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Wednesday, August 1, 2018 2:00 pm - 2:00 pm EDT (GMT -04:00)

Seminar • Networks and Distributed Systems — Cache Modeling and Optimization using Miniature Simulations

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.

Tuesday, August 7, 2018 10:30 am - 10:30 am EDT (GMT -04:00)

Seminar • Software Engineering — A rerun of "The Prehistory and History of RE (+SE) as Seen by Me"

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.

Wednesday, August 29, 2018 1:30 pm - 1:30 pm EDT (GMT -04:00)

Seminar • Algorithms and Complexity — Counting Subwords and Regular Languages

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. 

Monday, September 24, 2018 3:30 pm - 3:30 pm EDT (GMT -04:00)

Inaugural Seminar • AI Institute — Artificial Intelligence — History, Opportunities and Challenges

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.