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Bio
He is a leading researcher in the field of Graph Deep Learning, a new framework that combines graph theory and deep learning techniques to tackle complex data domains in natural language processing, computer vision, combinatorial optimization, quantum chemistry, physics, neuroscience, genetics and social networks. In 2016, he received the highly competitive Singaporean NRF Fellowship of $2.5M to develop these deep learning techniques. He was also awarded several research grants in the U.S. and Hong Kong. As a leading researcher in the field, he has published more than 60 peer-reviewed papers in the leading journals and conference proceedings in machine learning, including articles in NeurIPS, ICML, ICLR, CVPR, JMLR. He has organized several international workshops and tutorials on AI and deep learning in collaboration with Facebook, NYU and Imperial such as the 2019 and 2018 UCLA workshops, the 2017 CVPR tutorial and the 2017 NeurIPS tutorial.
Research Interests
Papers共 29 篇Author StatisticsCo-AuthorSimilar Experts
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CoRR (2024)
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ICLR 2024 (2023)
JOURNAL OF MACHINE LEARNING RESEARCH (2022): 43:1-43:48
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2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2016): 2439-2443
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