Events

Filter by:

Limit to events where the first date of the event:
Date range
Limit to events where the first date of the event:
Limit to events where the title matches:
Limit to events where the type is one or more of:
Limit to events tagged with one or more of:
Limit to events where the audience is one or more of:
Wednesday, December 20, 2023 11:00 am - 12:00 pm EST (GMT -05:00)

Master’s Thesis Presentation • Algorithms and Complexity • Compact Routing on Planar Graphs

Please note: This master’s thesis presentation will take place online.

Newsha Seyedi, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Ian Munro

This thesis delves into the exploration of shortest path queries in planar graphs, with an emphasis on the utilization of space-efficient data structures. Our investigation primarily targets connected, undirected, static pointer planar graphs, focusing on scenarios where queries predominantly start or end at a select subset of nodes.

Please note: This master’s thesis presentation will take place in DC 2310 and online.

Sonja Linghui Shan, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jeffrey Shallit

Please note: This master’s thesis presentation will take place in DC 3317.

Adrian Cruzat La Rosa, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Diogo Barradas

Please note: This PhD defence will take place in DC 3317 and online.

Chendi Ni, PhD candidate
David R. Cheriton School of Computer Science

Supervisors: Professors Yuying Li, Peter Forsyth

Please note: This master’s research paper presentation will take place online.

Muhammad Arsalan Khan, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Shane McIntosh

Friday, January 12, 2024 12:00 pm - 1:00 pm EST (GMT -05:00)

PhD Seminar • Artificial Intelligence • Learning Voting Rules Using Neural Networks

Please note: This PhD seminar will take place online.

Ben Armstrong, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Kate Larson

We present our work using machine learning models to approximate social choice functions, a.k.a. methods of voting. Voting rules are functions that are given voter preferences and produce a winning candidate.

Please note: This master’s thesis presentation will take place in DC 2310.

Renee Leung, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Jesse Hoey

Please note: This master’s thesis presentation will take place in DC 2314, not DC 3317. 

Lucas Fenaux, Master’s candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Florian Kerschbaum