSE). His research focuses on Social Statistics, with expertise in large-scale data analysis, latent variable modelling, survival analysis, and quantile regression. Lee holds a BSc in Mathematics, an MPhil in Risk Management Science, and a postgraduate diploma in Education, all from the Chinese University of Hong Kong. His academic supervisors are Dr. Yunxiao Chen and Professor Fiona Steele. His research interests emphasize methodological advancements in statistical modelling, particularly in handling high-dimensional data and latent variables. Recent publications (2023-2025) highlight contributions to quantile regression, factor analysis stability, and longitudinal data methodologies. Lee’s work bridges theoretical statistics with practical applications in social sciences and biostatistics. No scientific awards or grants are explicitly mentioned in the provided texts. He is affiliated with LSE’s Social Statistics research group and contributes to the department’s focus on data-driven social science research.

• Multivariate Survival Analysis
• Empirical Processes Theory
• Longitudinal Data Analysis
• +۳ مورد دیگر

Changyong Feng, PhD, is a Professor of Biostatistics and Computational Biology, Anesthesiology, and Dentistry at the University of Rochester Medical Center. He serves as Co-Director of the Department of Dentistry and holds a faculty position in the Department of Biostatistics and Computational Biology. He earned his PhD in Statistics from the University of Rochester in 2002. Dr. Feng’s research focuses on multivariate survival analysis, empirical processes theory, longitudinal data analysis, and statistical methods in epidemiology and clinical trials. His work spans interdisciplinary collaborations, including studies on anticoagulant efficacy in cardiovascular surgery, microbiome data analysis, and behavioral predictors of oral health. He has contributed to clinical trials, anesthesiology practices, and veterinary pharmacology. His expertise includes developing statistical models for complex biomedical data. Dr. Feng is affiliated with multiple departments at URMC, reflecting his role in bridging statistical methodologies with clinical and translational research. His publications address diverse topics from drug dosing optimization to influenza antibody dynamics. His research underscores the application of advanced statistical techniques to real-world medical challenges.

Yu Chengمشاهده پروفایل

استاد

• Dynamic Treatment Regimes
• SMART (Sequential Multiple Assignment Randomized Trials)
• Causal Inference
• +۶ مورد دیگر

Yu Cheng is a Professor and Chair of the Department of Statistics at the University of Pittsburgh, affiliated with the Dietrich School. She holds secondary appointments in Biostatistics and Psychiatry. Her research focuses on dynamic treatment regimes, SMART trials, causal inference, and multiple endpoints analysis. She leads a PCORI-funded method grant on SMART studies and co-investigates projects on cardiovascular disease and maternal/child health outcomes. Her awards include ASA Fellow (2024) and Pittsburgh Chapter Statistician of the Year (2020). Dr. Cheng earned her PhD in Statistics from the University of Wisconsin-Madison (2006), following MS (National University of Singapore, 2001) and BS (University of Science and Technology of China, 1999) degrees. She has held leadership roles including ASA LiDS Section Treasurer (2021–2023), Department Chair (2014–present), and Director of Graduate Studies (2016). Her research integrates methodological innovation with clinical collaboration, addressing topics like HIV, smoking cessation, lupus, depression, and cystic fibrosis. Her 150+ publications span journals like Biometrics , Journal of the American Statistical Association , and Biostatistics . Teaching includes courses in Survival Analysis, Probability, and Statistical Consulting. She actively mentors students through supervised consulting programs.

Jay Gottfriedمشاهده پروفایل

استاد

• Human Olfactory Neuroscience
• Cognitive Neuroscience
• Behavioral Neuroscience
• +۳ مورد دیگر

Jay Gottfried is the Arthur H. Rubenstein University Professor at the University of Pennsylvania's Perelman School of Medicine. His research focuses on human olfactory neuroscience, investigating how odor perception is processed in limbic circuits related to emotion, learning, and memory. He employs advanced techniques like functional MRI (fMRI) and intracranial EEG to study olfactory systems, with recent work exploring olfactory navigation using grid-like neural representations and interactions between sensory cortices. Education: A.B. in Molecular Biology from Princeton University; M.D. and Ph.D. in Physiology and Neuroscience from NYU School of Medicine. Postdoctoral training in olfactory neuroscience at University College London. Research Interests include odor coding, oscillations in the piriform cortex, and translational studies linking olfactory dysfunction to neurodegenerative diseases. Notable projects involve odor categorization, fear learning, and sleep-dependent memory consolidation. His lab bridges human and rodent studies to elucidate olfactory mechanisms. Key findings include temporal segregation of nostril odor signals, grid-cell-like representations in olfactory navigation, and the role of piriform cortex in integrating sensory information. He collaborates on developing high-resolution imaging and EEG techniques to map olfactory circuits.

Bin Pengمشاهده پروفایل

استاد

• Econometrics
• Panel Data Analysis
• Time Series Analysis
• +۳ مورد دیگر

Bin Peng is a Professor in the Department of Econometrics and Business Statistics at Monash University. His research focuses on developing novel econometric models and methods, particularly in panel data analysis, time series econometrics, and climate data modeling. He holds a PhD in Econometrics from Monash University (2013) under Professors Giovanni Forchini and Don Poskitt, preceded by a BSc in Mathematics from Nanjing University (2007). His work addresses structural changes in factor models, time-varying parameters in vector error-correction frameworks, and productivity convergence in manufacturing sectors. Key contributions include nonparametric panel models for climate data and methodologies for handling interactive effects in panel data with general factors. Peng has received multiple Dean’s Awards, including the 2021 Early Career Research Excellence Award, 2023 Commendation for Excellence, and 2024 Researcher of the Year. He leads a 2021–2025 project on modeling time trends in panel data, funded by Monash University. His recent articles (2021–2025) emphasize methodological advancements in econometric theory, applied to climate science, economic growth, and macroeconomic policy.