Nathaniel Hawkins



I am PhD student in the Department of Computational Mathematics, Science, and Engineering (CMSE) at Michigan State University. Currently, I am working with Dr. Arjun Krishnan on a project to annotate biological samples from unstructured text using Natural Language Processing (NLP) based machine learning methods. Broadly, I am interested in machine-learning, algorithm development and optimization, and developing new methods for data analysis. Once this project is complete, I will begin working on a gene expression imputation pipeline. I began taking graduate coursework in the Fall of 2019.

Formerly, I was an Education Researcher and Curriculum Consultant with the Department of CMSE at Michigan State University. My work involved idenitfying the core competencies and skills needed to participate in the field of computational science. Through expert interviews, we described the shared practices of professionals in academia and industry who use computational modeling and data analysis in their day-to-day work. This work was motivated by the Communities of Practice framework. I also worked on curriculum development for two introductory computational science courses offered at Michigan State. Some examples of my curriculum development can be found on my “Projects” page.

I graduated from Michigan State University in May, 2018 with a B.S. in Physics and a minor in Computational Mathematics, Science, and Engineering. In my undergraduate career, I participated in physics education research and biophysics research. My education research focused on understanding students’ perceptions of the use of computation in developing their understanding of physics. In my biophysics research, I studied the use of clustering algorithms for identifying structure in single-cell RNA sequencing data.

Personally, I love playing the ukulele, exercising and yoga, learning more about computers, Dungeons and Dragons, video games, and tasteful audiobooks. Above all else, I enjoy connecting with people, whether it’s over a cup of coffee or in passing in the hallway. Follow me on Twitter, look me up on Github, and/or add me on LinkedIn.