Siqi Wu
Assistant Professor at IU Luddy Department of Information and Library Science
Bloomington, Indiana, United States
Summary
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Senior🎓
Top SchoolSiqi Wu is an Assistant Professor at Indiana University’s Luddy Department of Information and Library Science, bringing a decade of experience in research and software development. A Google PhD Fellow and former postdoctoral researcher at the University of Michigan, she pursues computational social science and social computing—measuring and modeling social behavior in web contexts. Her career spans academic roles across Australia and the United States, including research fellowships at ANU and UMich, and industry experience as a Software Engineer at MicroStrategy. She holds a PhD in Computer Science from ANU, a Master of Engineering in IT from the University of Melbourne, and a BEng in Electrical and Electronics Engineering from Tianjin University, reflecting a strong foundation in both theory and applied engineering. Based in Bloomington, Indiana, she blends rigorous data-driven research with scalable software development, with a GitHub presence centered on computational social science and social computing. She is recognized for bridging technical depth with a keen interest in how online environments shape collective behavior.
11 years of coding experience
1 year of employment as a software developer
Australian National UniversityBachelor of Engineering (BEng), Electrical and Electronics Engineering, Bachelor of Engineering (BEng), Electrical and Electronics Engineering at Tianjin UniversityThe University of Melbourne
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (31)
twitter10
crowdsourcing9
popularity9
video-player9
player9
swift9
macos8
mpv8
youtube8
youtube-api7
crawler6
api6
ressources6
python6
java6
Programming languages (6)
Github contributions (5)
avalanchesiqi/yt-longevity
Jul 2016 - Jan 2018
Contributions:321 commits, 2 PRs, 245 pushes in 1 year 6 months
youtube-datadata-analysisyoutubeyoutube-data-analysisworkflow
avalanchesiqi/pyquantifier
Jun 2023 - Dec 2024
A Python package to estimate class prevalence in unlabeled datasets by specifying stability assumptions
Contributions:2 reviews, 3 PRs, 49 pushes in 1 year 6 months
prevalence-estimationquantification
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