Who or what affected your decision to pursue a faculty position?
I loved research right from the beginning. All of my supervisors were passionate about their research and that passion was contagious. I knew right from the beginning that research was what I wanted to do and the best path to do that was pursuing a faculty position.
How do your former supervisors influence your current training philosophy?
My supervisors allowed me to be self-guided, which was a good match for my character. As a supervisor, I try to encourage students’ independence. When a student starts off you have to give them a lot more direction to establish where their program is headed, but by the end of their training my goal is essentially to watch at a distance. My supervisors did a wonderful job of that.
I believe that you can do a couple of important things for your students. First, you can instill in them a real passion for the subject so that they love the work they do. Second, you can close off dead-ends for them, keeping them on a fruitful path. If you don't have that first piece – the passion – in place, you will need to micromanage everything because they won’t take the work to the next level. But once they have passion for their research, you just need to keep them pointed in the right direction.
What educational background and personal strengths do you look for from prospective group members?
I look for someone who is independent and self-motivated, hard-working. And someone who when given an idea, doesn’t just to do the one thing but instead pushes it several steps further, even if that might not be the best approach; thinking beyond the basic instructions is critical. People focus too much on the technical details, such as whether a student has the right math background, which is less important to me than their work ethic and level of motivation. I've seen technically proficient students come into a research group where they are expected to work on their own, but the skills required for research are not well aligned with the skills required for taking tests at the undergraduate level.
What contemporary scientific issue concerns you the most and requires immediate attention?
Something that concerns me is the entrance of unqualified people into the field of data science. Statistics, in my opinion, is the data science, yet the term has been adopted by people with a wide variety of training. There are strengths that come with statistical training – an understanding of foundational issues like confounding, design, collinearity, and causality, for example. There are many subtle issues in data analyses that can lead to major issues and major biases if they aren’t considered carefully beforehand; these things are taught in a formal statistics education. It's very easy to take statistical data and come to conclusions with it, but the understanding that comes with a proper statistical education can prevent you from making wrong conclusions.
Dr. Bornn’s presence enhances SFU’s expertise in the statistical analysis and modeling of big data. His interests complement those of his SFU colleagues in Math and Statistics, Biology, Kinesiology, Health, Computing, and Earth Sciences. His research will harness the immense power of using raw tracking data to make accurate predictions.
Read more: Dr. Bornn’s personal website and the New Science Faculty page
Interview by Jacqueline Watson with Theresa Kitos