ORCID as entered in ROS
https://orcid.org/0000-0002-2934-4478
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Conference Papers
, 2024, 'Contactless external quantum efficiency evaluation of perovskite solar cells', Seattle; USA, presented at
, 2024, 'Deep learning-assisted extraction of photoluminescence and series resistance images from a single electroluminescence measurement', Seattle, USA, presented at
, 2024, 'Extracting the parameters of two-energy-level defects in silicon wafers using machine learning models', Seattle, USA, presented at
, 2024, 'Outdoor photoluminescence imaging of perovskite-based photovoltaic devices', Seattle; USA, presented at
, 2024, 'Spatially resolved characterisation of large-area perovskite modules', Seattle, USA, presented at
, 2024, 'Photovoltaic soiling from particulate matter across Australia', Canberra, Australia, presented at
, 2023, 'Automated photovoltaic module quality assessment from luminescence images: defect classification using unsupervised machine learning', Melbourne, Australia, presented at
, 2023, 'Advanced analysis of internal quantum efficiency measurements of GaAs solar cells using machine learning', Melbourne, Australia, presented at
, 2023, 'AI-driven extraction of spatially-resolved photoluminescence and series resistance from electroluminescence images', Melbourne, Australia, presented at
, 2023, 'Distortion correction for real-world solar panel images based on deep learning', Melbourne, Australia, presented at
, 2023, 'Enhancing luminescence images through deep learning-based point spread function correction', Melbourne, Australia, presented at
, 2023, 'Implied voltage images of each subcell in perovskite/Si tandem solar cells using luminescence measurements', Melbourne, Australia, presented at
, 2023, 'Photovoltaic module performance and degradation analysis', Melbourne, Australia, presented at
, 2023, 'Temperature-dependent performance of 50 µm thick silicon heterojunction solar cells for space applications', Melbourne, Australia, presented at
, 2023, 'Using latent ordinary differential equation neural networks to predict the degradation of heterojunction PV modules at the end of damp heat tests', Melbourne, Australia, presented at
, 2023, 'Automated photovoltaic module end-of-life assessment', UNSW Sydney, Australia, presented at
, 2023, 'Luminescence-based implied voltage imaging of tandem solar cells via bandpass filter method', Shenzhen, China, presented at
, 2023, 'Ionic compounds for electron extraction in low-temperature-processed crystalline silicon solar cells', Lisbon, Portugal, presented at
, 2023, 'Novel method for the extraction of the implied voltages of silicon wafers and solar cells from luminescence-based measurements', Lisbon, Portugal, presented at
, 2023, 'Surface saturation current densities of perovskite thin films from suns-photoluminescence quantum yield measurements', Lisbon, Portugal, presented at
, 2023, 'Advanced production line monitoring with time-lag sequential analysis', in 2023 IEEE 50th Photovoltaic Specialists Conference (PVSC), Institute of Electrical and Electronics Engineers (IEEE), Puerto Rico, pp. 1 - 1, presented at
, 2023, 'Automated analysis of internal quantum efficiency measurements of GaAs solar cells using machine learning', in Conference Record of the IEEE Photovoltaic Specialists Conference, Institute of Electrical and Electronics Engineers (IEEE), Puerto Rico, pp. 1 - 3, presented at
, 2023, 'Decoupling open-circuit voltage and series resistance in electroluminescence images through deep learning', in 2023 IEEE 50th Photovoltaic Specialists Conference (PVSC), Institute of Electrical and Electronics Engineers (IEEE), Puerto Rico, pp. 1 - 1, presented at
, 2023, 'Dynamic calibration of injection dependent carrier lifetime from time-resolved photoluminescence', in 2023 IEEE 50th Photovoltaic Specialists Conference (PVSC), Institute of Electrical and Electronics Engineers (IEEE), Puerto Rico, pp. 1 - 1, presented at
, 2023, 'Automated photovoltaic module quality assessment: defect identification and classification from luminescence images using machine learning', Puerto Rico, presented at
, 2023, 'Predicting damp heat degradation in heterojunction modules using machine learning', Puerto Rico, presented at
, 2023, 'Predicting damp heat degradation in heterojunction modules using machine learning', Puerto Rico, presented at
