Russell Greiner: Research

Research Interests

I am interested in building algorithms that learn from experience, to be able to perform their tasks better.
Most of my current work has a strong application pull -- i.e., is motivated by some specific tasks. Some other projects are more technology push -- where the goal is more exploring some foundation or mathematical framework, rather than solving some application.
(Each research project is placed in only one list, based on its main emphasis; almost all of the project actually involve both aspects.)

See also Research Summary 2006-2011 (NSERC Form 100), and miscellaneous extended webpages.

Application Pull   (Complete Listing)

Technology Push   (Complete Listing)

  • Patient-Specific Survival Prediction: a novel algorithm for learning patient-specific survival time distribution, based on all available patient attributes -- basically a personalized version of Kaplan-Meier curve, that can be used to visualize the survival rate of an individual patient, or to predict median survival time, or whatever.
  • Explaining the Gene Signature Anomaly: formally investigating the overlap of the top ranked features in two lists whose elements are ranked by their respective Pearson correlation coefficients with the same outcome.
  • Budgeted learning: deciding which features of which training instances to purchase, to produce an effective classifier, when the learner has a fixed budget for such purchases
  • Learning belief nets:

Webpages with Details

Education and Popularizing

Earlier work