I am an applied mathematician broadly interested at the intersection of neuroscience and machine learning. I am driven to uncover the algorithms underlying human/animal cognition. Taking inspiration from algorithms, I want to build more efficient and robust machine learning systems.
Right now, I am a 3rd year PhD student at University of Washington in the Applied Mathematics department. I am advised by Bing Brunton and Kameron Decker Harris. Currently, I am working on determining inductive biases - constraints in connectivity and learning mechanisms - used by sensory and motor circuits to inform deep learning methods. I am also interested in understanding why deep learning works. I find the mathematical techniques and ideas in deep learning theory aesthetically pleasing.
I did my undergraduate at UT Austin where I studied neuroscience and pure mathematics. My research advisors were Ila Fiete and Rishidev Chaudhuri. We used manifold learning methods, topological data analysis and good ol’ fashioned dynamical systems theory to visualize and verify the theoretically predicted ring attractor present in the mammalian head direction system. You can read the paper here.
I am originally from Nepal. I grew up in the capital, Kathmandu. My parents and I immigrated to the lonestar state (TX) in summer 2010. I have a sibling who’s doing his PhD in Physics at the University of Chicago. We try to keep the rivalry to a minimum ;). In my off time, I really enjoy listening to music and devouring books. I consider myself a hip-hop connoisseur. Some of my favorite artists in no particular order are Nas, Jay-Z, Rapsody, Travis Scott, and Drake. I’m also huge fan of the Joe Budden Podcast! Cooking is another thing I really enjoy and want to get better at. Since moving to Seattle, I have grown a love for hiking and spending time outdoors. If you want to chat about science or just vibe, please reach out. I thoroughly enjoy meeting and talking to new people!