The Citrine Research Spotlight Series profiles members of our academic and research community on their research interests, methodologies, and beyond.
This week we profile Vanessa Meschke, one of our 2017 NextGen Fellows. Vanessa is a rising senior at the University of Wisconsin Madison. She is majoring in Materials Science and Engineering, with a minor in Computer Science. Under the guidance of Professor Dane Morgan, Vanessa is working on predicting metallic alloys’ ability to form bulk glasses through data mining and machine learning and implementing a clustering algorithm with Citrination.
Learn more about Vanessa and her impressive undergraduate research career thus far below.
How did you get into your current field of undergraduate research?
As a junior in high school, I attended Michigan Tech’s Women in Engineering summer program, which offered an overview of what different engineers do and what it’s like to be a woman in a STEM field. The presentations from the week I remember most were a hands-on experiment with shape-memory alloys and an introduction to some different types of microscopes by the MS&E department. I loved the combination of chemistry and physics this field offers, and coming to UW-Madison has only enhanced this!
What are some of the larger themes and questions that inform your work?
I take a lot of motivation for my work from NASA’s vision: “reach[ing] for new heights and reveal[ing] the unknown for the benefit of humankind.” Materials science is capable of taking people higher and bettering the world, whether it be through new materials discovery or advancing processing methods. Marrying computer science with materials research is a way to enhance and expedite these changes. You never know when someone is going to discover the material that makes solar panels reach near-perfect efficiency or a bulk metallic glass that makes medical implants safer and stronger, and I want to be a part of that benefit.
How have you developed your own methodology?
I’ve just tried to find ways to keep myself excited about my work while making progress on tasks for research or schoolwork. Prof. Morgan has been particularly helpful in this; he has well-defined results he wants to see and ideas to circumnavigate major obstacles you may encounter along the way, but he makes sure to let my team try things our own way and learn from our mistakes.
Generally, what are some of the challenges you face, data related or otherwise?
My biggest challenges are overcoming my frustrations with programming. For research, I work mainly in Python, so a lot of my time is then spent looking up syntax and debugging programs.
What have been the most surprising or interesting findings in your past work or current work?
One of the most surprising pieces of information I’ve found during my current work is how important concrete is. My current project at the Skunkworks lab is predicting the mechanical properties of concrete, so I’ve done a fair amount of reading on what makes up concrete, how people measure its properties, and some of the concerns to keep in mind when designing a concrete mix. In cities, it’s easy to see how often concrete is used, but I had no idea how much work went into designing concrete before this project!
What do you enjoy the most about the work that you do?
My favorite part of my work is combining concepts I’ve learned in my different areas of study. I love being able to use an algorithm I’ve learned in a computer science class along with knowledge from my materials coursework to understand what kinds of features could be good inputs for a model. The same idea can be applied to analyzing a model’s output. It’s such a neat combination of two topics I learn about in the classroom!
What do you enjoy doing outside of undergraduate studies and research?
When I’m not working on schoolwork or research, I spend my time volunteering with the UW-Madison chapter of Circle K and participating in events sponsored by the Materials Advantage student organization. I also love baking, reading, and I’m currently training for a half marathon!
What has been your favorite part about participating in the Citrine NextGen Fellowship?
My favorite part of the NextGen Fellowship has been recognizing how materials science and machine learning can be combined in a meaningful way at both a business and academic level. Sometimes it’s hard to recognize areas where these two topics can combine since many people don’t understand what machine learning is or how it can be useful to them. NextGen has shown me a real-world example of a successful application of machine learning in an engineering setting.