Mentor Areas
- AI
- Human Mobility Science
- Massive Geospatial Data Science
- Software development
- Open science
Description:
NOMAD is a data platform for open mobility analysis at Penn. We build open-source tools and a secure Trusted Research Environment with a curated data catalog to process large-scale GPS traces for social science and public health. The platform provides documented, reproducible pipelines—ingestion, quality control, spatiotemporal transforms, mobility metrics, and privacy-preserving aggregation—accessible both as Python code and through simple web interfaces. This is an multi-faceted open-science project with the goal of increasing access and facilitating analysis of GPS human mobility datasets. By participating in this research project you will have the opportunity to learn how to process massive spatio-temporal data, front-end development, as well as an LLM-related project to automatically classify scientific literature in the field of human mobility science.
Research assistants help extend the NOMAD library and website by implementing and testing modules for mobility data processing, generation of synthetic datasets, dashboards, collaborations with local government agencies for disaster preparedness, epidemiology, sustainable development, and others. The software has an emphasis on scalable computing, so methods should run identically on a laptop or a Spark cluster. Students will be involved in assisting the deployment of a pilot study in which access to sensitive data is given to selected researchers who will be provided with software and infrastructure resources.
The role offers hands-on exposure to front- and back-end development (JavaScript/React, Node, DataBases, LLMs, Python; PySpark), software testing with Cypress, and analysis of spatial and geometry data. Students engage with research on group behavior, human mobility, and epidemic modeling while learning practical development processes in a collaborative lab. The emphasis throughout is on shipping transparent, well-tested tools that other researchers can use and reproduce.
Preferred Qualifications
- Proficiency in either Python and data science, or web development or front-end/full-stack development.
- Previous research experience is preferred but not required.
- Backgrounds in CS, software development, data science, network science, math or engineering are welcome.
Project Website
Learn more about the researcher and/or the project here. NOMAD
Details:
Preferred Student Year
Junior, Senior
Academic Term
Fall, Spring
I prefer to have students start during the above term(s).Volunteer
No
Yes indicates that faculty are open to volunteers.Paid
Yes
Yes indicates that faculty are open to paying students they engage in their research, regardless of their work-study eligibility.Work Study
Yes
Yes indicates that faculty are open to hiring work-study-eligible students.