Center for Causal Discovery Distinguished Lecture Series Presents Sergey Plis, PhD on May 21

Center for Causal Discovery

Distinguished Lecture in Causal Discovery

University of Pittsburgh, Carnegie Mellon University,

Pittsburgh Supercomputing Center and Yale University

http://www.ccd.pitt.edu

Sergey Plis, PhD, Director of Machine Learning, Mind Research Network, will deliver the Distinguished Lecture in Causal Discovery, In Search of a Common Scale for Causal Fusion in Neuroimaging,” at 11:00 am on Thursday, May 21, 2015, in the Giant Eagle Auditorium, A51 Baker Hall, Carnegie Mellon University.

Abstract: Cortical neurons form coherent functional networks that are surprisingly stable across subjects and conditions. Together these networks comprise a set of functional units of the brain. Understanding their interactions can lead to better understanding of brain's function and dysfunction due to disruption of the interaction structure. The most common way of assessing this structure are cross-correlation matrices (usually referred to as functional connectivity) but their drawbacks make us turn to modeling interactions via the directed graph of a Bayesian network (effective connectivity). Various brain imaging modalities contain different and arguably complementary information about interactions of functional network. Our goal is to bring together multimodal information to improve effective connectivity estimates.

This talk will show why unimodal structure learning may be dangerous, demonstrate a modality specific physical model of information fusion for connectivity search, and argue for a more general approach to causal fusion. In particular, we will focus on the problem of finding a common denominator for causal structures learned from time series at different time scales. I will demonstrate 1) a general theory which explains the effects of undersampling on apparent causal structure in terms of the true structure at the causal time scale; 2) a forward algorithm that computes a graph structure at any given undersampling rate; and 3) an inverse algorithm to compute all of the candidate graphs that could have generated the given undersampled structure.

Biography:  Dr. Sergey Plis is a Director of Machine Learning at the Mind Research Network. His research interests lie in developing novel and applying existing techniques and approaches to analyzing large scale datasets in multimodal brain imaging and other domains. He develops tools that fall within the fields of machine learning and data science. One key goal is to take advantage of the strengths of imaging modalities and infer structure and patterns that are hard to obtain non-invasively and/or that are unavailable for direct observation. In the long term this amounts to developing methods capable of revealing mechanisms used by the brain for forming task specific transient interaction networks and their cognition-inducing interactions via multimodal fusion at features and interaction levels. Ongoing work is focused on inferring multimodal probabilistic and causal descriptions of these function-induced networks based on fusion of fast and slow imaging modalities. This includes feature estimation via deep learning-based pattern recognition and learning causal graphical models.

Post Date: 
Thursday, May 14, 2015
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