Dissertation Talk: Alternate Representations for Scalable Analysis and Control of Heterogeneous Time Series

Presentation | November 6 | 6-8 p.m. | 405 Soda Hall

 Francois Belletti, UC Berkeley

 Electrical Engineering and Computer Sciences (EECS)

A plethora of algorithms and theories developed in the field of Machine Learning enable better identification of system dynamics and extensive control of the corresponding systems. However, the vast majority of research focuses on problems dealing with homogenous observation data sets or control environment.

Such a setting is not representative of the actual way data sets are collected and the problems that present themselves to practitioners.

The present work delves into a more realistic setting where a unified representation of the data or control problem of interest is not available. We deal with a collection of heterogeneous sub-parts that relate one to another but do not naturally present themselves to practitioners in a homogenous fashion.

Our main objective is to design methods that are readily applicable to heterogenous data sets and control problems in the distributed setting.

The applications entailed in our numerical experiments span the fields of quantitative finance, macroscopic traffic modeling, Mobility-as-a-Service, electrical load balancing, and optimal ramp metering for freeways.

The alternate representations we develop are statistically efficient, scale naturally, and are readily usable with collections of data-sets or controllers which may not rely on similar representational conventions.