"Learning Dynamics of Complex Systems from High-Dimensional Datasets" (This Week at the Statistics Seminar)

Attention conservation notice: Only of interest if (1) you care about statistics and complex systems, and (2) will be in Pittsburgh on Wednesday.

Sumanta Basu, "Learning Dynamics of Complex Systems from High-Dimensional Datasets"

Abstract: The problem of learning interrelationships among the components of large, complex systems from noisy, high-dimensional datasets is common in many areas of modern economic and biological sciences. Examples include macroeconomic policy making, financial risk management, gene regulatory network reconstruction and elucidating functional roles of epigenetic regulators driving cellular mechanisms. In addition to their inherent computational challenges, principles statistical analyses of these big data problems often face unique challenges emerging from temporal and cross-sectional dependence in the data and complex dynamics (heterogeneity, nonlinear and high-order interactions) among the system components.

In this talk, I will start with network Granger causality --- a framework for structure learning and forecasting of large dynamic systems from multivariate time series and panel datasets using regularized estimation of high-dimensional vector autoregressive models. I will discuss theoretical properties of the proposed estimates and demonstrate their advantages on a motivating application from financial econometrics --- system-wide risk monitoring of the U.S. financial sector before, during and after the crisis of 2007--2009. I will conclude with some of my ongoing works on learning nonlinear and potentially high-order interactions in high-dimensional, heterogeneous settings. I will introduce iterative Random Forest (iRF), a supervised learning algorithm based on randomized decision tree ensembles, that achieves predictive accuracy comparable to state-of-the-art learning machines and provides insight into high-order interaction relationships among features. I will demonstrate the usefulness of iRF on a motivating application from systems biology - learning epigenetic landscape of enhancer elements in Drosophila melanogaster from next generation sequencing datasets.

Time and place: 4--5 pm on Wednesday, 24 February 2016, place TBA

As always, the talk is free and open to the public.

Enigmas of Chance; Complexity