Zejiang (Shannon) Shen is a deep learning researcher, working on Natural Language Processing(NLP) and Computer Vision(CV) models for Document Intelligence(DI). Currently, he is a Predoctoral Young Investigator at Allen Institute for AI (AI2) on the Semantic Scholar research team, and focuses on building NLP models for understanding scientific document.

Before joining AI2, Zejiang Shen was a Data Science Fellow at the Institute for Quantitative Social Science (IQSS) at Harvard University. Supervised by Prof. Melissa Dell, he implemented deep learning models, curated datasets, and developed libraries that enables efficient and accurate processing of large-scale Japanese historical documents and historical newspapers.

During his master studies at Brown University, he worked on generative vision models under the supervision of Prof. James Tompkin, and participated in various data science challenges.

Recent News

2021 Jun

Please check our new paper on Scientific Document Parsing - Incorporating Visual Layout Structures for Scientific Text Classification.

2021 Jun

The LayoutParser paper is accepted as an oral paper at ICDAR 2021.

2021 Apr

The LayoutParser paper and website has been released.