Brickyard Suit, 699 S Mill Ave, Tempe, AZ 85281
[3/2020] Received the ASU CIDSE Doctoral Fellowship.
[3/2020] The Springer book is officially out! - Disinformation, Misinformation, and Fake News in Social Media. [ToC]
[2/2020] Invited to deliver a research talk at Purdue University.
[12/2019] Invited to serve as a PC member for WebSci'20.
[12/2019] Invited to serve as a PC member for ASONAM'20.
[11/2019] One paper is accepted in ICWSM'20
[9/2019] Fires in the Amazon: Arizona researchers determine what’s true, what’s not, an interview with Arizona PBS Cronkite News on AI and fake news [video].
[9/2019] AI Squares Off Against Fake News. -- feature news about KDD'19 tutorial.
[8/2019] Invited talk (remotely participating) at Microsoft AI on Learning with Weak Supervision.
[8/2019] Invited to serve as a PC member for AAAI'20.
[7/2019] Our book Detecting Fake News on Social Media is online.
[6/2019] Received the student travel award for KDD'19.
[6/2019] Two papers are accepted in ASONAM'19.
[6/2019] Invited talk at Microsoft Research AI Power Lunch talk series on fake news detection.
[Aug. 2015 - Present]
Research Assistant at DMML, Arizona State University, Tempe, AZ, USA.
[May 2019 - August 2019]
Research Intern at Microsoft AI Research, Redmond, WA, USA.
[May 2018 - August 2018]
Research Intern at Yahoo! Research, Sunnyvale, CA, USA.
[Jul. 2014 - June 2015]
Research visiting student at ICT, Chinese Academy of Sciences, Beijing, China.
[Mar. 2012 - Sept. 2013]
Research Intern at HP Labs China, Beijing, China.
I am a final year PhD student in the Department of Computer Science and Engineering at Arizona State University, Tempe, AZ. I am a research assistant at the Data Mining and Machine Learning Lab (DMML). My advisor is Professor Huan Liu. In general, my research lies in machine learning, data mining, social computing, and applications in disinformation, education, healthcare. My current interests include: (1) Data science for societal good: disinformation/fake news detection, user privacy, security; (2) Intelligent learning systems: interpretable, robust, fair; (3) Learning with limited and noisy data (weak supervision, data generation, meta learning, few-shot learning); (4) Representation Learning: feature learning for text/image/network, multi-modality fusion, domain adaptation.