Hi! Thank you for stopping by!

I am Xiaoqi Tan, an Assistant Professor at the University of Alberta and also a Fellow of the Alberta Machine Intelligence Institute (Amii). Below, you will find my core values in research and mentoring, as well as my perspective on some common inquiries from prospective students. I hope you find this information useful.

What is My Research?

I study algorithms for decision-making under uncertainty. Specifically, my recent work concentrates on three directions:

Collectively, my research across these directions aims to understand how to navigate two major sources of uncertainty in decision making: (i) sequential uncertainty, inherent to arguably all temporal decision processes; and (ii) strategic uncertainty, stemming from the behavior and incentives of interacting agents. Addressing these complexities calls for an interdisciplinary approach that integrates techniques from computer science, economics, statistics, and control.

What I Care About the Most in Research?

I value the beauty of mathematics and view technically sound, aesthetically elegant theorems as the foundation of enduring scientific contributions. While I admire strong applied research, I believe the deepest theoretical ideas often outlast the specific systems that first motivate them.

At the same time, my goal is not to pursue theory in isolation. I am most drawn to problems that are both intellectually deep and connected to meaningful real-world systems. In particular, I am interested in identifying simple yet fundamental abstractions that reveal new algorithmic ideas, clarify structural trade-offs, and guide the design of practical decision-making systems operating under uncertainty. My work therefore lies at the intersection of theory and systems. Rather than viewing these as opposing directions, I see theoretical abstraction as a tool for understanding the principles governing increasingly complex real-world platforms, including online markets, cloud systems, and modern AI infrastructure such as LLM inference systems.

For this reason, I think of my research not simply as algorithmic nor systems research in the classical sense, but more specifically as systems-oriented theory: mathematically rigorous research inspired by the structural challenges of real-world decision systems.

What is My Take on Mentorship and the Advisor–Advisee Relationship?

I respect scholarship and love research, like most academics do. I consider getting a graduate degree takes initiative and commitment — it requires strong motivation to excel, long-lasting enthusiasm in research, and probably most importantly, a good advisor-advisee match — based on mutual trust and respect, open and effortless communication, and sometimes, a bit of luck. While it is complex to define what is exactly a “good match,” a simple rule of thumb is: if you feel this is the person you are willing to “work with,” not to “work for,” then it is usually a good sign.

I consider it a privilege to mentor students, and I feel genuinely fortunate to work with them during some of the most vibrant and formative years of their academic journeys. At the same time, I’m also humbled by the responsibility that comes with this role. I once came across a reflection by a mathematician (whose name, regrettably, I can no longer recall) that I now keep as a quiet reminder on my desk: “There are moments when I take pride in my work, only to pause and question whether I’ve mistaken mediocrity for merit — what seems admirable to me may, in the end, hold little value. What I fear far more, however, is the possibility of unknowingly leading my students down the wrong path.” That fear, while humbling, has also deepened my appreciation for the advisor–advisee relationship. At its best, it is not a hierarchy, but a partnership — grounded in trust, mutual respect, and honest dialogue. Such a relationship can act as a safeguard, helping both mentor and mentee stay grounded, reflective, and open to growth.

What I Care About the Most in Prospective Students?

I often receive emails from prospective students with descriptions like “I know how to use $X$ to implement $Y$.” While this is undoubtedly a valuable skill, it is not my primary focus in my research. I am looking for students who are interested in (i) converting real-world problems into rigorous mathematical models (i.e., modeling) and (ii) developing algorithms to solve these problems (i.e., computation) with provable guarantees — in the form of mathematical theorems and lemmas. In short, I look for prospective students who are motivated and excited about creating new knowledge to explain “how and why things work — or why they don’t."

A frequently asked question by undergraduate and early-stage graduate students is: mathematical proofs can seem daunting; how can I determine if I will enjoy them? While there isn’t a one-size-fits-all answer, a reasonable approach is to ask yourself: Do I have an affinity for subjects like calculus, probability, linear algebra, and other math or theory-based courses (e.g., algorithm design and analysis, theory of computation, etc.)? If your answer is a clear and enthusiastic yes, and you’ve had positive experiences with most of these courses, then it’s a promising indicator!

Am I looking for New Students?

Yes! I am always looking for motivated students at all levels (undergraduate, MSc, and PhD) to join my SODALab @UofA.

For prospective undergraduate students: Undergraduate students may join my lab through various channels, such as NSERC USRA and URI @UofA. Students with experience in competitive programming and math competitions are particularly encouraged to apply. If interested, please follow the instructions below to email me your CV, transcript, and statement of interest. It is especially helpful if you specify in your email whether you are seeking a full-time summer internship or are interested in a longer-term, formal research commitment. The latter is generally preferred, as it offers the opportunity to engage in multiple components of a comprehensive training pipeline — such as guided study of graduate-level materials (e.g., through individual study courses), learning how to read and present research papers, and, ideally, exploring different directions that may help you discover what truly excites you in your future research.


For prospective graduate students (MSc/PhD): Please directly apply here and indicate me as your potential supervisor. If you do not hold a Master’s degree, please note that the typical path in Canada is to pursue a thesis-based Master’s degree first, followed by a PhD. This generally takes about 2 + 4 years. Direct entry into a PhD program, which usually lasts 5–6 years, is less common. It is also worth noting that thesis-based Master’s programs in Canada are often fully funded — essentially functioning like a “mini-PhD” in both research intensity and financial support. For example, thesis-based MSc students at the University of Alberta receive full funding throughout their two-year programs.

How to (Effectively) Write Me an Email about Your Application?

If you decide to write me an email and want to initiate an effective conversation about your application, please attach the following documents as three separate PDF files: (i) your CV, (ii) your full academic transcript, and (iii) a Statement of Interest (1–2 pages). In your statement, please begin by confirming that you have carefully reviewed this page. Then briefly summarize your prior research experience (if any), outline your future research interests, and explain why you would be a good fit for my group. If you have any publications, please highlight the one you are most proud of and briefly summarize your contribution to that work. You may also wish to share your longer-term goals, especially if you are considering a PhD (e.g., pursuing an academic career or working in industry).

Due to the volume of emails, I may not be able to reply to everyone individually, but I truly appreciate the time you take to review this page before reaching out. If you are already at UofA, please feel free to reach out if you’d like to chat.


by Xiaoqi Tan | Last updated: May 1, 2026