Jiahua Zhao

Jiahua Zhao

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PhD Student / Graduate Research Fellow +357 22 208 700

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Jiahua Zhao received his Bachelor's degree in Oceanography from the Southern University of Science and Technology (SUSTech) in China (July 2020), followed by a Master's degree in Geophysics from the University of Padova (UniPD) in Italy (September 2023), and finally joined the Cyprus Institute for Computation-based Science and Technology Research Center (CaSToRC) as a PhD Student / Graduate Research Fellow in October 2023.
During his undergraduate studies, Jiahua Zhao specialized in marine seismology. He interned at the Marine Seismology Group at the Lamont-Doherty Earth Observatory, Columbia University, in the summer of 2019, where he conducted research on 2-D / 3-D seismic tomography. He then joined the Seismology Group at Harvard University as an intern in October 2019 to work on intelligent storage of digitized historic analogue seismograms. While studying for his master's degree, Jiahua Zhao's research inclined towards the application of HPC and AI technologies. He participated in the Summer of HPC program of PRACE (Partnership for Advanced Computing in Europe), working with experts from the Edinburgh Parallel Computing Centre for the parallelization of CFD Python programs. He also worked as a visiting student in the Deep Imaging Group at King Abdullah University of Science and Technology (KAUST), developing a fast method for picking surface wave dispersion curves based on deep learning techniques.
In addition to his research work, Jiahua Zhao has participated in or mentored students in various international supercomputing competitions (e.g., ISC, SCC, and ASC, etc.), achieving excellent results.
Currently, as part of the ENGAGE programme, his PhD project under the supervision of Prof. D. J. Verschuur (TUDelft) and Prof. Assist. N. Savva (CyI), which aims to improve the efficiency and quality of 3-D seismic imaging processing by using high-performance computing technologies.
Research Interests:
High-performance Computing, Parallel Computing, Heterogeneous Computing, Machine Learning, Deep Learning, Seismology Tomography, Interdisciplinary Applications of Computational Technologies

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