I’m currently a postdoc at Lawrence Berkeley National Lab working with Kris Bouchard. I work on developing data analysis and machine learning methods for neuroscience data (and sometimes data from other fields). I also am interested in theoretical models of neural coding.
I focus on a few different topics in my research. I’m interested in understanding and pushing the limits of linear models and deep learning in neuroscience data analysis. I’m also interested in developing and understanding new machine learning models and methods.
I did my Ph.D. in physics at UC Berkeley. I worked in Mike DeWeese’s group studying theoretical and computational neuroscience. I volunteered with the Berkeley Compass Project: a group of graduate and undergraduate students looking to improve educational practices, support a diverse community, and develop professionally as an educator in the physical sciences. Through teaching with the Compass Project, I collaborated on and published a curriculum studying slinkies based on modeling instruction and studied how students can use the framework of experimental design and improvement as a framework to understand their own learning.
I’m interested in the brain, mathematical models, science education, and using data for social good.