, 2023, 'Temperature-dependent performance of ultra-thin silicon heterojunction solar cells for space applications', Puerto Rico, presented at
, 2023, 'Implied voltage imaging of solar cells and cell precursors: single bandpass filter method', Delft, The Netherlands, presented at
, 2023, 'Lightly doped silicon heterojunction solar cells in relevant field conditions of illumination and temperature', Delft, the Netherlands, presented at
, 2022, 'A deep learning approach to denoise luminescence images of solar cells', Newcastle, Australia, presented at
, 2022, 'Extracting the parameters of two-energy-level defects in silicon wafers using machine learning', Newcastle, Australia, presented at
, 2022, 'Evaluation of variations in solar cell production lines using time-series electroluminescence images', Newcastle, Australia, presented at
, 2022, 'Machine learning-powered module end-of-life decisions from luminescence images', Newcastle, Australia, presented at
, 2022, 'Machine learning-powered module end-of-life decision making based on luminescence images', Nagoya, Japan, presented at
, 2022, 'Modelling the dynamics of charge extraction transients from organic photovoltaic devices using generative representation learning', Nagoya, Japan, presented at
, 2022, 'A hybrid empirical and machine learning method to estimate solar cell fill factor from luminescence images', Nagoya, Japan, presented at
, 2022, 'Accelerating the solar energy transition through artificial intelligence', Nagoya, Japan, presented at
, 2022, 'Investigating the performance of non-standard cell configurations for reconfigurable
solar modules under varying shading conditions', Nagoya, Japan, presented at
, 2022, 'Quality assessment of photovoltaic manufacturing lines through lag-sequential analysis', Nagoya, Japan, presented at
, 2022, 'Super-resolution luminescence imaging of solar cells using deep learning', Nagoya, Japan, presented at
, 2022, 'Photoluminescence imaging using non-uniform excitation: Recent progress', Milan, Italy, presented at
, 2022, 'Photodoping causes inconsistencies in the injection-dependent lifetimes of perovskite thin films', in 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC), Institute of Electrical and Electronics Engineers (IEEE), Philadelphia, USA, pp. 0368 - 0368, presented at
, 2022, 'A deep learning approach to increase luminescence image resolution of solar cells', in 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC), Institute of Electrical and Electronics Engineers (IEEE), Philadelphia, USA, pp. 329 - 329, presented at
, 2022, 'Automated analysis of internal quantum efficiency using chain order regression', in Conference Record of the IEEE Photovoltaic Specialists Conference, Institute of Electrical and Electronics Engineers (IEEE), Philadelphia, USA, pp. 476 - 478, presented at
, 2022, 'Contactless determination of emitter sheet resistance for diffused silicon wafers', in 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC), Institute of Electrical and Electronics Engineers (IEEE), Philadelphia, USA, pp. 0367 - 0367, presented at
, 2022, 'Fill factor prediction of modern industrial cells: Potential gaps and improvements', in 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC), Institute of Electrical and Electronics Engineers (IEEE), Philadelphia, USA, pp. 0353 - 0353, presented at
, 2022, 'Silicon heterojunction solar cells with high bulk resistivities over 1,000 Ω·cm in relevant field conditions of illumination and temperature', in 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC), Institute of Electrical and Electronics Engineers (IEEE), Philadelphia, USA, pp. 1045 - 1045, presented at
, 2022, 'Temperature dependence of silicon-dielectric interface recombination', in 2022 IEEE 49th Photovoltaics Specialists Conference (PVSC), Institute of Electrical and Electronics Engineers (IEEE), Philadelphia, USA, pp. 0443 - 0443, presented at
, 2022, 'Using machine learning to predict the complete degradation of accelerated damp heat testing in just 10% of the time', in Conference Record of the IEEE Photovoltaic Specialists Conference, Institute of Electrical and Electronics Engineers (IEEE), Philadelphia, USA, pp. 0472 - 0474, presented at
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https://orcid.org/0000-0002-2934-4478