### Timing It Just Right: Learning and Optimization of High Dimensional Event Cascades

- Series
- Applied and Computational Mathematics Seminar
- Time
- Monday, February 24, 2014 - 14:00 for 1 hour (actually 50 minutes)
- Location
- Skiles 005
- Speaker
- Le Song – Georgia Tech CSE

Dynamical processes, such
as
information diffusion in social networks, gene regulation in
biological systems and
functional collaborations between brain regions, generate a
large
volume of high dimensional “asynchronous” and
“interdependent”
time-stamped event data. This type of timing information is rather
different from traditional iid.
data and discrete-time temporal data, which calls for new
models and
scalable algorithms for learning, analyzing and utilizing
them. In
this talk, I will present methods based on multivariate point
processes, high dimensional sparse recovery, and randomized
algorithms for addressing a sequence of problems arising from
this
context. As a concrete example, I will also present
experimental
results on learning and optimizing information cascades in web
logs,
including estimating hidden diffusion
networks
and influence maximization with the learned networks.
With both careful model and algorithm design, the framework is
able
to handle millions of events and millions of networked
entities.