Adithya "Adi" Yerramsetty

Howdy! My name's Adi. Below you'll find some things I've made public, which you hopefully find interesting/useful.

I'm currently

• a junior at ASU going for a double major in CS + Math
• president at the ASU Machine Learning Club, where I teach ML, Deep Learning and more to interested members
• an undergrad researcher at ASU DREAM Lab working on NeRF/View Synthesis
• working on building a NeRF of all of Seattle from some driving data I collected
• implementing and playing with various algos/problems in ML/CV including Diffusion Models, NeRFs, Visual-SLAM, etc.

In the past I

• interned as a Deep Learning Intern at Silicon Valley Bank(May 2022 - January 2023), where I worked on Graph Neural Nets for investment predictions
• have won prizes at a variety of hackathons, including but not limited to DataFest @ ASU, ASU AI In Education and SVB @ ASU 2021

Here is

• a really lightweight, scalar-only AutoGrad library I built, inspired by MicroGrad by Karpathy - not very performant, but can fit Linear Models and MNIST MLP, and in theory can handle most models

I'm currently writing up

• a whirlwind tour of ML, going over some of the topics we would normally cover at the ML club
• an explainer for the proof for gradient descent; a lot of it is detailed nicely by this article, but I want to go through it myself
• a derivation for the SVD Solution to Homogenous LLS. I have it on my old site, but haven't moved it here yet. It's pretty useful in V-SLAM
• a post on the basics of vector search(LSH, HNSW, etc). They're a super cool application of Neural Nets, and fascinated me during my freshman year
• an overview and walkthrough of applying standard generative models(GAN, EBMs, Diffusion Models) on MNIST. I'm working toward's re-producing Waymo's MotionDiffuser, so this is a good preliminary.