 |
Join us this August for four great courses that will sharpen your quantitative research skills and give you methods and tools to deepen your understanding in biostatistics and bioinformatics.
Participants are highly encouraged to enroll in multiple courses. Space for each course is limited, so register quickly to reserve your spot!
Academic credit is not provided, unless otherwise noted on the course description.
Bioinformatics: Big Data in Biomedical ResearchAugust 6 - 109am - 12pm |
||||
|
This course will explore statistical, bioinformatic, and computational methods and tools for analyzing big Omics data in biomedical research, including experimental design for Omics research, next-generation sequencing data analysis, and statistical and bioinformatic methods in high dimensional data. During this course, students will gain practical experience and skills to align RNA-seq reads to reference genome, quantify gene expression, perform differential expression analysis and acquire functional interpretation of results. |
||||
 |
Yu Shyr, Ph.D. is the Chair of the Department of Biostatistics at Vanderbilt University, and the Founding Director of the Center for Quantitative Sciences. He has more than 25 years of experience as a statistical consultant, providing experimental design and statistical analysis support for basic science research, translational research, clinical trials, epidemiology studies, and high-dimensional data studies. He has published more than 430 peer-reviewed papers, including many statistical methodology papers. |
 |
Qi Liu, PhD. is an Assistant professor of Biostatistics and full-time faculty in the Vanderbilt Center for Quantitative Sciences. Dr. Liu has more than 15 years of experience in the areas of bioinformatics and systems biology. Dr. Liu is proficient in various kinds of next-generation sequencing analysis, and is also an expert in bioinformatics methodology development and integrative omics analysis. |
|
Visit this link for more information and to register for this course. |
||||
Machine Learning in Python and TensorflowAugust 6 - 101pm - 4pm |
|||
|
This course will develop proficiency in the following: Data transformation and manipulation, regularized regression models, decision trees and random forests, Gaussian processes, gradient boosted trees, and neural networks. Participants should have some experience using high-level programming languages, such as R or Python. |
|||
 |
Chris Fonnesbeck, Ph.D. Chris Fonnesbeck is an Associate Professor in the Department of Biostatistics at the Vanderbilt University School of Medicine. He specializes in computational statistics, Bayesian methods, meta-analysis, and applied decision analysis. He originally hails from Vancouver, BC and received his Ph.D. from the University of Georgia. His research interests include
computational statistics, hierarchical modeling, Bayesian statistics, meta-analysis, epidemiology, decision analysis and adaptive decision-making, machine learning, statistical software, marine mammal ecology, and Sabermetrics. |
||
Visit this link for more information and to register for this course. |
|||
Data Science in Biomedical ResearchAugust 13 - 179am - 12pm |
|||
|
This course focuses on the use of computational biology tools for advanced, discovery-oriented data science in biomedical research. This CQS Summer Institute will have a biological applications emphasis. The central goal of this course is to teach students how to choose and apply tools with contrasting strengths to identify unexpected patterns and to test hypotheses in complex datasets. Drs. Irish and Lau will introduce the theme of data science and provide a conceptual background. Dr. Lau will lead two practical days focused on single cell RNA sequencing (scRNA-seq), feature selection, and identifying developmental progressions in single cell data. Dr. Irish will lead two practical days focused on implementing machine learning tools, longitudinal analysis, and cross-platform comparisons with an emphasis on single cell protein and phospho-protein data. Students will learn to apply concepts from machine learning and data science in basic and clinical research studies. |
|||
 |
Jonathan Irish, Ph.D. is an Assistant Professor in the Department of Cell & Developmental Biology (CDB) at Vanderbilt University School of Medicine. Current projects in the Irish lab aim to personalize therapies by probing millions of cells in tissue biopsies and then using machine learning computational tools to identify and target cells that behave abnormally. |  |
Ken Lau, Ph.D. is an Assistant Professor in the Department of Cell & Developmental Biology (CDB) at Vanderbilt University School of Medicine. The central goal of his research lab is to understand how the inflammatory microenvironment, the so-called “niche”, signal to epithelial cells to alter their phenotypes. |
Visit this link for more information and to register for this course. |
|||
Machine Learning & Statistics using RAugust 13 - 171pm - 4pm |
|
| This CQS Summer Institute course provides an introduction and overview of the most powerful and common machine learning methods and statistical inference techniques using R (The R Project for Statistical Computing: https://www.r-project.org). Upon completion of this course, participants will be able to select and apply off-the-shelf machine learning and statistical software tools to many different types of analytical tasks. | |
 |
Matt Shotwell, Ph.D. is an Associate Professor in the Department of Biostatistics at Vanderbilt University Medical Center. He currently teaches a graduate-level course in Statistical Learning, which approaches Maching Learning topics from an applied statistics perspective using R (http://www.r-project.org). Dr. Shotwell is an expert in quantitative research methods, including design of experiments, nonlinear modeling, and machine learning. Dr. Shotwell has autored more than 70 peer-reviewed publications and and has served as a principal investigator or co-investigator for more than 15 extramurally funded research projects. |
Visit this link for more information and to register for this course. |
|