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.
When: Saturday Oct. 22 from 11:00 AM – 1:00 PM central time
Who: faculty, grad students, postdocs, and more who are interested in expanding undergraduate research in machine learning
11:00 AM – Welcome and Introduction to ML and Undergraduate Research (Dane Morgan)
- Attendee Goals Zoom Poll
- Group Zoom Picture
11:25 AM – What you can do: Introduction to the Impact of Machine Learning (ML) in Undergraduate Research (chair – Dane Morgan)
- How Undergraduate Informatics Research Changed my Research, Teaching, and Thinking: A Faculty Perspective (Dane Morgan)
- Working with Undergraduates using Machine Learning Models to Predict Materials Properties: A Graduate Student Perspective (Ben Afflerbach)
- Impact of Machine Learning Focused Research Projects on My Career Path: An Undergraduate perspective (Michael Vanden Heuvel)
11:55 AM – How you can do it: Demonstration of Tools and Resources That Support Undergraduate ML Research (chair – Ben Afflerbach)
- Machine Learning Tools and Resources (Ben Afflerbach)
- Modular Educational Materials
- Cloud computing resources
- Research Workflow Software
- Building a Community (Dane Morgan)
- Invitation to Join a Growing Research Community for Undergraduate Research with Machine Learning
12:15 PM – Identifying and Discussing Community Needs (Breakout Rooms)
- Self-selected breakout rooms themed for researchers and educators
12:35 PM – Breakout Room Report Out and Wrap-up (Dane Morgan, Ben Afflerbach)
- Closing Zoom Poll
12:45 PM – Research Project Brainstorming (Breakout Rooms, current research mentors – optional)
- Self-selected breakout rooms themed based on registration feedback
Dane Morgan is the Harvey D. Spangler Professor of Engineering in the Department of Materials Science and Engineering at the University of Wisconsin, Madison. His work combines thermostatistics, thermokinetics, and informatics analysis with atomic scale calculations to understand and predict materials properties. Morgan has graduated/trained over 70 graduate students and postdoctoral researchers and leads the Informatics Skunkworks, which has helped engage over 350 undergraduates at the interface of data science and science and engineering. He has received multiple teaching and research awards, and has published over 350 papers in materials science.
Anne Lynn Gillian-Daniel is the Director of Education and Outreach for the Materials Research Science and Engineering Center (UW-MRSEC) at the University of Wisconsin-Madison. As part of her position, Anne Lynn develops and leads professional development workshops around science communication, mentoring, and bias mitigation for researchers at all stages of their careers. She also works to broaden participation of underrepresented groups in materials science and engineering and to help early career researchers improve their understanding of issues around equity and inclusion.
Rebecca Cors is a social scientist and program evaluator at the Wisconsin Center for Education Research. She has expertise in investigating the effectiveness of science learning programs and has served as an external evaluator for NSF-funded projects designed to train scientists for a stronger workforce. Cors also sat on a review panel for NSF research training (NRT) proposals. She has published and presented research about out-of-school learning, about science and nature education, and about collaborations to promote natural resources management.
Benjamin Afflerbach is a Postdoc in the Department of Materials Science and Engineering at the University of Wisconsin-Madison working with Professor Morgan to help grow the Informatics Skunkworks program. His research focuses on enabling machine learning models to predict materials properties. He has led more than 5 undergraduate research projects and has developed and taught curriculum for onboarding new undergraduate researchers to more than 100 students.