Reliable detection of disease in the early stages of Alzheimer’s Disease (AD) continues to be a challenge. This project led by Drs. Conati and Field aims to investigate the value of eye-tracking data as one of the sources of information to build machine learning detectors of AD. In addition, the team will investigate eye-tracking based detectors of AD-related states such confusion and distress during naturally occurring tasks.

Extraction of knowledge from information sources ranging from unstructured and semi-structured, to structured has gained significant interest both in academia and in the industry. This is fueled by applications such as question answering and computational fact checking. Knowledge graphs (KG) have lately emerged as a de facto standard for knowledge representation, whereby knowledge is expressed as a collection of “facts", represented in the form of (subject, predicate, object) triples where subject and object are entities and predicate is a relation between those entities.

In this project, Drs. Sigal and Schmidt are pursuing a number of research goals at the intersection of computer vision and machine learning. In part one, the team will advance automatic video summarization by exploring novel richer joint video-linguistic and graph-structured representations to facilitate video retrieval, summarization and--potentially--action recognition tasks.

Existing systems for critical NLP tasks like question-answering and summarization are still unable to accurately uncover and effectively leverage the discourse structure of text; i.e., how clauses and sentences are related to each other in a document. This is a serious limitation in that relationships between clauses and sentences carries important information, which allows the text to express a meaning as a whole, beyond the sum of its parts. The goal of discourse parsing is to automatically determine the coherence structure of text.

Recent advances in next generation sequencing (NGS) technologies have led to the ability to measure gene expression and DNA mutations across thousands of cells in cancer tumors at the single-cell level. This allows us to quantify the effect of chemotherapeutic drugs on the way tumors mutate and answer questions about why particular groups of cells (known as clones) evade treatment and cause relapse. However, the vast quantities of data produced by such measurements combined with the low signal-to-noise ratio makes analysis and interpretation particularly difficult.

Chronic Obstructive Pulmonary Disease (COPD) is a progressive, debilitating, chronic respiratory disease. It is currently the 4th leading cause of mortality and is responsible for 100,000 hospitalizations and 10,000 deaths annually in Canada, and 3 million deaths worldwide. Although our understanding of COPD pathogenesis has improved substantially over the past 20 years, there is a notable lack of treatments that can modify disease progression and reduce mortality. Furthermore, current methods to clinically diagnose COPD are non-specific and insufficient to advance knowledge.

Massive open on-Line courses (MOOCS) have great potential to innovate education, but suffer from one key limitation typical of many on-line learning environments: lack of personalization. Intelligent Tutoring Systems (ITS) is a field that leverages Artificial Intelligence and Machine Learning to devise educational tools that can provide instruction tailored to the needs of individual learners, as good teachers do. In this project, Drs. Conati and Roll aim to apply some of the concepts and technique from ITS research to MOOCS.

The diagnosis, management, and treatment of chronic diseases (e.g., diabetes, chronic obstructive pulmonary diseases, and heart failure) have traditionally been focused on longitudinal histories and physical examinations as primary tools of assessment, and augmented by laboratory testing and imaging. Equally important to history taking and physical examinations is the objective assessments and understanding of the contribution of the patients' states of mind to their disease states. This is historically only documented qualitatively but highly challenging to measure quantitatively.

The recent explosion of chemical and biological information calls for fundamentally novel ways of dealing with big data in the life sciences. This problem can potentially be addressed by the latest technological breakthroughs on both software and hardware frontiers. In particular, the latest advances in artificial intelligence (AI) enable cognitive data processing at very large-scale by means of deep learning (DL).

Computer systems – both hardware and software – currently represent an active barrier to the scientific investigation of genomic data. Answering even relatively simple questions requires assembling disparate software tools (for alignment, variant calling, and filtering) into an analytics pipeline, and then solving practical IT problems in order to get that pipeline to function stably and at scale. This project will employ a whole system approach for providing a framework for genomic analysis.