RESEARCH COLLOQUIUM ON COMPUTATIONAL SOCIAL SCIENCE/DATA SCIENCES – Vadim Sokolov – Dimensionality Reduction for Agent Based Models

When:
September 20, 2019 @ 3:00 pm – 4:00 pm
2019-09-20T15:00:00-04:00
2019-09-20T16:00:00-04:00
Where:
CENTER FOR SOCIAL COMPLEXITY SUITE, 3RD FLOOR, RESEARCH HALL
Cost:
Free
Contact:
Karen Underwood
7039939298

Research Colloquium on Computational Social Science/Data Science

Vadim Sokolov
Assistant Professor
Department of Systems Engineering and Operations Research
George Mason University

Dimensionality Reduction for Agent Based Models

Friday, September 20, 3:00 p.m.
Center for Social Complexity Suite, 3rd Floor Research Hall

All are welcome to attend.

Abstract:

Bayesian algorithms such as Markov Chain Monte Carlo or Bayesian optimization can quickly become computationally prohibitive or even infeasible for high dimensional ABM problems. In many applications, however, the underlying dynamics of an ABM typically can be represented in a lower dimensional space. We will review existing linear and nonlinear dimensionality reduction methods, such as Laplacian eigenmaps and restricted Boltzmann machines. Further, we will present some new results for nonlinear dimensionality techniques based on deep learning models. We will demonstrate our approach in the context of Bayesian optimization algorithms applied to a transportation agent-based model. Finally, we discuss directions for future research.

Bio:

Vadim Sokolov is an assistant professor in the Systems Engineering and Operations Research Department at George Mason University. He works on building robust solutions for large scale complex system analysis, at the interface of simulation-based modeling and statistics. This involves, developing new methodologies that rely on deep learning, Bayesian analysis of time series data, design of computational experiments and development of open-source software that implements those methodologies. Inspired by an interest in urban systems he co-developed mobility simulator called Polaris that is currently used for large scale transportation networks analysis by both local and federal governments. Prior to joining GMU he was a principal computational scientist at Argonne National Laboratory, a fellow at the Computation Institute at the University of Chicago and lecturer at the Master of Science in Analytics program at the University of Chicago.