U of T Statistical Sciences: Can you tell me a little bit about your background, and how you became interested in statistics?
Alex Stringer: I grew up in Toronto, and I started my undergrad in the food science program at the University of Guelph. Food science is a fantastic program, especially at Guelph, but when I took my first statistics course, I liked it so much that I changed my major. I had a really good professor, Jeremy Balka, at Guelph.
I liked math on and off throughout high school, and statistics was a kind of math that really clicked with me. I wasn't sure what I wanted to do after my undergrad, so I applied to the masters at U of T, and I had a great time as a master's student. It was a course-based masters, so I didn't really get into research. But I met some great people, both professors and students, who have become my friends. After that I got a job at TransUnion in Burlington, and so I did that for about a year. Then I got a job at ScotiaBank in Toronto, and did that for about a year and a half.
How did you make the decision to come back for a PhD?
When I was working in Toronto, I was neighbours with Alison Gibbs, who's a professor in the department. I saw her and we went for coffee, and she encouraged me to think about continuing with academia. Then I actually got hired out of industry to teach on a sessional basis. And I figured at that point, “Wow, I should apply to the PhD”. You know, give it a shot, right?
I asked Nancy Reid, who's been incredibly helpful and supportive as she tends to be, for suggestions on supervisors. One person she suggested was Patrick Brown, because he has a lot of really interesting data and does a lot of really interesting interdisciplinary work.
So I emailed Patrick and said, “Can we talk about supervision?” And he replied almost immediately saying, “Sure, come to this workshop that I'm organizing on campus”. I get to the workshop and I had never met Patrick before, and he's there wearing, if I recall correctly, a full white linen suit, and a white straw boater.
It was June. He was dressed absolutely amazingly. Like, he was dressed to kill. And based on that, I said, “I want this man to supervise my doctorate”.
And then I ran into Jamie Stafford at the conference. He came over and said, you know, “why are you here?” And I told him and he said, “Well, great, we'll co-supervise you”. And so then I got two fairly fantastic PhD supervisors out of that bizarre sequence of events.
What is it about statistics that made you want to switch into it early on, during your undergrad?
I mean, the food program had really incredible courses. The food science students at Guelph are making ice cream in the dairy lab, and they're learning how Canada Bread makes a million loaves of bread a day. It's all really cool stuff.
But I was really interested in the fact that, in statistics, you could constantly just be learning about completely new things. Like before this meeting with you, I just had a meeting where they were talking about people in the oil sands who try to estimate animal abundance by setting up trail cameras and counting the animals that go by each camera.
This, apparently, is an unsolved statistical problem. I don’t know anything about oil sands or trail cameras, but now I'm meeting to discuss a project about oil sands and trail cameras. It's a weekly occurrence where I learn about a completely new area of science that has been surprisingly unsolved and where statistics is apparently the answer. It’s incredible.
There are lots of people doing statistics who specialize, but you don't have to. You can also just constantly be doing new applied things. That was apparent from the first day.
What are you working on right now?
I’m doing something cool on a topic called benchmark dosing, which is essentially trying to infer the amount of exposure to some sort of toxin or environmental danger that yields some sort of a response in the population.
This is used by the environmental protection agencies all over the world to decide, you know, how much mercury we're allowed to have in the fish we eat and things like that. But it's actually statistically very poorly understood, apparently. Or at least, there's room to understand more about the properties of these estimates. So I don't know anything about toxicology, but suddenly I'm working on a toxicology related project. As a statistician, there are things like that that just pop up all the time.
How did your time in the University of Toronto PhD program shape your research?
My supervisors directed me right from the start on on things that they thought I'd be interested in, and they were right. I became very interested in what they told me to look at.
I also met some very good friends in the program, and we’ve collaborated on research. Blair Bilodeau, Yanbo Tang and I just got a paper accepted, which we started without supervisors as PhD students. We all have fairly distinct skill sets, and we created something that's much greater than what any pair of us could have even done. So that was a really, really great experience. And I think I'll probably work with them for a long time.
If you had any advice for students who were considering a PhD in statistical science but are not sure whether it’s the right fit for them, what would you advise them to consider?
One thing I’d say is that it's not a straightforward next step from your previous degree. It's a massive shift both in responsibility and in what's required to succeed.
If you're an undergrad and you're thinking, “Should I do a master's?”, then sure, do one. If you can get in, and if you know you have the background to get into the Master's program, you're probably better off doing it. That's not true of a PhD. If you're unsure, you might want to try something else first, like working. There's no downside to working after your masters for a few years. You gain a lot of maturity while working too, and then of course money, which you need to live. Most people, especially in U of T's program, are in their mid to late twenties when they start the program. I was 27 when I started. It's never too late to do a PhD.
The thing about a PhD is that it’s more like an apprenticeship than a program. It's not actually that conceptually different from a trade apprenticeship. Think of it like it's a research apprenticeship: you're learning how to be an independent researcher. It's different from your previous degrees in that way. I wouldn't call an undergrad or a master's an apprenticeship really.
So outside of statistics, what do you do for fun?
Well, I had a kid two years ago, so lately I basically just do that.
Oh, congrats! That's awesome.
Thanks! But yeah, I love nature. I actually live in Muskoka, and I go to Waterloo three days a week. We're constantly in the forest and canoeing and doing that sort of thing.
I used to play in bands. I played guitar for five years in the Happy Pals New Orleans Party Orchestra, which is Toronto's longest running musical residency. They've played at the same place every week at the same time for over 50 years. And then I also had my own rock and roll band, which was just for fun.
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