Free Workshop – Enhancing Undergraduate Research With Machine Learning

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.

Workshop Info

When: Saturday Oct. 22 from 11:00 AM – 1:00 PM central time

Where: Zoom

Who: faculty, grad students, postdocs, and more who are interested in expanding undergraduate research in machine learning
45+ participants registered so far!
Register Here

 

Draft Agenda:

11:00 AM – Welcome (Dane Morgan)

11:05 AM – Introduction to the Power of Machine Learning in Undergraduate Research (Dane Morgan)

  • How Undergraduates Helped me Grow my Informatics Research Program (Dane Morgan)
  • Working with Undergraduates using Machine Learning Models to Predict Materials Properties (Ben Afflerbach)
  • Student Experiences With Machine Learning Focused Research Projects (Current Undergraduate Researcher)

11:45 AM – Demonstration and Presentation of Tools and Resources That Support Undergraduate Research

12:15 PM – Invitation to Join a Growing Research Community for Undergraduate Research with Machine Learning

12:30 PM – Breakout Room Research Project Brainstorming

  • Pre-assigned breakout room themed discussions based on registration feedback

1:00 PM End

 

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.