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Insights at UBC Sauder

Meet UBC Sauder’s new faculty – Joseph Paat

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Posted 2020-10-27
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At UBC Sauder, faculty members are more than just ‘professors.’ They conduct impactful research that is changing how society views the world while also inspiring students to pursue their academic passions and become the thoughtful, values-driven leaders the business world needs. This year, UBC Sauder welcomes seven new full-time lecturers, tenured and tenure-track faculty to the school. In the second of this series, we introduce you to Joseph (Joe) Paat, Assistant Professor, Operations & Logistics Division, UBC Sauder School of Business.

What brought you to UBC Sauder? 

I would have to say the faculty in the Operations and Logistics Division. Everyone in the division makes a genuine effort to form a community. The group also has many members who have made strong contributions in my field of research. I look forward to learning from them and developing new collaborations.

What are your areas of research and how did you get into this field? 

It is often the case that a decision is difficult to make because we can only select from a discrete set of choices rather than an entire spectrum. This occurs, for instance, when we are forced to associate with a group rather than be allowed to identify somewhere in-between. I am interested in methods for solving models in operations management that involve discrete decision variables. These models have many applications in healthcare (how do we assign patients to operating rooms?) and transportation (what is the fastest route to my destination?), among others. However, rather than consider a particular application, I focus on techniques that can be applied to generic models. I come from a mathematics background, and during my studies I looked for applications of what I was taught. I find optimization to be an exciting area that connects techniques from mathematics to relevant questions in industry.

What fuels your research – what prompted you to research this area?

As a business grows, so too do the models that represent its operational problems. The advent of `big data' and advances in technology has led to a recent surge in large-scale models. Unfortunately, for most discrete models, algorithmic performance does not scale well with the size of the model. This leads to the main question motivating my work: Are there data parameters, other than the amount of data, that influence how quickly a model can be optimized? Ideally, such parameters govern relationships in the model data. By understanding influential data parameters, we look to expand the list of large models that are efficiently solvable.

What inspires you to teach? 

My list of favorite teachers is composed of those who guided me onto my career path. Of course, the list includes professors who brought me into my area of research. However, also on the list are those individuals who taught me soft skills such as how to present, how to create a welcoming environment, and what's meaningful in the long run. I am inspired to teach because it gives me an opportunity to help students achieve their own goals.

What’s the most interesting thing you’ve discovered through your research?

Generally speaking, an optimization model becomes more difficult to solve as the number of discrete variables increases. Through a long line of work, we were able to understand how well 'difficult' models can be approximated using simpler models with fewer discrete variables. This direction of work includes methods for reformulating general models so that fewer discrete-valued decisions need to be made. 

What do you believe is the future of your industry? 

In many industries, classic tools begin to break down when overloaded. This is also the case for optimization algorithms: If the data set is too large, then the algorithm slows to a halt and the model becomes hard to control. Over the past decade, companies have used large-scale data analysis to drive their operations. I believe this will be a driving force in the future, and we will need new methods for finding a solution (either exact or approximate) to large-scale models.

What are you most looking forward to in Vancouver?

I have a long list of nature walks to take and sites to see. However, at this point I am most looking forward to the diverse cuisine that Vancouver has to offer. I am open to any suggestions!