Nihang's Homepage

About Me

Hi, I'm Nihang Fu, a final-year PhD student in Computer Science at the University of South Carolina (Columbia, SC, USA). I am conducting my research in the Machine Learning and Evolution Laboratory supervised by Dr. Jianjun Hu. I obtained my M.S. degree in Computer Engineering from Northeastern University (Boston, MA, USA) working at Augmented Cognition Laboratory. Before that, I received my B.S. degree in Eletrical Engineering and Automation from Chongqing Jiaotong University (Chongqing, China) and Currently, my researches focus on applying deep learning in materials science.

Education

2021 - 2025 (expected), Ph.D. in Computer science, University of South Carolina (Columbia, SC, USA)
2019 - 2021, M.S. in Electrical and Computer Engineering, Northeastern University (Boston, MA, USA)
2015 - 2019, B.S. in Electrical Engineering and Automation, Chongqing Jiaotong University (Chongqing, China)

Selected Publications

[See all papers on Google Scholar]

Fu, N., Omee, S.S. and Hu, J., 2025. Physical encoding improves OOD performance in deep learning materials property prediction. Computational Materials Science, 248, p.113603. [paper]
Li, Q., Fu, N., Omee, S.S. and Hu, J., 2024. MD-HIT: Machine learning for material property prediction with dataset redundancy control. npj Computational Materials, 10(1), p.245. [paper]
Hu, J., Li, Q. and Fu, N., 2024. Generative AI for Materials Discovery: Design Without Understanding. Engineering.
Fu, N., Wei, L. and Hu, J., 2024. Physics-Guided Dual Self-Supervised Learning for Structure-Based Material Property Prediction. The Journal of Physical Chemistry Letters, 15(10), pp.2841-2850. [paper] [code]
Omee, S.S., Fu, N., Dong, R., Hu, M. and Hu, J., 2024. Structure-based out-of-distribution (OOD) materials property prediction: a benchmark study. npj Computational Materials, 10(1), p.144.
Fu, N., Hu, J., Feng, Y., Morrison, G., Loye, H.C.Z. and Hu, J., 2023. Composition Based Oxidation State Prediction of Materials Using Deep Learning Language Models. Advanced Science, 10(28), p.2301011. [paper] [code] [toolbox]
Fu, N., Wei, L., Song, Y., Li, Q., Xin, R., Omee, S.S., Dong, R., Siriwardane, E.M.D. and Hu, J., 2023. Material transformers: deep learning language models for generative materials design. Machine Learning: Science and Technology, 4(1), p.015001. [paper] [code]
Liu, S., Huang, X., Fu, N., Li, C., Su, Z. and Ostadabbas, S., 2022. Simultaneously-collected multimodal lying pose dataset: Enabling in-bed human pose monitoring. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(1), pp.1106-1118.
Wei, L., Fu, N., Siriwardane, E.M., Yang, W., Omee, S.S., Dong, R., Xin, R. and Hu, J., 2022. TCSP: a template-based crystal structure prediction algorithm for materials discovery. Inorganic Chemistry, 61(22), pp.8431-8439.
Huang, X., Fu, N., Liu, S. and Ostadabbas, S., 2021, December. Invariant representation learning for infant pose estimation with small data. In 2021 16th IEEE International Conference on Automatic Face and Gesture Recognition (FG 2021) (pp. 1-8). IEEE.

Awards

National Scholarship, Ministry of Education of the People’s Republic of China, China, 2017.

National Scholarship, Ministry of Education of the People’s Republic of China, China, 2016.