Saptarshi Chakraborty

Saptarshi Chakraborty

Assistant Professor of Biostatistics

Teaching

COURSE TEACHING

  1. STA 642: Topics in Advanced Modeling (Graduate Level). Provides a PhD-level introduction to advanced modeling techniques useful for complex statistical problems beyond the classical linear models. Specific topics include models for binary response data such as probit and logit models, analysis of data with discrete ordered responses, models for count data, log-linear models for contingency tables, and mixed effects models, and Bayesian methods. Spring 2023.

  2. STA 545/EAS 506: Statistical Data Mining I (Graduate Level). Presents statistical models for data mining, inference and prediction. Topics covered: linear and logistic regression, shrinkage, lasso, partial least squares, tree-based methods, model assessment and selection, model inference and averaging, neural networks, and R for data mining. Department of Biostatistics, University at Buffalo, SUNY. Fall 2022.

  3. STA 521: Introduction to Theoretical Statistics I (Graduate Level). Provides the background in probability and distribution theory required for theoretical statistics. Topics covered: axioms of probability theory, independence, conditional probability, random variables, discrete and continuous probability distributions, functions of random variables, moment generating functions, laws of large numbers and central limit theorem. Department of Biostatistics, University at Buffalo, SUNY. Fall 2020.

  4. STA 522: Introduction to Theoretical Statistics II (Graduate Level). Introduces principles of statistical inference. Topics include classical methods of estimation, tests of significance, interval estimates, Neyman-Pearson theory of testing hypotheses, maximum likelihood estimation and Bayesian statistics. Department of Biostatistics, University at Buffalo, SUNY. Spring 2021, Spring 2022.

  5. STA 4321/STA 5325: Introduction to Probability/Fundamentals of Probability (Advanced Undergraduate Level). Introduces probability theory required for mathematical statistics. Topics covered: axioms of probability theory, independence, conditional probability, random variables, discrete and continuous probability distributions, moment generating functions, functions of random variables, and multivariate distributions. Department of Statistics, University of Florida. Fall 2017.

WORKSHOP TEACHING/INSTRUCTING

  1. Classification Methods for Data Mining. CTSI Workshop: BERD Data Mining in Health Sciences. Clinical and Translational Science Institute, University at Buffalo, SUNY. April 2022.

  2. Statistics in the Era of Massive Data. BERD Faculty Lecture. BERD Winter Institute for Biostatistics 2022. University at Buffalo, SUNY. January 2022.

  3. Introduction to Bayesian Statistics. BERD Winter Institute for Biostatistics 2021, 2022. University at Buffalo, SUNY. January 2021, 2022.

  4. An Introduction to Hidden Genome Modeling with R package hidgenclassifier. Virtual Workshop given at Department of Biostatistics & Epidemiology, Memorial Sloan-Kettering Cancer Center, New York. November 2020.