

I'm a 4th-year Ph.D. student in the Brain and Cognitive Sciences (BCS) Department at MIT, co-advised by Ed Boyden as part of the Synthetic Neurobiology Group and by Ila Fiete as part of The Fiete Lab.
My research integrates machine learning, data science, and experimental neuroscience to address complex biological challenges.
At the start of my Ph.D., I focused on theoretical approaches to protein design, developing methods to enhance protein optimization by smoothing fitness landscapes.
Currently, I am applying such approaches towards the development of genetically encoded voltage indicators (GEVIs) in the nematode C. elegans.
Concurrently, I design data infrastructure and processing frameworks, with integrated machine learning pipelines, to support C. elegans projects within the Boyden lab, focusing on those that that use high-speed SPiM confocal or light-sheet fluorescence microscopy in vivo.
Together, these efforts aim to facilitate optical control and monitoring of the worm's nervous system with high spatiotemporal resolution. When possible, to complement my computational expertise, I learn and perform the wet-lab skills necessary to ensure alignment between assumptions made at the whiteboard and realities encountered at the lab bench.