Brickyard Suit, 699 S Mill Ave, Tempe, AZ 85281
[7/2020] Two papers are accepted in CIKM 2020.
[7/2020] An algorithm to detect fake news - an interview together with my advisor Prof. Huan Liu.
[7/2020] Invited to serve as a PC member for SDM 2021.
[6/2020] Received the Best Reviewer Award in ICWSM 2020.
[6/2020] Invited to serve as a PC member for WSDM 2021.
[6/2020] Two papers are accepted in ECML-PKDD 2020.
[5/2020] FakeNewsTracker is among the Top ML Projects To Fight Fake News Fatigue During COVID-19.
[5/2020] Co-presented (remotely) a keynote talk in the Workshop on Data Science for Fake News at PAKDD 2020.
[4/2020] One paper is accepted in SIGIR'20.
[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]
[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.
[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.
Kai Shu obtained his PhD of Computer Science in Summer 2020 at Arizona State University, under the supervision of Professor Huan Liu. He is a research assistant at the Data Mining and Machine Learning Lab (DMML). In general, his research lies in machine learning, data mining, social computing, and applications in disinformation, education, healthcare. His 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.
I am actively looking for self-motivated PhD students to conduct research in the area of data mining, machine learning and social media mining. Interested students please feel free to drop me an email with your CV and transcript.
CALL FOR PAPERS: