Biostatistics Student Profiles
This is not a complete list of our PhD students (listing optional)
Andy Beck, BS, MS
Fine mapping of mutation rates across the genome
Marco Benedetti, BS, MS
Statistical methods for spatial and spatio-temporal data.
Rupam Bhattacharyya, BS, MS
Regression Modelling, Bayesian Statistics, Statistical Genetics, Precision Medicine
Jonathan Boss, BS, MS
Multi-pollutant modeling, non-linear interaction selection in the presence of many environmental contaminants, and statistical inference when exposure data are subject to multiple detection limits. Applied collaborations in environmental statistics ranging from amyotrophic lateral sclerosis, racial disparities in telomere length, and gestational duration in pregnant women
Irena Chen, B.A.
Bayesian methods for high dimensional/temporal data
Zhongsheng Chen, BS, MS
Statistical methods for rare variant analysis
Statistical methods for sequential multiple assignment randomized trial (SMART) in small samples. In particular,developing methods to estimate the response rates of treatments or dynamic treatment regimes in SMART when the outcome of interest is binary. Application of adaptive designs in SMART.
Elizabeth Chase, BSP.H., MS
Research interests still developing. Thus far, working on penalized regression and epidemiologic methods, with applications in cancer, HIV, women's health, and health disparities. Other interests include causal inference, missing data, neighborhood/network effects, and electronic health records.
Theresa Devasia, BS, MS
Cancer Research, Survival Analysis and Clinical Trials
Diptavo Dutta, B.Sc., M.Stat.
GWAS, Rare variants, pleiotropy, pathway-based association, variable selection
High dimensional inference
Allison Furgal, BS, MS, M.A.
Joint models for longitudinal and competing risks survival data, multivariate survival models, copula models
Cui Guo, BS, MS
Bayesian methods and imaging statistics
Boran Gao, MBBS, MPH, MS
Statistical Genetics with interest in variance component analysis using summary statistics.
Tian Gu, BS, MS
Health Data Science, Missing Data, Data Integration, Prediction Models
Sarah Hanks, B.A.
Development and application of statistical methods for the analysis of human genetic and genomic data.
Wei Hao, BS, MS
Emily Hector, BS., MS
Estimating equations, Composite likelihood, Generalized method of moments, Divide- and-conquer, Heterogeneous data integration, High-Dimensional data, Correlated data, Parallel computing.
Abhay Hukku, BS, MS
Development of statistical methodology to address problems in human genetics. Currently working on new methods for conducting gene-set based analyses.
Kim Hochstedler, BS
Clinical trials, survival analysis, cancer, mental illness
Alan Kwong, BS, MS
Statistical genetics, genetic association, next-generation sequencing
Pin Li, BS, MS
Clinical trials, predictive risk modeling, use of biomarkers to individualize and adapt treatment.
Lan Luo, BS, MS
Streaming Data Analytics and Real-time Regression Analysis.
Ying Ma, B.M., MS
Statistical Genetics, Single cell data analysis
Pedro Orozco del Pino, BS, MS
Generalizability of Genetic Risk Scores
Robert Peng, B.A., MS
Use of polygenic risk scores to predict a patient's genetic risk for various diseases
Adam Peterson, BS, MS
Spatial-Temporal Aggregated Predictors: Methodology and accompanying software (rstap)and Classifying Built Environment Exposure
Emily Roberts, B.A., MS
Survival analysis and causal inference including surrogate endpoints for clinical trials.
Stephen Salerno, BS, MS
Statistical methodology and its applications to clinical quality measure development, health policy, and global public health.
Lulu Shang, BS, MS
Yanyi Song, BS, MS
Bayesian method, mediation analysis, statistical genetics
Kelly Speth, BS, MS
Dynamic treatment regimes, observational data, machine learning, cancer biostatistics.
Yilun Sun, BS, MS
Robust Methods in Missing Data and Dynamic Treatment Regimes
Ming Tang, BS, MS
Missing Data Analysis, Dynamic Treatment Regimes
Nicole Wakim, BS, MS
Clinical trials, Alzheimer's research
Lili Wang, BS, M.S.
Survival analysis, recurrent events, mixed-effects models, and causal inference.
Wenjia Wang, BS, MS
Semiparametric and Joint Modeling of Survival in Cancer Screening Problems
Josh Weinstock, B.A., MS
WGS variant calling; Mendelian Randomization; clonal hematopoiesis
Whiteman, Andrew, BS, MS
Biostatistics; imaging statistics, computational methods
Wenbo Wu, B.A., M.A.
High-dimensional statistics, machine learning and optimization
Jingyue Xi, BS, MS
Statistical Genetics and Single Cell Analysis
Pranav Yajnik, BS, MS
Genetic Epidemiology, GWAS
Guangyu Yang, BS, MS
Estimation of the change points in the broken-stick model. Survival Analysis
Yuan Yang, BS, M.S.
Nonparametric regression, high dimensional data, statistical inference, variable selection, and survival analysis.
Non-parametric Bayesian Statistics and Bayesian Network Methodology.
Hengshi Yu, BS, MS
Causal inference and statistical learning. Strong interest in the interplay between stochastic modeling and semi-parametric statistics.
Ketian Yu, BS, M.S.
Youfei Yu, B.A., MS
Causal inference approach for censored data and data with multiple treatments.
Yuqi Zhai, BS, MS
Design and analysis of sample surveys, Bayesian inference and spatial statistics.
Gregory JM Zajac, BS, MS.
Statistical methods and computational algorithms for the analysis of human genetic data. Current projects include genome-wide association studies, contamination, linkage, and meta-analysis.
Zhang, David Daiwei, BS, MS
Machine learning, statistical computing, statistical genetics, Bayesian methods
Xutong Zhao, BS, MS
Zhangchen Zhao, BS, M.S.
Gene-based tests and efficient resampling methods
Yingchao Zhong, B.A., MS
Kidney transplantation registry data; survival analysis methods
Nina Zhou, BS, MS
Dynamic Treatment Regime, Missing Data and machine learning
Jiaqiang Zhu, BS, MS
Statistical Genetics, Differential Gene Expression Analysis, Differential Methylation Analysis
Yongwen Zhuang, BS, M.S.
Statistical methods in genetics, computational statistics, machine learning