The QMSS Innovation Lab was established with the goal of generating bold ideas; providing an interdisciplinary perspective; developing thoughtful methods; and working with data for the public good.
QMSS partners with organizations in the public and private sectors on projects that enable students to apply their research and data science skills while providing value to the partner organization. Innovation Lab projects typically synthesize the statistical, computational, engineering and social challenges involved in solving complex real-world problems. Projects typically progress through the following phases:
- Background and problem definition
- Data wrangling, munging and cleaning
- Exploratory Data Analysis
- Coding prototypes of algorithms and models
- Data Visualization
- Reporting, communicating and ethics discussion
- Productising any models or algorithms if applicable
For more information about partnering with QMSS, find out how it works HERE.
This Lab is also designed to have a strong student-focused orientation and support individual or team student projects and research ideas that they would like to explore and work on independently while pursuing their degree. In addition, QASR, the QMSS student organization, works to explore specific topics of interest to students through panels, book discussions, and workshops to expand on class-based learning and research.
We have worked with a number of organizations on critical projects. A partial list is below:
The Opportunity Project (US Census)
CASE STUDY Fall 2021
R-Story; Facilitating Rural Economic Development in partnership with the Census Bureau and the Environmental Protection Agency
Challenge: Create digital tools that help rural communities access and use data to implement solutions to economic, environmental, and human health challenges, taking care to reach places that have limited professional capacity and small budgets.
Student Team: Alisha Gurnani Gretchen Streett, Asahi Alex Nino,
CASE STUDY Fall 2021
COVID-19 Forecasting Tool
Challenge: Improve on previous modeling efforts to forecast COVID-19 infection rates based on daily time series data. Evaluate how to forecast daily COVID-19 time series data in one geography (e.g. New York) based on daily COVID-19 time series data from both that geography and other geographies (e.g. South Korea, Italy).
Student team: Sydney Son; Andrew Thvedt; Louisa Ong, Ariel Luo, Jen Woo