Current undergraduate students

Speaker: Marta Kryven, PhD Candidate

Human beings quickly and confidently attribute more or less intelligence to one another. What is meant by intelligence when they do so? And what are the surface features of human behavior that determine their judgments? Because the judges of success or failure in the quest for 'artificial intelligence' will be human, the answers to such questions are an essential part of cognitive science. This thesis studies such questions in the context of a maze world, complex enough to require non-trivial answers, and simple enough to analyze the answers in term of decision-making algorithms.

Speaker: Ricardo Salmon, PhD Candidate

Stochastic satisfiability (SSAT) problems are an extension of SAT problems that is PSPACE-complete and in the same complexity class as quantified Boolean formula (QBF) and partially observable Markov decision processes (POMDPs).

Speaker: Haifeng Xu, University of Southern California

Strategic interactions among self-interested agents (a.k.a., games) are ubiquitous, ranging from economic activity in daily life and the Internet to defender-adversary interactions in national security.  A key variable influencing agents' strategic decision making is the information they have available about their environment as well as the preferences and actions of others. In this talk, I will describe my work on computational questions pertaining to the role of information in games.

Speaker: James Wright, Microsoft Research

In order to do a good job of interacting with people, a system must have an adequate model of how people will react to its actions. This is particularly true in strategic settings: settings that contain multiple agents, each with their own goals and priorities, in which each agent's ability to accomplish their goals depends partly on the actions of the other agents. Standard models of strategic behavior assume that the participants are perfectly rational. However, a wealth of experimental evidence shows that not only do human agents fail to behave according to these models, but that they frequently deviate from these models' predictions in a predictable, systematic way.

Speaker: Feng-Xuan Choo, PhD candidate

Building large-scale brain models is one method used by theoretical neuroscientists to understand the way the human brain functions. Researchers typically use either a bottom-up approach, which focuses on the detailed modelling of various biological properties of the brain and places less importance on reproducing functional behaviour, or a top-down approach, which generally aim to reproduce the behaviour observed in real cognitive agents, but typically sacrifices adherence to constraints imposed by the neuro-biology. 

The focus of this thesis is Spaun, a large-scale brain model constructed using a combination of the bottom-up and top-down approaches to brain modelling. Spaun is currently the world's largest functional brain model, capable of performing 8 distinct cognitive tasks ranging from digit recognition to inductive reasoning. The thesis is organized to discuss three aspects of the Spaun model.

Speaker: Daniel Recoskie, PhD candidate

We propose a method for learning wavelet filters directly from data. We accomplish this by framing the discrete wavelet transform as a modified convolutional neural network. We introduce an autoencoder wavelet transform network that is trained using gradient descent.

Speaker: Rejean Lau (University of Alberta)

Sentiment classification is a form of the text categorization problem where user sentiment is categorized as either positive or negative sentiment. It is generally accepted that sentiment analysis is a more challenging classification problem then topic categorization and here the bag of words approach does not perform as well. Using the IMDB sentiment dataset from Cornell University, we improve on their results by using a stacked classifier and web-mined features.

Speaker: Nasser Mooman

With the increasing amount of available information sources for learners, the need for systems that can effectively and efficiently mine, retrieve, and process such information has become critical.

Speaker: Linli Xu

In this talk, I will discuss a new unsupervised algorithm for training hidden Markov models that is convex and avoids the use of EM.

Speaker: Mu Zhu (Statistics and Actuarial Sciences, University of Waterloo)

I shall describe a few projects in the area of rare target detection.