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Thursday, December 13, 2018 10:00 am - 10:00 am EST (GMT -05:00)

Master’s Thesis Presentation • Systems and Networking — Aggregation of Heterogeneous Anomaly Detectors for Cyber-Physical Systems

Murray Dunne, Master’s candidate
David R. Cheriton School of Computer Science

Distributed, life-critical systems that bridge the gap between software and hardware are becoming an integral part of our everyday lives. From autonomous cars to smart electrical grids, such cyber-physical systems will soon be omnipresent. With this comes a corresponding increase in our vulnerability to cyber-attacks. Monitoring such systems to detect malicious actions is of critical importance. 

Thursday, December 13, 2018 4:00 pm - 4:00 pm EST (GMT -05:00)

PhD Seminar • Artificial Intelligence — Beyond LIF: The Computational Power of Passive Dendritic Trees

Andreas Stöckel, PhD candidate
David R. Cheriton School of Computer Science

The artificial neurons typically employed in machine learning and computational neuroscience bear little resemblance to biological neurons. They are often derived from the “leaky integrate and fire” (LIF) model, neglect spatial extent, and assume a linear combination of input variables. It is well known that these simplifications have a profound impact on the family of functions that can be computed in a single-layer neural network. 

Wednesday, December 19, 2018 12:30 pm - 12:30 pm EST (GMT -05:00)

Master’s Thesis Presentation • Software Engineering — Astra: Evaluating Translations from Alloy to SMT-LIB

Ali Abbassi, Master’s candidate
David R. Cheriton School of Computer Science

We present a variety of translation options for converting Alloy to SMT-LIB via Alloy’s Kodkod interface. Our translations, which are implemented in a library that we call Astra, are based on converting the set and relational operations of Alloy into their equivalent in typed first order logic (TFOL). 

Friday, December 21, 2018 1:30 pm - 1:30 pm EST (GMT -05:00)

PhD Seminar • Symbolic Computation — Finding the Nearest Non-Trivial Smith Normal Form

Joseph Haraldson, PhD candidate
David R. Cheriton School of Computer Science

We consider the problem of computing the nearest matrix polynomial with a non-trivial Smith Normal Form (SNF). This is a non-convex optimization problem where we find a nearby matrix polynomial with prescribed eigenvalues and associated multiplicity structure in the invariant factors.