Systems Science Friday Noon Seminar Series

How are Data Science and Systems Science Connected?

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Date

2-15-2018

Abstract

Data Science is a relatively new interdisciplinary field, taking concepts from statistics and machine learning to produce predictive models. However, Systems Science concepts (such as feature-feature interactions and dynamics) have been largely underutilized in Data Science. In this talk, I'd like to start a discussion of specific ways that Systems Science can inform Data Science. I will start with examples of network analysis in my research that have led to better predictive models, and end with a discussion about the interpretability of black box predictors such as neural networks. I believe that Systems Science approaches can enhance Data Science by providing a deeper understanding of interactions between features and interpretability.

Biographical Information

I am an Assistant Professor in the Division of Bioinformatics and Computational Biology in the Department of Medical Informatics and Clinical Epidemiology at OHSU (BCB/DMICE). My research focus is on the Systems Biology of Complex Diseases, especially within cancer. I use integrative modeling approaches (such as network analysis and graphical models) across OMICs types to achieve this. I am also an active participant in the Portland Data Science community, especially the R programming community. More information at http://laderast.github.io/

Subjects

Big data, System theory, System analysis, Neural networks, Machine learning

Disciplines

Computer Sciences | Data Science | Medicine and Health Sciences

Persistent Identifier

https://archives.pdx.edu/ds/psu/33261

How are Data Science and Systems Science Connected?

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