Causal inference algorithms for mixed data and applications to Precision Medicine

Seminar Date: 
Seminar Time: 
11am - 12pm
Seminar Location: 
5607 Baum Boulevard, Room 407A
Takis Benos, PhD
Presenter's Institution: 
Computational and Systems Biology, University of Pittsburgh

The Benos’ group develops computational, machine learning methods to address important questions in medicine.   We are interested in identifying the factors that affect chronic disease onset and progression and cancer survival.  We also develop predictive methods and tools that can directly improve health.  To do so, we use probabilistic graphical models and other machine learning methods that can integrate and mine high-dimensional, multi-modal data.  In this presentation we will show some recent advances in the area of causal modeling over mixed data and their applications in diseases such as chronic obstructive pulmonary disease (COPD) and cancer.