Events

Kshitij Jain, Master’s candidate
David R. Cheriton School of Computer Science

We introduce a problem called the Minimum Shared-Power Edge Cut (MSPEC). The input to the problem is an undirected edge-weighted graph with distinguished vertices s and t, and the goal is to find an s-t cut by assigning "powers'' at the vertices and removing an edge if the sum of the powers at its endpoints is at least its weight. The objective is to minimize the sum of the assigned powers.

Irfan Ahmad, Founder and CEO
CachePhysics

​Caches in modern distributed and storage systems must be manually tuned and sized in response to changing application’s workload. A balance must be achieved between cost, performance and revenue loss from cache sizing mis-matches. However, caches are inherently nonlinear systems making this exercise equivalent to solving a maze in the dark.

Daniel M. Berry
David R. Cheriton School of Computer Science

Dan Berry weaves the twin peaks of (1) his life in computing, programming, programming languages, software engineering, electronic publishing, and requirements engineering with (2) the almost concurrent development of programming languages, software engineering, and requirements engineering.

Aiman Erbad, Computer Science and Engineering Department
Qatar University

Abdullah Rashwan, PhD candidate
David R. Cheriton School of Computer Science

Finn Lidbetter, Master’s candidate
David R. Cheriton School of Computer Science

Let x and y be words. We consider the languages whose words z are those for which the numbers of occurrences of x and y, as subwords of z, are the same (resp., the number of x's is less than the number of y's, resp., is less than or equal). In this talk we will give a necessary and sufficient condition on x and y for these languages to be regular, and we show how to check this condition efficiently. 

Matthew Amy, PhD candidate
David R. Cheriton School of Computer Science

Pirathayini Srikantha, Department of Electrical and Computer Engineering
Western University

Omar Zia Khan, Senior Applied Scientist
Microsoft

Hamid Tizhoosh, SDE
University of Waterloo

The history of artificial intelligence (AI) contains several ebbs and flows and is marked by many colorful personalities. We review major milestones in the development of machine learning, starting from principal component analysis to deep networks, and point to a multitude of pivotal developments that have strongly contributed to drawing the historical path of AI.