Center for Causal Discovery
Distinguished Lecture in Causal Discovery
University of Pittsburgh, Carnegie Mellon University,
Pittsburgh Supercomputing Center and Yale University

sachsKaren Sachs, PhD, Research Scientist in the School of Medicine, Stanford University, Causal Learning in Signaling Networks,” at 11:00 am on Thursday, December 17, 2015, in Rooms 407A/B BAUM, 5607 Baum Blvd., The Offices at Baum.

Abstract: Signaling networks are crucial control circuits that determine cellular behavior.   Probabilistic graphical models can be used to elucidate signaling regulatory interactions, as we initially demonstrated using single cell proteomics data, a data-rich domain.  Despite the large number of available data points in this domain, a number of challenges remain for causal inference, pertaining to the representation of distributions, the underlying dynamics of the system, variable noise and other issues.  In this talk, I will discuss these challenges and some methods to overcome them.  I will also discuss applications for characterizing networks in health and disease.

Biography:  Dr. Karen Sachs is a research scientist at the Stanford School of Medicine.  She did her Ph.D. under the supervision of Dr. Doug Lauffenburger in the Bioengineering department at MIT, during which she introduced the idea of single cell data as potent data for causal inference.  She has spent her career working on single cell data, focusing on causal inference algorithms, as well as other computational approaches, mostly from machine learning.  She came to the Stanford University School of Medicine as a Leukemia and Lymphoma Society postdoctoral fellow, working under the supervision of Dr. Garry Nolan, a world leader in single cell proteomics.  Her application areas include cancer, especially blood cancers, drug response, and immunology.

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