Since my work spans ecology, evolution, epidemiology, mathematics, statistics, and computation, my *ideal* prospective student/postdoc knows something about all of those topics. You shouldn’t be discouraged if you don’t know all that stuff yet (I didn’t when I started!), but knowing something about *some* of it will definitely encourage me to accept you as a student, for two reasons:

- you’ll be more capable of doing interesting stuff, with less up-front investment by me
- you’ll have demonstrated some initiative (“I’m fascinated by (math/stats/computation/ecology/epidemiology), but I have never made any effort to learn about it” …)

## Admission and timing

My ‘lab’ size is approximately 1-2 undergraduates, 1-2 master’s students, 1-2 PhD students, maybe a postdoc. (I take relatively few undergraduate and master’s students because I don’t need people to do grunt-work/have to invest a lot in each student.)

I would expect to hear from prospective graduate students sometime May-October for admission the following September, and prospective undergraduate thesis students in March-April for thesis projects starting the following September.

## Projects

I like prospective students to have some idea what topics they would like to work on. More detailed proposals are welcome, but in my experience most incoming students have a hard time identifying a good, feasible project. Check the ideas page and some of my recent papers for general areas of interest.

## Preferred background

### Math & stats

- Differential and integral calculus; ideally linear algebra (MATH 1B3, ideally 2LA3 or 2R03), differential equations (MATH 2C03, possibly 2F03)
- At least basic stats (STATS 2B03); ideally probability (2D03), math stats (2MB3), and regression (3A03). Generalized linear models (4C03) and other advanced courses are a bonus.

### Biology

- Basic eco/evo (BIO 1M03); ecology (2F03) and evolution (3FF3). Ideally population ecology (3SS3), genetics (2C03).

### Computation

- The most important practical skill is R programming; the more, the better. If you know Python well (MATH 1MP3, COMPSCI 1MD3) you can probably learn R. C++ or other more technical languages would be a bonus. If you have general command-line/shell/infrastructure knowledge (Unix shell, make, GitHub, etc.) that’s a bonus.