Quantum Computing and the Limits of the Efficiently Computable

Note: This is a public talk open to the general audience. Refreshments will be served prior to the event starting at 17:45


I'll discuss how computational complexity — the study of what can and can't be feasibly computed — has been interacting with physics in interesting and unexpected ways. This will include a crash course about quantum mechanics and the capabilities and limits of quantum computers. I'll also touch on speculative models of computation that would go even beyond quantum computers.

Efficient Human-in-the-loop Statistical Relational Learning


Statistical Relational Learning (SRL) Models combine the powerful formalisms of probability theory and first-order logic to handle uncertainty in large, complex problems. While they provide a very effective representation paradigm due to their succinctness and parameter sharing, efficient learning is a significant problem in these models. First, I will discuss state-of-the-art learning method based on boosting that is representation independent. Our results demonstrate that learning multiple weak models can lead to a dramatic improvement in accuracy and efficiency.

Directed Research Project Presentations


The first milestone in the CIS Ph.D. program is the Directed Research Project (DRP). Each student must complete this milestone within their first two years in the program. Typically, students devote the summer after their first year in the program exclusively to their research projects. In this colloquium, seven of our Department's Ph.D. students will present summaries on the progress on their DRPs over this past summer.

Faculty Research Topics


This week we continue our introduction to faculty research topics. We encourage all faculty members and PhD students to attend and hope these presentations help students meet faculty members and get exposed to the full range of the research portfolio in our department. We feature the following three speakers this week:

Presentation #1: "Research Overview for CDUX: Computing and Data Understanding at eXtreme Scale" by Associate Professor Hank Childs


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