A core problem in statistics and machine learning is to approximate difficult-to-compute probability distributions. This problem is especially important in probabilistic modeling, which frames all inference about unknown quantities as a calculation about a conditional distribution.
Networks are ubiquitous in biology where they encode connectivity patterns at all scales of organization, from populations to a single cell. How to extract and understand non-trivial topological features and structures inherent in the networks is critical to understanding interactions within complicated biological systems.
Location: Computer Science Small Auditorium (Room 105)