Published: April 10th, 2018

Category: Uncategorized

Date: May 17, 2018

Time: 8:30am- 11:30am

Course 1

Machine Learning, Artificial Intelligence, and Precision Medicine

  • Instructor: Haoda Fu, Ph.D., Research Advisor, Eli Lilly and Company
  • Abstract: This course will provide an overview of statistical machine learning, and artificial intelligence techniques with applications to the precision medicine, in particular to deriving optimal individualized treatment strategies for precision medicine. This short course will cover both treatment selection and treatment transition. The treatment selection framework is based on outcome weighted classification. We will cover logistic regression, support vector machine (SVM), robust SVM, and angle based classifiers for multi-category learning, and we will show how to modify these classification methods into outcome weighted learning algorithms for precision medicine. The second part of short course will also cover the treatment transition. We will provide an introduction on reinforcement learning techniques. Algorithms, including dynamic programming for Markov Decision Process, temporal difference learning, SARSA, Q-Learning algorithms, actor-critic methods, will be covered. We will discuss on how to use these methods for developing optimal treatment transition strategies. The techniques discussed will be demonstrated in R.

Course 2

Elastic Functional and Shape Data Analysis

  • Instructor: Anuj Srivastava, Professor, Department of Statistics, Florida State University
  • Abstract: Functional and shape data analysis are important research areas, due to their broad applications across many disciplines. An essential component in comparing functions and shapes is the registration of points across functional objects. Without proper registration the results are often inferior and difficult to interpret. The current practice in functional data analysis and shape communities is to treat registrationas a pre-processing step, using off-the-shelf alignment procedures, and follow it up with statistical analysis of the resulting data. In contrast, an elastic framework is a more comprehensive approach, where one solves for the registration and statistical inferences in a simultaneous fashion. The key idea here is to use metrics with appropriate invariance properties, to form objective functions for alignment and to develop statistical models involving functional data. While these elastic metrics are complicated in general, we have developed a family of square-root transformations that map these metrics into simpler Euclidean metrics, thus enabling more standard statistical procedures. Specifically, we have developed techniques for elastic functional PCA and elastic regression models involving functional variables. This tutorial will demonstrate these ideas using imaging data in neuroscience where anatomical structures can often be represented as functions (curves or surfaces) on intervals or unit spheres. Examples of curves include DTI fiber tracts and sulcal folds while examples of surfaces include subcortical structures (hippocampus, thalamus, putamen, etc). Statistical goals here include shape analysis and modeling of these structures and to use their shapes in medical diagnosis. As an extension, we will also cover shape analysis of 3D objects by considering shapes of their boundaries (surfaces). A prominent example of this kind of data is full body scans of humans, and we will discuss elastic shape analysis of human body shapes.

*Additional fees and registration is required for all workshops