My name is Kevin Miao! I am currently working at Apple as a Machine Learning Engineer. My work entails developing and researching tools for machine learning and computer vision algorithms.

My interests

I am deeply curious about the intersection of explainable artificial intelligence, deep learning and computer vision. Generally, I am interested in investigating how we can make machine learning models more emergent and natural. Rather than focusing on changing up architecture and increasing compute, I want to find out how we can learn better representations faster in low-data regimes. How do we utilize our data efficiently when we cannot afford millions of data points? How can we speed up training? My vision is that the answer to these questions will allow us to build safe, intelligent and efficient large systems which we can safeguard against perturbations or threats.

more background 👇

I recently finished my Master’s degree in Electrical Engineering and Computer Science (‘22) at UC Berkeley. 🐻 Under the supervision of Joey Gonzalez and close collaboration with Trevor Darrell and Kurt Keutzer. In my thesis work, I devised a framework for knowledge-embedded regularization in self-supervised vision transformers (Arxiv link can be found below).

Prior to that I finished my undergraduate degree at UC Berkeley in Computer Science with honors. However, my original academic interests have actually always been in Biology and Medicine until my third year of college when I first learned to code. I investigated the evolution of the olfactory neurons in S. Flava (a fruit fly species) under Noah Whiteman. After switching my major to Data Science (and then to Computer Science), I combined my biomedical and computative knowledge to investigate and develop precision medicine frameworks at UCSF with Julian Hong. In the summer after obtaining my bachelor’s degree in 2021, I worked as a Machine Learning Intern at Felyx, an Amsterdam-based mobility startup, and created an end-to-end computer vision pipeline to detect wrongly parked vehicles.