Nisarg Shah, Department of Computer Science
University of Toronto
Fiodar Kazhamiaka, PhD candidate
David R. Cheriton School of Computer Science
Priyank Jaini, PhD candidate
David R. Cheriton School of Computer Science
Michael Cormier, PhD candidate
David R. Cheriton School of Computer Science
Matei Ripeanu, Department of Electrical and Computer Engineering
University of British Columbia
Abdullah Rashwan, PhD candidate
David R. Cheriton School of Computer Science
Sum-product networks have recently emerged as an attractive representation due to their dual view as a special type of deep neural network with clear semantics and a special type of probabilistic graphical model for which inference is always tractable. Those properties follow from some conditions (i.e., completeness and decomposability) that must be respected by the structure of the network.
Cecylia Bocovich, PhD candidate
David R. Cheriton School of Computer Science
Circumventing state firewalls! Détournement! Doctor Who references! Now with higher bandwidth!
Sverrir Thorgeirsson, PhD candidate
David R. Cheriton School of Computer Science
Torben Bach Pedersen, Professor of Computer Science
Aalborg University
Data collected from new sources such as sensors and smart devices is large, fast, and often complex. There is a universal wish to perform multidimensional OLAP-style analytics on such data, i.e., to turn it into “Big Multidimensional Data.” Supporting this is a multi-stage journey, requiring new tools and systems, and forming a new, extended data cycle with models as a key concept. We will look at three specifics steps in this data cycle.
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.