User:Xianteng
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This user is a student editor in Wikipedia:Wiki_Ed/School_of_Information_Sciences/INFSCI_2430_-_Social_Computing_(Spring_2017). Student assignments should always be carried out using a course page set up by the instructor. It is usually best to develop assignments in your sandbox. After evaluation, the additions may go on to become a Wikipedia article or be published in an existing article. |
— Wikipedian ♀ — | ||||
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Name | Xian Teng | |||
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Born | October 20, 1989 | |||
Nationality | China | |||
Country | China | |||
Current location | Pittsburgh | |||
Race | Asian | |||
Family and friends | ||||
Marital status | Married | |||
Education and employment | ||||
Occupation | PhD student | |||
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Joined | January 19, 2017 | |||
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My name is Xian Teng (Chinese: 滕贤). I am a doctoral student in School of Information Sciences at University of Pittsburgh. My major is Information Science and Technology. Currently, I work in Pitt Computational Social Science Lab directed by my advisor Dr. Yu-Ru Lin. Before I moved to Pittsburgh, I was a visiting scholar in Levich Institute at City College of New York under supervision of Dr. Hernan Makse. I obtained my master degree in applied mathematics and bachelor degree in automation from Beihang University (also known as Beijing University of Aeronautics and Astronautics), Beijing, China.
I am broadly interested in data science, network science and computational social science. My research is mainly focused on the detection of emerging events or anomalous patterns in large-scale network and social media data. Examples of application include disease outbreak detection using streaming social media dataset, emergency detection in cities by transportation data, as well as terrorist attack and civil unrest detection.
My prior research topics were information diffusion and disease spreading in complex networks. In particular, I was interested in studying individual roles in diffusion behaviors and trying to identify the most influential spreaders given a large complex network. Our paper entitled "Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-scale Social Networks" was published in Scientific Report in October, 2016.