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Young-Ok Cha

Young-Ok

Postdoctoral Research Assistant

Email: young-ok.cha@qmul.ac.uk

Profile

I am a Postdoctoral Research Assistant at Queen Mary University of London working at the intersection of artificial intelligence, materials discovery and electromagnetic systems. My research combines natural language processing, large language models, machine learning and data-centric methods with metamaterials, metasurfaces, antennas and electromagnetic materials. I develop computational approaches to extract and structure knowledge from scientific publications and support predictive modelling and discovery. Before my doctoral studies, I worked at Samsung on LCD and touch-sensor integration, CMOS-based image sensing and signal processing. This industry research contributed to 11 international patents and involved collaboration with partners including Motorola, Sharp and Hitachi.

Research

Research Interests:

Natural language processing; large language models; machine learning; AI-driven materials discovery; scientific literature mining; database building; data analysis; metamaterials and metasurfaces; antennas; electromagnetic materials and devices; additive manufacturing for electromagnetics.

Publications

1. Cha, Yo, Fernando, I., Mena, W. et al. (2026). “Graph-FINDER: multimodal AI for extracting materials data from scientific figures at scale.” npj Computational Matererials, DOI: 10.1038/s41524-026-02247-y. v

2. Cha, Y.-O., Ihalage, A. A. and Hao, Y. (2023). “Antennas and Propagation Research From Large-Scale Unstructured Data With Machine Learning: A review and predictions.” IEEE Antennas and Propagation Magazine, 65(5), 10–24. DOI: 10.1109/map.2023.3290385.

3. Khan, A. N., Cha, Y.-O., Giddens, H. and Hao, Y. (2022). “Recent Advances in Organ Specific Wireless Bioelectronic Devices: Perspective on Biotelemetry and Power Transfer Using Antenna Systems.” Engineering, 11, 27–41. DOI: 10.1016/j.eng.2021.10.019.

4. Cha, Y. and Hao, Y. (2022). “The Dawn of Metamaterial Engineering Predicted via Hyperdimensional Keyword Pool and Memory Learning.” Advanced Optical Materials, 10(8). DOI: 10.1002/adom.202102444.

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