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.


My research is focused on making AI models more scalable and accessible. Working in both Language and Vision domains, I design more efficient modeling methods and create diverse datasets that improves the scalability of the models. My work also tries to bring the unprecedented capabilities of novel AI models to a more broad audience by eliminating many technical barriers. Ultimately, I hope these methods can become the infrastructure for solving the most challenging problems in our society.


2020 Oct

Allen Institue for AI (Semantic Scholar)

Predoctoral Young Investigator

2019 Sep

Harvard University (IQSS)

Data Science Fellow

2019 May


2021 Oct

The LayoutParser project is featured in Standford Digital Economy Seminar Series.

2021 Sep

Give a talk on LayoutParser and Document Intelligence at Online Seminar in Economics + Data Science @ ETH Zurich

2021 Sep

The LayoutParser talk video is available on Youtube.

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 Sep

A Unified Toolkit for Deep Learning Based Document Image Analysis

Online Seminar in Economics + Data Science @ ETH Zurich
2020 Mar

Information Retrieval over Historical Scans with Non-trivial Layouts

Kaggle Days Meetup Boston
2019 Feb

Deep Learning-based Framework for Automatic Damage Detection in Aircraft Engine Borescope Inspection

ICNC 2019, Honolulu, Hawaii