Events

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. 

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. 

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.

Yuxin Chen, Postdoctoral scholar, Department of Computing and Mathematical Sciences
California Institute of Technology

How can we intelligently acquire information for decision making, when facing a large volume of data? 

In this talk, I will focus on learning and decision making problems that arise in robotics, scientific discovery and human-centered systems, and present how we can develop principled approaches that actively extract information, identify the most relevant data for the learning tasks and make effective decisions under uncertainty.

Aishwarya Agrawal, PhD candidate
School of Interactive Computing, Georgia Tech

In this talk, I will present our work on a multi-modal AI task called Visual Question Answering (VQA) — given an image and a natural language question about the image (e.g., “What kind of store is this?”, “Is it safe to cross the street?”), the machine’s task is to automatically produce an accurate natural language answer (“bakery”, “yes”).

Shrinu Kushagra, PhD candidate
David R. Cheriton School of Computer Science

Data de-duplication is the task of detecting multiple records that correspond to the same real-world entity in a database. In this work, we view de-duplication as a clustering problem. We introduce a framework which we call promise correlation clustering. Given a complete graph \(G\) with the edges labeled \(0\) and \(1\), the goal is to find a clustering that minimizes the number of \(0\) edges within a cluster plus the number of \(1\) edges across different clusters (or correlation loss). The optimal clustering can also be viewed as a complete graph \(G^*\) with edges corresponding to points in the same cluster being labeled \(0\) and other edges being labeled \(1\).