PHC 7090 – Advanced Biostatistical Methods I. Fall 2020-2024.
Level: Graduate. This is the first course of a two-course sequence providing students with advanced knowledge and mathematical details on statistical inference and linear models. Covers Frequentist and Bayesian estimation and inference. It also encom- passes estimation and inference for linear models and fitting of regression models.
PHC 6937 – Analysis of Multivariate Data. Fall 2018, 2020–2022.
Level: Graduate. Covers linear models methodology including simple and multiple regression and analysis of variance including factorial and block designs. The course covers regression for categorical data, random effects models for correlated data, and nonparametric and semiparametric regression.
PHC 6937 – Frontiers in Biostatistics. Spring 2018–2020.
Level: Graduate. Introduces Biostatistics Masters and Ph.D. students to current issues and methods in modern biostatistics research. Current faculty present selected topics from their current research.
PHC 6937 – Bayesian Biostatistical Methods. Fall 2019.
Level: Graduate. Equips students with an understanding of the basics of Bayesian statistics, with special emphasis on practical implementation.
STAT 9710 – Mathematical Statistics I. Fall 2011–2014.
Level: Graduate. Theory of estimation and tests of hypotheses including sufficiency, completeness and exponential families. Neyman-Pearson lemma, most powerful tests, similarity and invariance. Bayes and minimum variance unbiased estimates. Confi- dence intervals and ellipsoids.
STAT 9720 – Mathematical Statistics II. Spring 2012–2017.
Level: Graduate. Asymptotic distributions of maximum likelihood estimators, chi- square and likelihood ratio test statistics. EM algorithm, bootstrap, and introduction to generalized linear models.
STAT 4710/7710 – Introduction to Mathematical Statistics. Spring 2008– 2010, Spring 2012, Spring 2014, Spring 2017, Fall 2010, Fall 2013, Fall 2016, Fall 2017. Level: Graduate/Undergraduate. Introduction to theory of probability and statistics using concepts and methods of calculus.
STAT 4510/7510 – Applied Statistical Models I. Spring 2011, Spring 2013, Spring 2015, Fall 2011–2012, Fall 2014, Fall 2016.
Level: Graduate/Advanced Undergraduate. Introduction to applied linear models including regression (simple and multiple, subset selection, estimation and testing) and analysis of variance (fixed and random effects, multifactor models, contrasts, multiple testing).
STAT 4750/7750 – Introduction to Probability Theory. Spring 2009–2011, Fall 2009–2010, Fall 2017.
Level: Graduate/Advanced Undergraduate. Probability spaces; random variables and their distributions; repeated trials; probability limit theorems.
STAT 4410/7410 – Biostatistics. Fall 2009.
Level: Graduate/Advanced Undergraduate. Study of statistical techniques for the design and analysis of clinical trials, laboratory studies and epidemiology. Topics include randomization, power and sample size calculation, sequential monitoring, Carcinogenicity bioassay and case-cohort designs.
STAT 4830/7830 – Categorical Data Analysis. Fall 2008.
Level: Graduate/Advanced Undergraduate. Discrete distributions, frequency data, multinomial data, chi-square and likelihood ratio tests, logistic regression, log linear models, rates, relative risks, random effects, case studies.
BIO503 – Programming and Statistical Modeling in R (co-taught with Christopher Paciorek). Spring 2007.
BIO283 – Spatial Statistics for Health Research (co-taught with Louise Ryan, Yi Li, Christopher Paciorek). Fall 2004.
Shiqi Cui. Dissertation topic: Bayesian Mixture Models for High-Throughput Bioin- formatics Applications. Co-directed with Marco Ferreira. Graduated in December 2014. Working for Google LLC.
Chiyu Gu. Dissertation topic: Scalable Bayesian Nonparametric Learning for Biomed- ical Big Data. Graduated in May 2018. Working for Bayer Crop Science.
Chetkar Jha. Dissertation topic: Bayesian Nonparametric Analysis of Multivariate Unordered Categorical data. Graduated in May 2019. Assistant Professor at Ahmedabad University.
Dongyan Yan. Dissertation topic: Scalable Computational Algorithms and Massively Parallel Computing for Bayesian Mixture Models. Graduated in June 2019. Working for Elli Lilly and Company.
Archie Sachdeva. Dissertation topic: Bayesian Analytical Methods for Microbiome Data. Graduated in August 2022. Working for Bristol-Myers Squibb Company.