Q: What was your research area?
A: My research area was broadly in applied operations research; I considered two different problems in my thesis. The first was a mechanism design problem in which I introduced a way to model operational rule changes via distributional censoring. The second was a sports ranking problem in which I connected two subfields of network-based ranking models to enhance ranking predictive performance.
Before the Ph.D.
Q: What inspired you to pursue a Ph.D.? Was there a defining moment or motivation behind your decision?
A: My inspiration for doing a Ph.D. was perhaps atypical. My undergrad was a Bachelor of Commerce with a Major in Finance at the Sobey School of Business at Saint Mary’s University. Afterwards, I applied and was accepted to the University of Toronto’s Master of Financial Economics Program (MFE). During my first year, I took a class in econometrics, which was my first rigorous introduction to quantitative analysis. Despite not having all the requisite background to excel in the course (Commerce didn’t require the requisite courses in probability and statistics), I found the methods quite captivating. They seemed to answer a question I had during my time in the Commerce program: how do we informatively make decisions?
During that summer, I interned at the Ontario Teachers’ Pension Plan Investment Board, specifically in the Infrastructure and Natural Resources group. Most of my colleagues in that group had a background in investment banking, which tends to be based on the Corporate Finance subfield of Finance. As such, the analytical methods tended to focus on financial statement analysis; financiers would know this as fundamental analysis. As I worked, it gradually dawned on me that I did not particularly enjoy financial statement analysis as the only approach in my analytical arsenal.
I began reading about quantitative finance and investment analysis in my spare time. After doing some digging, I realized that all of the main methodologies of quantitative finance are actually housed and originate from operations research (e.g., optimization, statistics, stochastic processes). After getting support from my parents on a change of direction away from near-term gainful employment on Bay Street, I pivoted to applying to Ph.D. programs in the Fall as I completed my MFE.
Q: How did you prepare for your Ph.D.? (e.g., choosing a program, advisor, or research area)
A: To be honest, I applied quite broadly. In my letter of intent, I did indicate that I was interested in exploring machine learning approaches in quantitative finance, but that was based on my, at the time, naive understanding of those problems and methodologies. Ultimately, I applied to the Ph.D. program at the University of Waterloo, and I was quickly accepted after a brief call with one of my eventual supervisors, Professor Stanko Dimitrov.
Challenges and Learning Moments
Q: What obstacles did you face during your Ph.D. course, and how did you navigate them?
A: A big challenge for me during my Ph.D. was that I came to operations research from a different discipline, so I had to scope out the mathematical tools I was missing. I was honest about my shortcomings with my supervisors, and they were very understanding. This allowed me to avoid taking a proof-based optimization course during my first year, which, at the time, I would have been unable to handle.
In light of this knowledge gap, I also got into a regular habit of studying math for a few hours a day. I can’t say that all of those hours were ultimately spent as efficiently as possible; in hindsight, there would be a few choice textbooks I would have given myself to work on. However, I did gradually develop a degree of mathematical maturity, which was ultimately crucial in me completing my Ph.D.
Q: How did you stay motivated and manage stress, especially during intense periods or when things didn’t go as planned?
A: I am a big believer in trying to cultivate small wins throughout the day. If you base your self worth on just one component of your life, if that is going poorly, everything feels like it’s going poorly. In a researcher context, that means developing some activities not directly tied to your research to help you feel good about yourself daily. Research is a marathon, especially at the beginning when you’re inexperienced, so those wins will keep you going over the long term.
Exercise was always crucial for me to de-stress and find my equilibrium. I grew up as a high-level academic and athlete, and I believe the balance is necessary for consistent high-performance. Doing it daily was a great ``physical win’’.
Another reason I liked studying some math every day is that it let me chalk up a ``learning win’’ daily. Even if my research was giving me issues, I had already invested something, knowledge-wise, into myself that day.
Q: Was there a point where you doubted your ability to continue? How did you push through those moments?
A: I certainly did experience doubts during my program. I pushed through those moments by believing in the system I had set up for myself. I knew that if I stuck to my learning, I would eventually cultivate the competency I needed to complete my research. That, in my opinion, is why systems and routines are important.
Q: What’s a memorable mistake or failure you experienced during your Ph.D., and what did it teach you?
A: While I was working on my mechanism design paper, I had reached a point when I thought I had completed the work. I had solved the various cases for the Karush-Kuhn-Tucker conditions for the optimization problem. After excitedly submitting it to my supervisors, I was told that, unfortunately, this perceived end point was merely the starting point for the research.
In hindsight, I find this moment quite funny. It highlights, to me, the difference in understanding I have now for what constitutes a contribution to the literature. I eventually developed a novel contribution for the paper, and this moment taught me that distinction.
Q: What kept you passionate about your research throughout your Ph.D., despite the challenges?
