"The students in my lab would gain skills in programming, parallel computing, using high performance computers and becoming familiar with machine learning packages."
- Program:
- Campus: Vancouver
Position: Associate Professor
Education: BE (Zhejiang University), PhD (Purdue University)
What led you to engineering?
I was always very interested in math, and from a young age I was excited about how math could be applied to solve real-world problems.
Although I completed my undergraduate degree in biological engineering, over the course of my studies I realized my talents were more on the theoretical side.
During my PhD I focused on solving optimization and control problems.
Chemical and Biological engineering
Why did you decide to focus your career on research?
While I was a student, I kept wanting to solve deeper and more complex problems. During my PhD, I completed internships in industry for several companies, including United Airlines and Air Products. But I realized I wanted to work on more fundamental problems where my solutions could potentially have a broader impact.
For example, if I can propose an algorithm that solves a complex optimization challenge, it can benefit companies across multiple industries, rather than just one specific company.
Tell us about your research.
My research focuses on the design and implementation of large-scale optimization algorithms that can be used in a wide range of applications to help people make better decisions when there are a lot of competing objectives.
The work that I am doing has implications for many different industries, but is generally under the umbrella of chemical or biological engineering where I need to understand the material constraints of a system.
Read how e ngineering offers rewarding careers
I develop computational tools to design energy systems to be more sustainable or control those systems so they are more sustainable.
Here’s one example. Imagine you have a chemical plant. You need to operate the plant to manage the mass balance and energy balance. You likely want to minimize costs and maximize revenues. And you will also want to achieve other objectives, like minimizing CO2 emissions or other waste.
Fundamentally, we can formulate this as a mathematical problem. The algorithms I develop aim to do this.
What are the applications of your research?
This research is being used in many different areas, including energy system design and power management.
One interesting project underway by one of my post-doctoral fellows is on biomass conversions.
BC forestry operations tend to leave behind a large amount of forest residuals – like branches and needles that have been traditionally considered waste. This is an issue as it means forestry operators aren’t maximizing the value of the wood they are harvesting and the residuals are also a wildfire risk.
One solution has been to move the biomass from the forest to a centralized facility for processing into a value-added product, like a fertilizer or biofuel. But this too has its challenges. Forestry operations are located in remote areas and the transportation costs to move the biomass to a centralized facility are huge.
What my postdoc is proposing is to move the factory to the forest: bringing in small-scale reactors to convert the biomass into a usable product.
Our research then focuses on determining how to make this process as efficient as possible in terms of the size of the reactor, optimizing it so that it is as efficient as possible regardless of the type of input.
My postdoc created a company, Takachar, that is doing this work. Takachar has won lots of awards, including the Earthshot Prize and an award from the Musk Foundation, and the company has implementations in India and Kenya.
Another application of this research is some work I am doing for BC Hydro. BC will need to increase its electricity production to meet the growth in demand as a result of electrification of many industries.
Questions like “how do we generate more electricity in a clean way” or “how can BC Hydro manage their water resources more smartly” are ultimately optimization problems.
What are some of the challenges you face in your research?
The models can be quite complicated. And in all models, there are challenges in ensuring that you have clean data to work from.
It’s also very important to come up with ways to share the output of the models in ways that it can be understood and used by others. Including non-technical decision-makers.
Do undergraduates work in your lab?
Definitely! I often have a group of undergraduate students working with me or the graduate students in the Cao Research Group. They are often involved in machine learning projects where they are provided with data that they test on different machine learning packages and then assess how accurate the resulting neural network is.
I have had several undergraduate students publish their first authored papers based on research. The students in my lab would gain skills in programming, parallel computing, using high performance computers and becoming familiar with machine learning packages.
Why should students choose UBC?
UBC has a very strong reputation in sustainability, with many faculty working on cutting-edge energy applications or working on sustainable energy systems. It’s an exciting place to study!
