The next Waterloo-local ACM-style programming contest will be held on Sunday, September 30 in MC 3003.
All members of the University of Waterloo community are invited to try their programming skill in Racket, C, C++, Java, Pascal, Python, or Scala.
Join us
On Friday, September 28 we will launch the new Cybersecurity and Privacy Institute.
The Institute brings together under one umbrella Waterloo’s 40 security researchers from across the University. These renowned experts are collaborating to uncover new approaches to security and privacy while also partnering with corporations and government to advance the application and implementation of cybersecurity and privacy technologies.
Mohamed Malek Naouach, Master’s candidate
David R. Cheriton School of Computer Science
Adam Molnar, Deakin University
Daniel Recoskie, PhD candidate
David R. Cheriton School of Computer Science
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.
Nicole McNabb, Master’s candidate
David R. Cheriton School of Computer Science
Omar Zia Khan, Senior Applied Scientist
Microsoft
Ahmed Khan, Master’s candidate
David R. Cheriton School of Computer Science
Neurobiologically-plausible learning algorithms for recurrent neural networks that can perform supervised learning are a neglected area of study. Equilibrium propagation is a recent synthesis of several ideas in biological and artificial neural network research that uses a continuous-time, energy-based neural model with a local learning rule. However, despite dealing with recurrent networks, equilibrium propagation has only been applied to discriminative categorization tasks.
The David R. Cheriton School of Computer Science invites you to attend the 2018 Cheriton Research Symposium, held on Friday, September 21, 2018 in the Davis Centre.
Ifaz Kabir, Master’s candidate
David R. Cheriton School of Computer Science
Md Faizul Bari, PhD candidate
David R. Cheriton School of Computer Science
Elaheh Jalalpour, Master’s candidate
David R. Cheriton School of Computer Science
Steven Wang, Master’s candidate
David R. Cheriton School of Computer Science
Pirathayini Srikantha, Department of Electrical and Computer Engineering
Western University
Yossef Musleh, Master’s candidate
David R. Cheriton School of Computer Science
We introduce a Monte Carlo randomized algorithm for computing the characteristic polynomial of a rank 2 Drinfeld module than runs in $O(n^2 \log n \log \log n \log q)$ field operations. We also introduce a deterministic algorithm that runs in $O(n^{2.6258} \log n + n^2 \log n \log log n \log q)$ field operations. Both approaches are a significant improvement over the current literature.
Irish Medina, Master’s candidate
David R. Cheriton School of Computer Science
Smart water meters have been installed across Abbotsford, British Columbia, Canada, to measure the water consumption of households in the area. Using this water consumption data, we develop machine learning and deep learning models to predict daily water consumption for existing multi-family residences. We also present a new methodology for predicting the water consumption of new housing developments.
Filip Pawlega, Master’s candidate
David R. Cheriton School of Computer Science
Angshuman Ghosh, Master’s candidate
David R. Cheriton School of Computer Science
Matthew Amy, PhD candidate
David R. Cheriton School of Computer Science
Ricardo Salmon, PhD candidate
David R. Cheriton School of Computer Science
