Current undergraduate students

Subscribe to Current undergraduate students

Nabiha Asghar, PhD candidate
David R. Cheriton School of Computer Science

Most of the existing neural conversational models process natural language primarily on a lexico-syntactic level, thereby ignoring one of the most crucial components of human-to-human dialogue: its affective content. We take a step in this direction by proposing three novel ways to incorporate affective/emotional aspects into long short term memory (LSTM) encoder-decoder neural conversation models: (1) affective word embeddings, which are cognitively engineered, (2) affect-based objective functions that augment the standard cross-entropy loss, and (3) affectively diverse beam search for decoding.

Andrew Delong, Head of Computational Research
Deep Genomics

Genomics focuses on the sequences in our genomes and how they encode for function in our cells. Predicting how sequences will be interpreted by the cell is important for identifying disease-causing mutations and for designing therapies.

Adam Schunk, Master’s candidate
David R. Cheriton School of Computer Science

Over the past years online social networks have become a major target for marketing strategies, generating a need for methods to efficiently spread information through these networks. Close-knit communities have developed on these platforms through groups of users connecting with likeminded individuals.

Eunsol Choi, Paul G. Allen School of Computer Science
University of Washington

Real world entities such as people, organizations and countries play a critical role in text. Reading offers rich explicit and implicit information about these entities, such as the categories they belong to, relationships they have with other entities, and events they participate in.

Professor Shai Ben-David and his colleagues Pavel Hrubes, Shay Moran, Amir Shpilka and Amir Yehudayoff have shown that a simple machine learning problem — whether an algorithm can extract a pattern from limited data — is mathematically unsolvable because it is linked to inherent shortcomings of mathematics discovered by Austrian mathematician Kurt Gödel in the 1930s.

Brandon Alcox, Master’s candidate
David R. Cheriton School of Computer Science

This thesis investigates the application of various fields of artificial intelligence to the domain of sports management and analysis. The research in this thesis is primarily focused on the entry draft for the National Hockey League, though many of the models proposed may be applied to other sports and leagues with minimal adjustments.

Cheriton School of Computer Science Professor Shai Ben-David, his former PhD student Hassan Ashtiani, now an Assistant Professor at McMaster University, along with colleagues Christopher Liaw, Abbas Mehrabian and Yaniv Plan, have received a best paper award at NeurIPS 2018, the 32^ndAnnual Conference on Neural Information Processing Systems.

Nabiha Asghar, PhD candidate
David R. Cheriton School of Computer Science

We address the problem of incremental domain adaptation (IDA). We assume each domain comes one after another, and that we could only access data in the current domain. The goal of IDA is to build a unified model performing well on all the domains that we have encountered. We propose to augment a recurrent neural network (RNN) with a directly parameterized memory bank, which is retrieved by an attention mechanism at each step of RNN transition. The memory bank provides a natural way of IDA: when adapting our model to a new domain, we progressively add new slots to the memory bank, which increases the number of parameters, and thus the model capacity.

Andreas Stöckel, PhD candidate
David R. Cheriton School of Computer Science

The artificial neurons typically employed in machine learning and computational neuroscience bear little resemblance to biological neurons. They are often derived from the “leaky integrate and fire” (LIF) model, neglect spatial extent, and assume a linear combination of input variables. It is well known that these simplifications have a profound impact on the family of functions that can be computed in a single-layer neural network.

Professors Olga Veksler and Yuri Boykov joined the David R. Cheriton School of Computer Science earlier this year. Previously, both were full professors in the Department of Computer Science at Western University, where they were faculty members for 14 years.

Their research interests are in the area of computer vision. In particular, Olga’s interests are in visual correspondence and image segmentation, and Yuri’s also include 3D reconstruction and biomedical image analysis.

Current undergraduate students

PhD Seminar: Modelling the Continuum of Emotions in Neural Dialogue Systems

AI Seminar: Deep Learning for Genomics

Master’s Thesis Presentation: An Analysis on The Network Structure of Influential Communities in Twitter

AI Seminar: Learning to Understand Entities in Text

Professor Shai Ben-David and colleagues’ work on machine learning featured as front-page article on Nature

Master’s Thesis Presentation: Applications of Artificial Intelligence to the NHL Entry Draft

Professor Shai Ben-David and colleagues win best paper award at NeurIPS 2018

PhD Seminar: Progressive Memory Banks for Incremental Domain Adaptation

PhD Seminar: Beyond LIF: The Computational Power of Passive Dendritic Trees

Meet Professors Olga Veksler and Yuri Boykov, two computer vision researchers who recently joined the Cheriton School of Computer Science

Waterloo.ai