Scholar
Hancheng Cao
Google Scholar ID: w0UNVG0AAAAJ
Assistant professor, Goizueta Business School, Emory University
Citations & Impact
Citations
2,222
H-index
23
i10-index
32
Publications
20
Co-authors
0
Contact
Publications
Resume (English only)
Academic Achievements
- - Awarded three best paper/best paper honorable mention awards at top venues in human-computer interaction, including CHI, CSCW
- - Publications in top social science journals such as American Sociological Review, and reported in venues such as Wired, Forbes, and TED
- - Research supported by Stanford Interdisciplinary Graduate Fellowship
Research Experience
- - Research Intern at Semantic Scholar/AllenNLP, Allen Institute for Artificial Intelligence (Jun. 2022 - Dec. 2022), Hosts: Lucy Lu Wang, Kyle Lo, Jesse Dodge
- - Research Intern at E & D Office of Applied Research, Microsoft (Jun. 2020 - Sep. 2020, Mar. 2022 - Jun. 2022), Hosts: Longqi Yang, CJ Lee, Shamsi Iqbal, Mary Czerwinski, Brent Hecht, Jaime Teevan
- - Research Intern at Computational Social Science Group, Microsoft Research (Jun. 2021 - Sep. 2021), Hosts: Jake Hofman, Dan Goldstein
- - Research Intern at Map Service, Mobile Internet Group, Tencent Inc (Jul. 2018 - Sep. 2018)
Education
- - Ph.D. in Computer Science from Stanford University (Sep. 2018 - Jun. 2024), advised by Prof. Daniel McFarland and Prof. Michael Bernstein
- - Bachelor of Engineering (with honors) from Department of Electronic Engineering, Tsinghua University (Aug. 2014 - Jul. 2018)
- - Exchange Student at A. James Clark School of Engineering, University of Maryland, College Park (Aug. 2016 - Dec. 2016)
- - Visiting Student and Research Assistant at Human Dynamics Group, Media Laboratory, Massachusetts Institute of Technology (Jun. 2017 - Sep. 2017)
Background
- Research interests encompass computational social science, information systems, and human-AI interaction, with a specific focus on studying the implications of computing systems on the future of work. Bridging computation and social science theories, his works seek to unravel the intricacies of work dynamics and interactions through mining large-scale data with state-of-the-art computational methods, complemented by qualitative methods, experiments, and system building.
Co-authors