Current undergraduate students

Speaker: Ali Wytsma, Master's Candidate

The naive Bayes model is a simple model that has been used for many decades, often as a baseline, for both supervised and unsupervised learning. With a latent class variable it is one of the simplest latent variable models, and is often used for clustering. The estimation of its parameters by maximum likelihood (e.g., gradient ascent, expectation maximization) is subject to local optima since the objective is non-concave. However, the conditions under which global optimality can be guaranteed are currently unknown.

Speaker: Michael Cormier, PhD Candidate

In this talk I will present recent advances in our vision-based web page segmentation method. This method is an edge-based, Bayesian image segmentation algorithm, capable of estimating the semantic structure of a web page from an image of the page.

Speaker: Areej Alhothali, PhD Candidate

Natural language texts are often meant to express or impact individuals emotions. Sentiment analysis researchers are increasingly interested in investigating natural language processing techniques as well as emotions theories to classify sentiments expressed in natural language text. Most sentiment analysis research effort focuses on classifying highly opinionated documents from the writer's perspectives and uses either count-based word representations that ignore sentence structure or a small set of lexicon resources that do not cover the wide range of words used on the Internet.

Speaker: Mohammed Alliheedi, PhD Candidate

Over the past decade, the focus on argumentation mining has been growing significantly in different areas of Artificial Intelligence (AI) research. The incentive to build Natural Language Processing (NLP) systems to automatically identify and analyze argumentative components in various genres of texts has increased because knowledge of argumentative structure facilitates various tasks such as text summarization and opinion mining for commercial purposes.

Speaker: Carolyn Lamb, PhD Candidate

Computer software has frequently been used to perform creative tasks, such as generating artwork or discovering new mathematical proofs. But if a computer is intended to be creative, how do we measure its performance or decide if it is doing a good job?

Speaker: Mariah Shein, Master's Candidate

The development of the field of reinforcement learning was based on psychological studies of the instrumental conditioning of humans and other animals. Recently, reinforcement learning algorithms have been applied to neuroscience to help characterize neural activity and animal behaviour in instrumental conditioning tasks. A specific example is the hybrid learner developed to match human behaviour on a two-stage decision task. This hybrid learner is composed of a model-free and a model-based system.

Speaker: Carolyn Lamb, PhD Candidate

Artists, hobbyists, cognitive scientists, and computer scientists are all trying their hands at writing computer programs that generate poetry. Why? What do these poems look like, and how are they made? What might meta-poetry writers on both sides of the art-science spectrum learn from each other?

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.