Course description

This is a graduate-level seminar course focusing on current topics in computational genetics. It is intended for PhD students with interest in research problems in computational genetics. Topics will cover problems in medical and population genetics, statistical models that are needed to make inferences in these problems, and scalable inference in these models. Given the timing, we will focus on the recent work to understand the COVID-19 outbreak.

Prerequisites

Must have taken graduate level courses in computational genetics (CM226 or CM224) and/or machine learning (CS260). You will need instructor permission to enroll. If you are interested in taking the course, please email me with information about prior coursework and research experience.

Contact Info

Instructor: Sriram Sankararaman
Office Hours: By appointment
Email: sriram at cs dot ucla dot edu

Course format

• Readings: Each class will be assigned one or two readings.
• Lectures: The lectures will be led either by the instructor or by students.
• Project: A major component of this course will be an open-ended project.

Grading

• Presentation (40%)
  • Each student will have to present on one of the topics in the course. This will involve reading several papers and providing an understanding of the key ideas.
  • Please send your slides to me at least one week before your presentation.
• Class participation (10%):
  • Students are expected to attend all classes, complete posted readings and ask questions.
• Project (50%)
  • Ideally, you would work in groups of two but you can also work individually.
  • You are welcome to propose any project that is relevant to the course, including rotation projects.
  • You are expected to send an initial proposal followed by a final in-class presentation and report.

Tentative Schedule

See here