Yue Liu is pursuing a DPhil at the University of Oxford, working within the Mathematical Institute on biological system modelling. His work focuses on developing and applying new methods that can harness the complementary powers of both mathematical modelling and machine learning.
In combining these approaches, which historically have been viewed distinctly, and even as competitors, Yue is exploring a novel approach to studying biological systems that builds on the strengths of both methods. Through this research, he hopes to contribute to solving many long-standing questions in biology that are increasingly relevant in the context of the COVID-19 pandemic, such as finding better ways to control the spread of infectious diseases and improving cancer therapies.
His focus lies in developing machine learning techniques for surrogate mathematical models that allow for the application of efficient control methods. Biological phenomena are hugely complex, often arising through nonlinear processes over vast spatio-temporal scales, making them incredibly difficult to understand using experiments and verbal reasoning. In order to achieve a desired outcome through controlling certain inputs to the system, we must understand the driving mechanisms and how the control of inputs impacts on their dynamics. For example, in cancer we need not only to understand how tumours grow and spread, but also how to eliminate the cancer with a minimal dosage of, e.g. chemotherapy. For diseases such as COVID-19, we must understand both how the disease spreads as well as how interventions (lockdown, mask wearing, travel restrictions, etc.) impact the rate at which the disease is transmitted.
Prior to his DPhil, Yue completed a Master of Science at University of British Colombia. Looking forward, he intends to pursue a career in academia in Canada, where mathematical modelling is an increasing focus, in part due to the COVID-19 pandemic. Understanding and developing the ability to effectively manipulate such models will lead to further advances in biology and medicine which can support Canada’s recovery from the long term effects of the COVID-19 pandemic.
