Jingbo Shang
Associate Professor
San Diego, California, United States
Summary
🤩
Rockstar🎓
Top SchoolJingbo Shang is an Associate Professor of Computer Science and Data Science at UC San Diego with 13 years of experience bridging academic research and practical system-building in natural language processing and data mining. He earned a Ph.D. from UIUC and has progressed from research and teaching roles there to faculty appointments at UCSD, where he develops scalable algorithms and mentors graduate students. Jingbo contributes to open-source NLP tooling—most notably backend development on AutoPhrase, an automated phrase mining project that processes massive text corpora—bringing production-minded feature extraction and tokenizer integration to research code. His work blends statistical rigor with software engineering, improving model training, segmentation, and model persistence for real-world pipelines. Based in San Diego, he combines deep research expertise with hands-on engineering to move ideas from prototype to reproducible systems. A detail that often surprises collaborators is his sustained focus on backend robustness in research software, ensuring academic innovations are maintainable and deployable.
13 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at University of Illinois Urbana-ChampaignBachelor's degree Computer Science, Bachelor's degree Computer Science at Shanghai Jiao Tong University
English, Chinese
Github Skills (9)
text-mining10
automatic10
c-language10
cprogramming-language10
python9
machine-learning8
algorithms8
data-structures8
algorithm8
Programming languages (6)
Github contributions (5)
shangjingbo1226/AutoPhrase
Dec 2016 - Jan 2021
AutoPhrase: Automated Phrase Mining from Massive Text Corpora
Role in this project:
Back-end Developer
Back-end DeveloperContributions:74 commits, 7 PRs, 88 pushes in 4 years 2 months
Contributions summary:Jingbo primarily focused on developing and maintaining the backend components of the AutoPhrase project. Their contributions include implementing feature extraction methods, handling stopword and punctuation analysis, and integrating statistical features for phrase quality assessment. The code changes also involved modifications to the training and segmentation processes, with improvements to the handling of model saving and loading. They also worked on the integration of the tokenizer.
pythonmulti-languagetopic-modelinglexiconnatural-language-processing
shangjingbo1226/DPPred
Sep 2017 - Jun 2019
Contributions:6 commits, 5 pushes, 1 branch in 1 year 9 months
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