Ana F. Best, Ph.D., joined the Biostatistics Branch (BB) in 2015 as a postdoctoral fellow, under the mentorship of Philip Rosenberg, Ph.D. She received her B.Sc. in probability and statistics in 2010, and her Ph.D. in statistics from McGill University in 2015, under the supervision of Prof. David Wolfson. For her dissertation, Dr. Best investigated the theory and benefits of risk-set-sampling within prevalent cohort survival studies. Dr. Best’s current research interests include extensions of the Age-Period Cohort (APC) model; forecasting cancer incidence and survivorship; study design development in survival analysis, risk assessment, and screening protocols; and examination of individual and familial cancer risk for genetic disorders.
Hyoyoung Choo-Wosoba, Ph.D., joined the Biostatistics Branch (BB) as a postdoctoral fellow in August 2016. She earned her Ph.D. from the Department of Bioinformatics and Biostatistics at the University of Louisville, Kentucky, under the supervision of Dr. Somnath Datta. Dr. Choo-Wosoba‘s doctoral research focused on developing statistical methodology to analyze zero-inflated clustered count data. She is currently working under the mentorship of Paul Albert, Ph.D., senior investigator and Chief, BB, on a collaborative project with Bin Zhu, Ph.D., investigator, BB, concerning statistical approaches for copy number variation detection using next-generation sequencing data.
Lu Deng, Ph.D., joined the Biostatistics Branch (BB) as a postdoctoral fellow in September 2017. Dr. Deng received his Ph.D. (2017) in mathematical statistics from Nankai University of China, under the supervision of Drs. Zhaojun Wang and Changliang Zou. His Ph.D. dissertation research focused on statistical inference for high-dimensional data, including multiple hypothesis testing, variable selection, and sufficient dimension reduction. Currently, he is working with Kai Yu, Ph.D., senior investigator, BB, on developing statistical methodologies to address emerging challenges from current genetic and molecular epidemiology studies.
Andriy Derkach, Ph.D., joined the Biostatistics Branch (BB) as a postdoctoral fellow after receiving his Ph.D. from the Department of Statistical Sciences at the University of Toronto. For his doctoral dissertation, Dr. Derkach developed new methods for testing associations with groups of rare genetic variants, and extended the underlying theory to make general claims about the properties of score tests for association under response-dependent sampling. Dr. Derkach works with Joshua Sampson, Ph.D., investigator in BB, to identify biological mediators that link exposures with the risk of cancer.
Xing Hua, Ph.D., joined the Biostatistics Branch (BB) as a postdoctoral fellow in April 2013. Dr. Hua worked in the Department of Physiology and Cancer Center of the Medical College of Wisconsin (MCW) as a visiting scholar from 2011 and received his Ph.D. in statistics from the University of Science and Technology of China in 2012. His Ph.D. thesis focused on developing statistical methods and algorithms for analyzing cancer genome sequencing data, including calling somatic mutations and detecting driver genes. Dr. Hua is now working with Jianxin Shi, Ph.D., Investigator, BB, to develop methods for driver gene detection across cancer sites, detect mutations affecting survival, and perform statistical analysis of lung cancer tumor sequencing data.
Rebecca Landy, Ph.D., joined the Biostatistics Branch (BB) and Clinical Genetics Branch (CGB) as a postdoctoral fellow in February 2018. Dr. Landy earned her Ph.D. in epidemiology and statistics in 2012 from University College London, U.K., under the supervision of Prof. Rebecca Hardy. Her doctoral research focused on missing data in longitudinal studies. Prior to joining NCI, she worked as a statistician at the Centre for Cancer Prevention, Queen Mary University of London with Prof. Peter Sasieni, evaluating cervical cancer screening in England. Dr. Landy is working with Anil Chaturvedi, Ph.D., senior investigator, CGB, and Hormuzd Katki, Ph.D., senior investigator, BB, to understand the natural history of human papillomavirus-related oropharyngeal cancers, and the appropriate use of risk estimation methods when evaluating cancer screening.
Scientific Publications - Rebcca Landy
Marlena Maziarz, Ph.D. is a Postdoctoral Fellow in the Biostatistics Branch (BB) at the National Cancer Institute, Division of Cancer Epidemiology and Genetics. She received her M.Sc. in Computer Science (2003) from the University of Toronto, and her Ph.D. in Biostatistics (2015) from the University of Washington, Seattle, under the supervision of Prof. Yingye Zheng. For her dissertation, Dr. Maziarz worked on risk prediction and evaluation of predictions based on longitudinal biomarkers in cohort and two-phase studies. Currently, she is working on methodological and collaborative projects addressing questions arising in biased sampling, complex surveys, and analysis of microbiome data.
Ana Maria Ortega-Villa Ph.D., joined the Biostatistics Branch (BB) as a postdoctoral fellow in February 2017. She earned her Ph.D in statistics from Virginia Tech in December 2015, working with Dr. Inyoung Kim. Her doctoral research focused on the development of semiparametric varying coefficient models for matched case-crossover studies. Prior to joining NCI, Dr. Ortega-Villa was a postdoctoral fellow in the Biostatistics and Bioinformatics Branch of the Division of Intramural Population and Health Research of the Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD). While at NICHD, she focused on the development of predictors of gestational age geared towards under-resourced countries, as well as several collaboration projects on fetal growth. Dr. Ortega-Villa is currently working on several collaboration projects under the mentorship of Paul Albert, Ph.D., senior investigator and Chief of BB.
Yei Eun Shin, Ph.D., joined the Biostatistic Branch (BB) as a postdoctoral fellow in October 2017. Dr. Shin completed her Ph.D. in statistics from Texas A&M University, College Station, in August 2017. For her doctoral dissertation, she developed statistical research on covariate matching methods, monotone functional data analysis, and binary spatio-temporal data modeling. Dr. Shin is working with Mitchell H. Gail, M.D., Ph.D., senior investigator, BB, to enhance the survival model from the two-phase sampling design of a cohort.
Lingxiao Wang, M.S., joined the Biostatistics Branch (BB) as a predoctoral fellow in March 2017. She received her M.S. in survey methodology (statistics track) from the University of Maryland, College Park (UMCP), and an M.A in applied statistics from the University of California, Santa Barbara. She is now pursuing a Ph.D. degree in survey methodology at UMCP. Her Ph.D. dissertation research is being done under the NIH Graduate Partnership Program with BB mentors Hormuzd Katki, Ph.D., senior investigator, Barry Graubard, Ph.D., senior investigator, and her UMCP mentor Yan Li, Ph.D. Her research focuses on improving representativeness of non-probability epidemiological cohorts in the analysis, and developing statistical methods for analyzing health and genetic survey data.
Ho-Hsiang Wu, Ph.D., joined the Biostatistics Branch (BB) as a postdoctoral fellow in July 2016. He earned his Ph.D. (2016) from the Department of Statistics of the University of Missouri under the supervision of Dr. Marco Ferreira and Dr. Tieming Ji. Dr. Wu’s doctoral research focused on developing Bayesian methods and algorithms for analyzing next-generation sequencing data. In DCEG, he is working with Bin Zhu, Ph.D., investigator, BB, on statistical approaches for detecting driver genes in cancer development.
Han Zhang, Ph.D., joined the Biostatistics Branch (BB) as a visiting fellow in August 2011. Dr. Zhang received Ph.D. in statistics from the University of Science and Technology of China (USTC). His Ph.D. thesis focused on developing algorithms for haplotype analysis under pooling and individual design. He is now working with Kai Yu, Ph.D., Senior Investigator, BB, on the statistical approaches for rare variants association analysis and gene-gene interaction analysis in case-control studies.