Skip to main content

Meet the students pursuing their passions through a PhD

8 September 2021

Building a rewarding career through research

From bionics to machine learning, these engineering PhD candidates have taken their passions to the labs, and their research stands to have long lasting impact. 

Using bionics to restore blinking

Jacinta Cleary is working with Professor Gregg Suaning, Head of the School of Biomedical Engineering, to improve quality of life for people with facial paralysis.

After completing her undergraduate degree in biomedical engineering and neuroscience, Jacinta knew that she wanted to move into the field of bionics. 

“I started working as a research assistant in the bionics lab, and really enjoyed the work. 

“When I was given the opportunity to study a PhD, I thought it would be the perfect opportunity to further develop the skills which will help me build a career in the bionics field.”

Now Jacinta is working on a medical implant to restore blinking for people with facial paralysis.

“Being unable to blink is one of the most critical impacts of facial paralysis. Not only can it cause irreparable damage to a person’s eye when they are continually dry and irritated, it also impacts their ability to participate in society as we rely on eye contact and facial expressions as a major form of nonverbal communication.

To address this, Jacinta’s team are designing an implant that will not only restore the function of blinking, but will restore the natural movement of the eye, as a holistic solution to this problem. The team are currently undergoing pre-clinical trials.

The implant ready for preclinical study

The implant ready for preclinical study

An environmentally friendly lithium extraction method

Yanxi Yu

Yanxi Yu is a PhD student working with Professor Yuan Chen in the School of Chemical and Biomolecular Engineering. She was led to study a PhD because of her passion to use knowledge to create a more sustainable world.

Yanxi aims to address the growing demand of lithium-ion batteries, as the traditional extraction process of lithium is resource-demanding and time consuming. Her research seeks to use an environmental-friendly, electrochemical method to extract lithium ions from seawater or salt-lake brine. 

“Growing demand of lithium-ion batteries will exhaust the remaining lithium resources, and conventional lithium mining methods are inefficient and polluting. My research aims to develop a sustainable and energy-saving technology to extract lithium from green sources.”

Though Yanxi began studying a PhD to create technologies that can build a greener world, she has since gained some additional skills.

"Studying a PhD has helped me gain professional skills like problem-solving and analytical abilities, and has also trained me mentally."

The electrochemical system for lithium extraction

The electrochemical system for lithium extraction

Advancing reinforcement learning technology

Yinmin Zhang

Yinmin Zhang is conducting research into reinforcement learning alongside Associate Professor Wanli Ouyang in the School of Electrical and Information Engineering.

Reinforcement learning is an area of machine learning often adopted in in AI technology that allows for unsupervised machine learning through rewards and penalties. 

However, there are challenges in reinforcement learning which means that its adoption and application are still limited. Yinmin is working to address these challenges.

“There are two major problems in reinforcement learning: first, it is difficult to standardise reinforcement learning problems, because it involves multi-modal data inputs, cross-scale calculation, and multi-domain algorithm fusion”, said Yinmin.

“Also, the reinforcement learning algorithm data flows of single-machine and multi-machine, or even cross-cluster computing, are completely different.”

To address these challenges, Yinmin supported the development of the DI-engine: a generalised Decision Intelligence engine that supports most basic deep reinforcement learning (DRL) algorithms, which could help standardise problems and advance reinforcement learning research.

“Reinforcement learning has produced promising results in fields ranging from robotics control to strategy games and recommendation systems. I am passionate about reinforcement learning algorithms and want to delve further into offline reinforcement learning so we can see it continue to be adopted in real-life situations.”