Omar Shaikh

Mapping Researchers with PeopleMap

Jon Saad-Falcon
Austin P. Wright
Sasha Richardson
IEEE Visualization Conference (VIS), 2020
Best Poster, Honorable Mention

Abstract

Discovering research expertise at universities can be a difficult task. Directories routinely become outdated, and few help in visually summarizing researchers’ work or supporting the exploration of shared interests among researchers. This results in lost opportunities for both internal and external entities to discover new connections, nurture research collaboration, and explore the diversity of research. To address this problem, at Georgia Tech, we have been developing PeopleMap, an open-source interactive web-based tool that uses natural language processing (NLP) to create visual maps for researchers based on their research interests and publications. Requiring only the researchers’ Google Scholar profiles as input, PeopleMap generates and visualizes embeddings for the researchers, significantly reducing the need for manual curation of publication information. To encourage and facilitate easy adoption and extension of PeopleMap, we have open-sourced it under the permissive MIT. PeopleMap has received positive feedback and enthusiasm for expanding its adoption across Georgia Tech.

Materials

Project
Demo
PDF
Code

BibTeX

			
@inproceedings{saadfalcon2020peoplemap,
  title={CNN Explainer: Learning Convolutional Neural Networks with Interactive Visualization},
  author={Jon Saad-Falcon and Omar Shaikh and Zijie J. Wang and Austin P. Wright and Sasha Richardson and Duen Horng Chau},
  booktitle={IEEE Visualization Conference (VIS)},
  publisher={IEEE},
  year={2020},
  url={https://poloclub.github.io/people-map/}
}