Dr. Hong XING received the B.Eng. degree in Electronic Sciences and Technologies from Zhejiang University, China in 2011, and the Ph.D. degree in Wireless Communications from King‘s College...

She is currently an Assistant Professor with Information Hub/IoT Thrust, Hong Kong University of Science and Technology (GZ), and also an Affiliated Assistant Professor with the Department of Electronic and Computer Engineering (ECE), HKUST.

‪The Hong Kong University of Science and Technology (Guangzhou)‬ - ‪‪Cited by 2,053‬‬ - ‪wireless distributed learning‬ - ‪mobile-edge computing‬ - ‪physical-layer security‬

洪鑫研究员2010年本科毕业于中国科技大学，导师傅尧教授，2014年6月获得加州大学洛杉矶分校有机化学博士学位，导师美国科学院院士Kendall N. Houk教授。 2014年6月至2015年5月于加州大学洛杉矶分校化学系从事博士后研究工作，合作导师美国科学院院士Kendall N. Houk教授。 2015年6月至2016年5月于斯坦福大学化学工程学院从事博士后研究工作，合作导师美国工程院外籍院士Jens K. Nørskov教授。 2016年入选国家海外高层次人才引进计划青年项目，同年6月 …

Hong Xing (S’12–M’16) received the B.Eng. degree in electronic sciences and technologies, and the B.A. degree in English literature from Zhejiang University, Hangzhou, China, in 2011, and the Ph.D. degree in wireless communications from King’s College London, London, U.K., in 2015.

I am a PhD candidate at the Stanford Vision and Learning Lab in the Computer Science department of Stanford University, advised by Jiajun Wu. My research centers around how AI can understand, generate, and interact with the physical world. I spent two wonderful summers at Google Research, working with Noah Snavely and Bill Freeman.

hong xing. university of florida. Verified email at ufl.edu. neuroscience. Articles Cited by Public access. Title. Sort. Sort by citations Sort by year Sort by title. Cited by. Cited by. Year; Menthol-induced Ca2+ release from presynaptic Ca2+ stores potentiates sensory synaptic transmission.

Hong XING, Research Associate Professor | Cited by 1,778 | of Shenzhen University | Read 40 publications | Contact Hong XING

I am a second-year Ph.D. student in CS at Stanford advised by Prof. Ron Fedkiw. My research interest lies in computer graphics, computational physics, and machine learning. I strive to design numerical methods grounded in physical and mathematical principles to faithfully and efficiently simulate the motion of real-world objects.

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