A: Reflecting back on my starting point was always helpful. It let me see the growth I made, and how far I had come from where I started. It is also worth noting that my supervisors highlighted this to me a number of times during the course of my Ph.D. I will always be grateful for their kind words of encouragement and those reminders of my progress.
Q: Did you have a personal mantra or philosophy that guided you during your journey?
A: Indeed. Do something research-related every day (10 minutes is better than nothing). Also, try to improve yourself daily; the small improvements add up over time.
Achievements and Highlights
Q: What was your proudest moment or achievement during your Ph.D. journey?
A: My proudest moment during my Ph.D. was probably after I had finally devised the contribution portion of my mechanism design paper. I felt quite elated when my supervisors told me I had done real research and that it was good work.
After the Ph.D.
Q: What do you do? How did you reach the place where you are at?
A: After my Ph.D., I took some time to decide what I wanted to do next. Initially, I pursued setting up my own operations consulting business. However, after some initial investigation, I determined it would be best for me to get some industry experience first. Currently, I’m sharpening my machine learning skills and building a data science portfolio to show prospective employers during Fall 2025 recruiting. I consider it a self-funded, self-directed postdoc to transition from academia to industry.
Q: How did completing your Ph.D. change your perspective on the world or your field of research?
A: It was a transformative experience. I learned about fields and subfields I never knew of before, and my entire definition of ``rigour’’ changed. Perhaps, most importantly, I learned the difference between what it means to know of something versus truly knowing something. The latter, which implies mastery, takes a great deal of effort to achieve, though I’d wager it’s worth it, especially for the fundamentals.
Q: Looking back, what do you think were the most important skills or traits that helped you succeed?
A: Perseverance. “You only fail when you quit” is a saying I like. Being honest with where my weaknesses were and working to improve them over time.
Advice for Current and Aspiring Ph.D. Students
Q: If you could go back in time, what advice would you give to your younger self before starting your Ph.D.?
A: Download and use Anki. Anki is a spaced-repetition software, and it makes it much easier to structure the memorization portion of studying. I would tell myself to go through Stewart’s Calculus, Simon and Blume’s Mathematics for Economists, and Bertsekas and Tsitsiklis’s Introduction to Probability and memorize all the definitions, theorems, and proofs. Do all the practice problems.
After starting to do so during the latter part of my Ph.D., I really noticed how my knowledge gain accelerated. Focus on the simplest topic you have not yet mastered, master it, and proceed. Over time, you will be shocked at what you retain and how strong knowledge of the fundamentals carry you forward.
Q: How should students approach finding the right advisor or research topic?
A: This is an interesting question. I think finding the right advisor is challenging, in a vacuum. Look for supervisors who encourage you to interview their current students to ask what it is like working with them. Those supervisors have confidence that, even without their supervision, their students will speak highly of them.
Q: What strategies would you recommend for overcoming challenges and staying resilient?
A: Chalk up those small daily wins. Base your self worth off multiple things.
Q: What tools did you use to stay productive and manage your workload during your Ph.D.?
A: I am a big fan of time tracker software. I think it is very helpful to collect data on your own working habits. With enough observations, you will be able to get a sense for what constitutes a good work day. You ultimately realize that not all hours working are equal.
You may find it hard to get chores done when research feels like an omnipresent task. However, I found it helpful to use chores as a cognitive break. I spent most of my Ph.D. in my apartment during the pandemic, so I would take breaks every hour or two to throw on a YouTube video to listen to while I did some sort of small chore. If you take care of your working and living environs, you will feel better.
Also, I encourage you to experiment with your daily schedule. I found a very early wake up and workout (4:00 AM and 7:00 AM, respectively) seem to work best for me in terms of maintaining my health and fitness, while providing enough time to get my work done. Everyone is different, so find what works for you.
Q: Do you have any books to recommend for students?
A: I don’t tend to like self-help books, but Atomic Habits by James Clear has a lot of actionable advice for building healthy habits. I found it very useful for building habits around exercise, studying, and administration.
Otherwise, I recommend all Ph.D. students make an honest assessment of their knowledge of the fundamentals of their discipline. Master the standard textbook of your field, even if not all of the topics are directly related to your research. It will help you better-understand your colleagues’ research and potentially lead to fruitful collaboration. It will also boost your confidence.
Q: Any last advice to students who are currently pursuing or considering a Ph.D.?
A: Do not be shocked when people do not understand what a Ph.D. entails or the value of doing it. Economically, it is not necessarily the best choice. That is, of course, highly dependent on your chosen path, as, even in industry, many research positions explicitly require a Ph.D.
I don’t believe most reasons for doing a Ph.D. could be inherently wrong; I think the important thing is to be honest about why you’re getting one. I wanted to pivot into more quantitative work and to push myself further academically. I didn’t fully understand what research entailed when I started, but I am glad that I can now call myself a researcher.
