Area of Study / Keywords

machine learning; medical informatics; survival prediction

---

About

Education

• B.Sc., Mathematics & Computer Science, California Institute of Technology, 1976
• M.Sc., Computer Science, Stanford University, 1979
• Ph.D., Computer Science, Stanford University, 1985

---

Research

Areas

Artificial Intelligence

Bioinformatics

Machine Learning

Summary

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: for example, to better understand brain tumors (from MRI scans), various cancers based on microarray data, Single Nucleotide Polymorphisms, and/or metabolic profiles. Some other projects are more technology push -- where the goal is more exploring some foundation or mathematical framework, rather than solving some application: such as learning Bayesian belief nets, active learning and addressing high-dimensional data ("large p, small n").

See my homepage.

Projects in the news:

• Artificial intelligence for mental health
• Machine learning experts recognized for work on prostate cancer treatment
• Machine learning enables new insight into schizophrenia
• AI to help manage Canada's invasive species

CMPUT 469 - Artificial Intelligence Capstone

Students will experience the challenges, and rewards, of working in a team to address a real-world task, related to artificial intelligence or machine learning. This will involve first identifying the task itself, then iteratively addressing relevant issues (typically with feedback from a domain expert), leading to an implementation and culminating in evaluating that system. Students will also learn about best practices in organizing team projects, as well as important information about effective communication. Prerequisites: CMPUT 267, 365, and 366.

---

Browse more courses taught by Russ Greiner