Studying uncertainty: Computing scientist uses algorithms to tackle real-world problems

New Assistant Professor Xiaoqi Tan joins the University of Alberta and shares his research and thoughts on the importance of teaching.

Andrew Lyle - 29 October 2021

 Xiaoqi Tan, new assistant professor in the Department of Computing Science

Xiaoqi Tan, new assistant professor in the Department of Computing Science.

In an increasingly interconnected world, it can be difficult to model — and improve — some of the most complex systems of our society. Xiaoqi Tan, new assistant professor in the Department of Computing Science, focuses on algorithms to solve just these sorts of problems 

Tan first became interested in the field when he realized that a core challenge of integrating renewable energy resources into our power grid is their intermittency. Solar panels and wind power can fluctuate in their generation of electricity, and this uncertainty demands advanced algorithms to help balance these integrated systems. 

As Tan explains, managing uncertainty demands an interdisciplinary approach that works hand-in-hand with economics, operations research, and engineering — while making use of advanced computational tools.

Hear from Tan as he shares his research, real-world applications of his research including renewable energy and electric vehicles, and his philosophy on teaching students and helping them succeed.

What brought you to the University of Alberta?

The University of Alberta is one of the best universities in Canada and has a reputation for excellence in research and teaching. We have so many world-leading scientists, researchers, and staff across a wide spectrum of disciplines. This not only attracts the best students to come and study here, but also makes people proud to work here and be part of this great community. During my two-day interview, I was very impressed by the collegial atmosphere and positive environment within the department and the faculty.

Tell us about your research program.

I study the interplay between algorithms, decisions, and uncertainty in networked systems — especially in highly dynamic, strategic settings where multiple agents come together, interact and possibly pursue divergent or even conflicting objectives. The goal is to develop new decision-making tools and algorithms that can provide provable performance guarantees under different forms of uncertainty, using mathematical tools from computer science, optimization, economics, and control.

What questions does your research aim to answer?

On the theoretical side, the core question my research strives to answer is: What are the best possible algorithms we can design when facing uncertain inputs? On the practical side, my recent research is primarily driven by problems related to energy and urban sustainability. For example, how do we bring more renewable energy resources into our power grid given that their minute-to-minute power generation can vary significantly? What kind of challenges and opportunities do we face if all vehicles become electrified, connected, or even autonomous in future cities?

Managing uncertainty is challenging yet important in every aspect of our society. We are unlikely to find good answers to these questions if we don’t integrate tools from various disciplines such as computer science, economics, operations research, and engineering. This is also what makes the research so exciting, as I truly believe that interdisciplinary research is the best way to tackle some of the most pressing global challenges we are facing today.

What courses are you teaching?

I am currently teaching CMPUT 675: Optimization and Decision Making under Uncertainty. In this course, students learn an overview of recent developments and future research directions in the general field of algorithms and optimization under uncertainty. In Winter 2022, I will teach CMPUT 204: Algorithms I. CMPUT 204 is the first of two courses on algorithm design and analysis, with emphasis on fundamentals of searching, sorting, and graph algorithms. 

Why is teaching important to you?

Teaching is the best way to learn — to learn in a systematic way, and often brings new ideas and perspectives to my research. I strongly believe that teaching a course is not just about providing students with the “static” knowledge of a specific topic, but more about the “dynamic” process of expanding the depth of understanding of a field.

This requires a systematic understanding of both the course material and more importantly, how this course is connected to the broader educational goal of that field. This pushes you, as an instructor, to never stop learning and to constantly update your knowledge to continue to teach your students effectively. In the end, it’s all about helping students succeed.

Anything else you'd like to add?

I had never been to Edmonton before and the moment I got here I was amazed by the magnificence of the river valley. I was told that winter is cold here, but hopefully my three-year training in Toronto is enough to let me pass the test.