Researcher

Dr Tien-Chun Wu

My Expertise

My research spans the field of AI, data science, renewable energy, smart cities, and semiconductor technology. I currently employ spatiotemporal energy supply/demand modelling and forecasting, multimodal generative representation learning, and large language models to create innovative AI-driven solutions for sustainable energy challenges.

Fields of Research (FoR)

Time series and spatial modelling, Deep learning, Semi- and unsupervised learning, Natural language processing, Machine learning, Data engineering and data science, Photovoltaic power systems

Biography

Dr. Tien-Chun Wu is a research fellow under the ARENA project, where he develops machine learning models for utility-scale photovoltaic systems. Prior to joining UNSW, he was a postdoctoral fellow at the Massachusetts Institute of Technology (MIT), where he developed deep learning models to forecast spatiotemporal traffic and electricity demands, and optimise electric vehicle charging infrastructure. He also worked on large language models...view more

Dr. Tien-Chun Wu is a research fellow under the ARENA project, where he develops machine learning models for utility-scale photovoltaic systems. Prior to joining UNSW, he was a postdoctoral fellow at the Massachusetts Institute of Technology (MIT), where he developed deep learning models to forecast spatiotemporal traffic and electricity demands, and optimise electric vehicle charging infrastructure. He also worked on large language models for machine-generated logs. He holds a PhD in Electrical Engineering from the University of Cambridge in 2020, a MSc in Computer Science from Imperial College London, and a dual-degree in Electrical Engineering and Accounting from UNSW. During his PhD at Cambridge, he conducted research on graphene integrated intelligent sensors and systems, and was recognised by the CAPE Acorn Postgraduate Research Award. He published in renowned journals including Science, Science Advances, Nature’s npj, Nature Communications, Sensors and Actuators B.


My Qualifications

PhD in Electrical Engineering (University of Cambridge, UK).

MSc in Advanced Computing (Imperial College London, UK).

BEng (Honours) in Electrical Engineering and BCom in Accounting (UNSW)


My Research Activities

• Spatiotemporal forecasting models for predictive maintenance of photovoltaic systems.

• Multimodal deep learning models for solar power generation prediction.

• Self-supervised generative models for solar cell defect diagnostics.

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