Faculty

Speaker: Alan Tsang, PhD Candidate

With the ever increasing ubiquity of social networks in our everyday lives, comes an increasing urgency for us to understand their impact on human behavior. Social networks quantify the ways in which we communicate with each other, and therefore shape the flow of information through the community. It is this same flow of information that we utilize to make sound, strategic decisions.

Speaker: Deepak Rishi, Master's Candidate

Sentiment and emotional analysis on online collaborative software development forums can be very useful to gain important insights into the behaviours and personalities of the developers. Such information can later on be used to increase productivity of developers by making recommendations on how to behave best in order to get a task accomplished. However, due to the highly technical nature of the data present in online collaborative software development forums, mining sentiments and emotions becomes a very challenging task.

Speaker: Valerie Platsko, Master's Candidate

Smart meter technology allows frequent measurements of water consumption at a household level. This greater availability of data allows improved analysis of patterns of residential water consumption, which is important for demand management and targeting conservation efforts. The dataset in this thesis includes 8,000 single family residences in Abbotsford, British Columbia from 2012 to 2013, and contains hourly measurements of water consumption recorded by smart meters installed in 2010. This work focuses on identifying outdoor consumption due to its contribution to peak demand during the summer, which is important because of concerns about strain on infrastructure in Abbotsford.

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.