Free, Informative Online Workshop: “Enhancing Undergraduate Research with Machine Learning”

Please join us for a free, informative online workshop “Enhancing Undergraduate Research with Machine Learning” on Saturday, October 22 from 11 am -1 pm (CT).

Machine learning (ML) is transforming almost every area of our society, from self-driving cars to speaking robots to new medicines, and many students are interested in ML literacy to benefit their future careers and earning potential.  ML extracts patterns from data, providing unprecedented power to understand and predict in complex systems. Therefore, ML is a powerful tool to enhance basic science research across all disciplines for researchers.  ML’s power, low-cost, and relative accessibility create an exciting opportunity to simultaneously expand and evolve undergraduate research and provide critical workforce training in this field. The goal of this workshop is to help you explore how you can take advantage of this opportunity by presenting free tools and curriculum designed to reduce the  barriers to integrating more machine learning into your own undergraduate research programs.

During this free, interactive workshop, you will see some state of the art example projects in chemistry, physics, engineering and medicine that have benefited from ML and discuss opportunities to add ML to your work. You will also learn about the extraordinary free tools that support ML research, including curriculum and easy-to-use software we have developed to help undergraduate teams engage quickly and efficiently in authentic research in your work. Finally, you will have the opportunity to talk with researchers about using ML to advance your research. If you would like to submit a problem, data set, or just some initial ideas in advance of the workshop, we’ll have breakout rooms at the end where you can talk about your specific needs with a researcher experienced in ML.

Details about the workshop, speakers, schedule, etc. can be found here:

Please register here :  by Friday October 7.

If you have any questions please contact our Informatics Skunkworks team :

This workshop is supported by the National Science Foundation under award number 2017072.