Campuses:

machine learning

Wednesday, April 25, 2018 - 1:30pm - 2:00pm
Brian Reich (North Carolina State University)
Forensic analyses are often concerned with identifying the spatial source of biological residue. Using recent advances in high-throughput sequencing technologies, dust collected from nearly any object can be shown to harbor DNA fragments from thousands of bacteria and fungi species which may be informative of the source of the dust. We show that training collections of deep neural network classifiers on random Voronoi partitions of a spatial domain yields remarkably accurate geolocation predictions.
Saturday, September 16, 2017 - 11:20am - 11:50am
Yi-Hsiang Hsu (Harvard Medical School)
The fast moving of the cancer immunotherapy field has generated tremendous excitement regarding new therapeutic strategies and will likely change the paradigm of therapeutic interventions for cancer. Neoantigens, generated by tumor-specific DNA alterations that result in the formation of novel protein sequences and only in cancer cells, represent an optimal target for the immune system and make possible a new class of highly personalized vaccines with the potential for significant efficacy with reduced side effects.
Friday, September 15, 2017 - 9:00am - 10:00am
Michael Kosorok (University of North Carolina, Chapel Hill)
Estimating individualized treatment rules is a central task of personalized or precision medicine. In this presentation, we review several new developments in outcome weighted learning for identifying individualized treatment rules for two challenging settings: dose finding and treatment selection under right censoring. In the former, we develop an approach which involves the use of two different kernels; and in the later, we use random forests to address censoring before applying outcome weighted learning.
Monday, February 22, 2016 - 1:15pm - 2:00pm
Stephen Wright (University of Wisconsin, Madison)
We survey some developments in machine learning and data analysis,
focusing on those in which optimization is an important
component. Some of these have possible relevance for industrial and
energy applications, for example, constraints and covariances could be
learned from process data rather than specified a priori. Some
possibilities along these lines will be proposed.
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