My Personal Website at University of Michigan

“Changing the world, one site at a time...”

Adaptive Design for Personalizing Treatment
This line of research will use active learning and budgeted learning methods to efficiently sample patients with different biomarker profiles or belonging to different risk categories, with the goal of developing reliable decision rules for personalizing patient care.

Budgeted Learning
In some machine learning applications, the learning algorithms have access to the labels of the training data for free but have to pay to "see" the attribute values of those data. This problem is, in a sense, a dual to active learning. Budgeted learning algorithms attempt to intelligently choose the attributes to purchase in order to learn well with as few purchased attributes as possible.


Active Learning
In some machine learning applications, the learning algorithms are given abundant amounts of unlabeled data with an oracle that is capable of labeling a relatively small number of these examples to be used in supervised training. Active learning algorithms attempt to intelligently choose the examples to label in order to learn well with as few labeled examples as possible.


Receiver Operating Characteristic (ROC) Analysis
Receiver Operating Characteristic (ROC) analysis is an alternative means of measuring classifier peroformance. We study mechanisms of adjusting multi-class classifiers to improve performance (with respect to its ROC hypersurface) when faced with nonuniform misclassification costs.