Through ArXivLab we aim to develop the next generation recommender systems for the scientific literature using statistical machine learning approaches. In collaboration with ArXiv we are currently developing a new scholarly literature browser which will be able to extract knowledge implicit in the mathematical and scientific literature, offer advanced mathematical search capabilities and provide personalized recommendations.

One selected candidate will receive a stipend via the DSI Scholars program. Amount is subject to available funding.

Faculty Advisor

Project timeline

  • Earliest starting date: 03/01/2019
  • End date: 08/31/2019
  • Number of hours per week of research expected during Spring 2019: ~10
  • Number of hours per week of research expected during Summer 2019: ~20-40

Candidate requirements

  • Skill sets: Prior programming experience, especially with Javascript (e.g. react.js, node.js an d express.js) and Python (e.g. flask, numpy and scipy). Prior exposure to NLP and ML approaches either through research projects or classes. Experience with databases (e.g. SQL) and/or the Solr search platform is a definite plus.
  • Student eligibility (as of Spring 2019): freshman, sophomore, junior, senior, master’s
  • International students on F1 or J1 visa: eligible