Possible Summer Jobs (2016) for Undergraduate Students
If you are interested in any of the positions mentioned below, please send me ...
Note that we are only looking for students from the University of Alberta, or other local universities.
Patient Specific Survival Prediction
We consider the challenge of predicting the survival time for individual patients -- or actually, the distribtion over survival time (like a Kaplan-Meier Curve, but for an individual, not a class of patients.) This led to our PSSP tool; check out this website; and description.
We are looking for a student to help us extend this:
Intelligent Diabetes Management
Patients with TypeI diabetes must regulate their own insulin, by administering insulin injections, several times a day. The amount is based on a parameterized formula, that involves their current blood glucose level and anticipated carbohydrate consumption, etc. This project seeks ways to improve that formula, for each individual patient, based on his/her specific logs. See project webpage.
Patient-specific Cancer TreatmentLearn which treatment should be most effective for each specific (cancer) patient, based on
Human Metabolome Project
Learning tasks typically begin with a data sample --- eg, symptoms and test results for a set of patients, together with their clinical outcomes. By contrast, many real-world studies begin with no actual data, but instead with a budget --- funds that can be used to collect the relevant information. For example, one study has allocated $30 thousand to develop a system to diagnose cancer, based on a battery of patient tests, each with its own (known) costs and (unknown) discriminative powers. Given our goal of identifying the most accurate classifier, what is the best way to spend the $30 thousand? Should we indiscriminately run every test on every patient, until exhausting the budget? Or, should we selectively, and dynamically, determine which tests to run on which patients? We call this task budgeted learning.
There are many open questions, both theoretic and empirical.