Research data scientist and machine learning engineer; background in experimental physics, signals analysis, and software engineering; professional experience in technical consulting, business process automation, and data analytics; a passion for generative learning models and computational optimisation.
My Grants
- Australian Renewable Energy Agency (ARENA) R&D Project Grant, "Machine learning applications for utility-scale...view more
Research data scientist and machine learning engineer; background in experimental physics, signals analysis, and software engineering; professional experience in technical consulting, business process automation, and data analytics; a passion for generative learning models and computational optimisation.
My Grants
- Australian Renewable Energy Agency (ARENA) R&D Project Grant, "Machine learning applications for utility-scale PV", RG220681, Dec 2022
- Australian Centre for Advanced Photovoltaics (ACAP) Industrial Collaboration Grant, "End-of-Life Classification of Solar Panels", Mar 2022
My Qualifications
- Ph.D. Physics (2018)
Intelligent Polymer Research Institute, University of Wollongong
Major: Physics of Excited-States in Condensed-Matter
Supervision: Prof. Attila Mozer & Assoc. Prof. Tracey Clarke
Thesis: ”The driving force dependence of charge carrier dynamics in donor-acceptor organic photovoltaic systems using optical and electronic techniques”
- B. Nanotechnology Honours (2011)
School of Chemistry and Molecular Bioscience, University of Wollongong
Major: Computational Chemistry
Supervision: Assoc. Prof. Haibo Yu
Thesis: ”Development of a polarisable force field for Nafion”
My Awards
- Winning Entry, 1st International Eurovision AI Song Contest, hosted by VPRO, Netherlands, May 2020; ”Beautiful the World” available at youtu.be/sAzULywAHUM; Australian team as collaboration between academics at RMIT and UNSW, and industry partner Uncanny Valley; personal contribution included developing raw audio and song structure generation capabilities with learning algorithms.
My Research Activities
Current research at UNSW focuses on the study of electrochemical defect mechanisms in silicon photovoltaics; modelling state-space dynamics using generative representation learning to investigate underlying physical mechanisms; more broadly, utilising recent developments in machine learning to model complex dynamic systems.
My Research Supervision
Areas of supervision
- Development of generative representation learning systems to model complex dynamics of physical systems from experimental characterisation data, focusing on excited-state physics within photovoltaic systems (charge-carrier generation, transport, recombination
Currently supervising
- Chukwuka Madumelu; secondary supervision of doctoral candidate in photovoltaic engineering, University of New South Wales, 2019-2022
https://orcid.org/0000-0002-3345-5912