Course: Bachelor of Science (mathematics and physics)
Supervisor: Hugh Geaney, Michela Ottaviani, Mehakpreet Singh
Name of Research Project/Activity: Efficient algorithms based on machine learning for state-of-health and state-of-charge in high energy density lithium-ion batteries.
Q) Can you tell me a bit about yourself, your background, and why you decided to study the course you are taking at UL?
I am an Australian student completing the final year of a three-year bachelor of science majoring in mathematics and physics. I am inspired by the various approaches to scientific thought and the philosophy of science. UL provided an excellent opportunity to meet staff and students from many corners of the world, allowing me to glimpse the range of styles of mathematics and physics. I spent a year on exchange at UL studying several mathematics and physics courses under the brilliant instruction of the institute’s great physicists and mathematicians.
Q) What motivated you to apply for the Summer Bursary Programme?
After conversations with the members of the mathematics and physics faculty, I felt the strong encouragement and supportive culture that exists at UL. This great sense of keen collaboration and the inspiring intelligence of the department members truly motivated me to engage in a research programme with my supervisors. I was extremely eager to experience the role and duties of a researcher in mathematics and physics and to test my academic skills in a real scenario. The Summer Bursary Programme gave me the opportunity to fulfil these goals and desires and offered even more.
Q) What are you doing as part of your research here at UL?
I am investigating the different kinds of artificial neural networks (ANNs) and applying them to model battery states in Li-ion batteries. I am showing that a Li-ion battery's state of charge (SOC) and capacitance fade can be modelled and predicted using a few specific variables. I am using Matlab to build this model from scratch and investigating the different aspects of the model and how they can improve the accuracy.
Q) What skills have you developed over the summer?
I learned how to construct an ANN entirely from scratch in Matlab and explored the functions and variables of the models. I investigated different ways to manipulate large files of data to minimise hyperparameters and fit the data to my model and vice versa. I now understand how a neural network can be modelled in a computer programme and how it maps inputs to outputs on the scale of individual synapses. I learned the theoretical foundations of neural networks and activation functions from statistical and pure mathematical perspectives and how these functions have been derived. I have also improved my skills in individual and guided research/education.
Q) What has this experience taught you and what would you recommend it to others?
It has taught me the value of my discipline and ability to self-guide my learning. I have developed a great sense of what a research position may entail on a day-to-day basis and the kinds of struggles one can face in these roles. I found greater importance in the use of collaboration and the sharing of thoughts/ideas to help find a solution to obstacles and problems. I would highly recommend the programme to others as an opportunity to test the field of research as a possible career path and to develop the key skills required to pursue and succeed in it.
Q) What are your future career plans, would you consider a career in research?
My future plans involve attaining a PhD in mathematics and to pursue a career in research. I hope to explore as many fields of mathematics as possible before settling on one or a few to focus my research on. I also plan to investigate the different approaches to mathematics taught around the world and to glimpse the impact that philosophy has on the production of mathematics.