Senior James Bunnell will have co-published five scientific papers when he graduates in the fall with a bachelor’s degree in computer engineering.
Undergrad Research Spotlight: This is part of a series on undergraduate research opportunities at SDSU and the programs that help support a future pipeline of scientists from diverse and underrepresented groups.
A rising senior at San Diego State University, computer engineering major James Bunnell will soon have the distinction of having contributed to five scientific papers as an undergraduate. He is now focused on machine learning to assess the best options for materials to be used in sensors that will be embedded in the brain to help patients with debilitating movement disorders such as Parkinson’s Disease.
Bunnell is a first-generation college student whose parents worked for the Federal Aviation Administration. Their experience changed his own career trajectory, leading him to embark on a pathway to research.
Bunnell credits assistant dean of engineering for student affairs Theresa Garcia who leads these pioneering programs for providing him training and introducing him to lab research. With the help of these opportunities, he was paired with mechanical engineering professor Sam Kassenge to do lab research.
Kassegne said, “I am extremely happy to have such a bright young man onboard our research group and to see him going places where he will make an impact. His research productivity is quite impressive and is on par with that of seasoned graduate students.
“The story of James is a reminder that undergraduate research could be a very productive and key component in research labs. Programs that support such undergraduate engagements like ANSWER and MESA are making an impact in the lives of these young students.”
Bunnell spoke with SDSU NewsCenter about his time and research in the lab.
What is the most interesting project you’ve worked on?
The most interesting project I’m doing right now is machine learning, assigned to me by Professor Kassegne. We’re attempting to make a machine learning system that can predict the best choice of material for certain applications. It can evaluate 2D materials based on a given set of parameters. There are so many different materials out there, we want to find a way to filter them.
How can you incorporate this research into the real world?
Postdoctoral researcher Surabhi Nimbalkar is working on graphene and glassy carbon synthetic composite material to be used in sensors embedded in the brain. We want to evaluate how 2D materials like graphene are going to interact with a 3D material like glassy carbon. We want to make a model that can predict such interactions. We can evaluate what applications would be possible from that, depending on the material’s properties. When we need a material that can better conduct heat and reduce the heat transfer between objects it comes into contact with, this method helps us find the right combination of 2D and 3D materials.
What is your role in the lab?
I do my own research with machine learning. I’m figuring out how to make that kind of system happen, how it would work, and then generating the data so we can train it. I also help graduate students with their research. In the five papers I’ve contributed to, I’ve been helping the master’s and Ph.D students publish their papers with data analysis using Python software and MATLAB computing platform. Before COVID-19 happened, I was learning how to fabricate things in the nanofabrication cleanroom where they make the micro devices they use for testing. But unfortunately, that was postponed due to the limited in-person access due to COVID and we pivoted to computational approaches.
Why is your research important to you and the rest of the world?
We’re discovering knowledge that will have a significant impact. I like the saying “we stand on the shoulders of giants.” We’re always producing new material, but we may not be the ones to transform them into interesting applications, although I hope to create interesting applications myself someday.
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