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. Much of my recent work falls into three closely related directions:

• Online Algorithms — designing algorithms with provable guarantees when decisions must be made sequentially without knowing the full input in advance.
• Algorithmic Economics — studying resource allocation, incentives, fairness, and strategic interactions in systems with multiple agents.
• Learning Theory — understanding what can and cannot be learned from different forms of information, especially in online and sequential decision-making problems.

Across these directions, I am broadly interested in how algorithmic performance depends on the structure of the decision problem, the uncertainty in the environment, and the information available to the decision maker. These questions naturally bring together ideas from computer science, economics, statistics, and control, and often arise in systems involving interacting people, institutions, and computational agents.

What I Care About the Most in Research?

I value the beauty of mathematics and believe that technically sound, conceptually simple, and aesthetically elegant results often form the foundation of lasting scientific contributions. At the same time, I am most drawn to problems that connect deep theoretical questions with meaningful real-world systems.

In particular, I enjoy identifying simple abstractions that reveal new algorithmic ideas, clarify structural trade-offs, and help explain how complex decision systems behave under uncertainty. My work therefore sits at the intersection of theory and systems. I think of this style of research as systems-oriented theory: mathematically rigorous work 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 am particularly interested in students who enjoy mathematical thinking and want to understand not only how to build algorithms, but also why they work, when they fail, and what fundamental limits govern them.

Much of our work involves two complementary skills: turning real-world decision problems into clean mathematical models, and developing algorithms with provable guarantees. You do not need to arrive as an expert in online algorithms, economics, machine learning, or systems. What matters more is curiosity, comfort with abstraction, and a willingness to develop strong foundations in mathematics and computer science. If you are unsure whether this style of research is for you, one useful question is whether you have genuinely enjoyed courses such as probability, linear algebra, algorithms, theory of computation, optimization, or other mathematically oriented subjects. If the answer is yes, that is usually a good sign.

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 from a range of backgrounds who are genuinely interested in algorithms, mathematical thinking, machine learning, or systems research are encouraged to apply. Prior experience in areas such as competitive programming, mathematics competitions, or independent technical projects can certainly be helpful, but it is by no means required. If you are interested, please follow the instructions below to email me your CV, transcript, and a brief 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 provides the opportunity to engage more deeply with the research process through activities such as guided study of advanced materials, learning how to read and present research papers, and gradually exploring different research directions to discover the kinds of problems and styles of thinking resonate most with you.

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: Sept 1, 2026