About Me

Hello, I am a fifth year graduate student in the Computer Science and Engineering department within the University of Washington. I’m currently working in the ICTD lab with Kurtis Heimerl. I received both my bachelor’s degree in Electrical Engineering and Computer Science and Master of Engineering degree in Computer Science from MIT.

My research broadly addresses ways to make machine learning algorithms more applicable in low resource settings. Due to this interest, I closely follow work in edge machine learning, federated learning and hardware-software codesign of machine learning algorithms. I am currently actively working on developing mixture of expert models for mobile vision transformers as an approach to structured sparsity for efficient inference. My application area mostly focuses on wildlife monitoring for ecological conservation although my research is broadly applicable to edge deep learning. For this project, I look at both a multimodal approach for detection (video and sound) as well as edge optimization for energy efficiency (through nature inspired inductive biases). I partner with stakeholders from conservation (Conservation X Labs, Quantitative Ecology Lab) and agriculture (Farmbeats and Nelson Farms in Eastern Washington) to test proposed models using CPU, Raspberry Pi, Google Edge TPU accelerator and Android. My research has been sponsored by Azure AI for Earth grant, UW CS for the Environment Felloship and National AI Research Resources grant.

My current guides are Mandelbrot and Hegel.