Seminar Details

Seminar Details


Jan 13

3:30 pm

Exploring dynamic complex systems using time-varying networks

Mladen Kolar (Sponsored by Microsoft)


University of Chicago

Extracting knowledge and providing insights into the complex
mechanisms underlying noisy high-dimensional data sets is of utmost
importance in many scientific domains. Networks are an example of
simple, yet powerful tools for capturing relationships among entities
over time. For example, in social media, networks represent
connections between different individuals and the type of interaction
that two individuals have. In systems biology, networks can represent
the complex regulatory circuitry that controls cell behavior.
Unfortunately the relationships between entities are not always
observable and need to be inferred from nodal measurements.

I will present a line of work that deals with the estimation of
high-dimensional dynamic networks from limited amounts of data. The
framework of probabilistic graphical models is used to develop
semiparametric models that are flexible enough to capture the dynamics
of network changes while, at the same time, are as interpretable as
parametric models. In this framework, estimating the structure of the
graphical model results in a deep understanding of the underlying
network as it evolves over time. I will present a few computationally
efficient estimation procedures tailored to different situations and
provide statistical guarantees about the procedures. Finally, I will
demonstrate how dynamic networks can be used to explore real world