- 😄 I'm Mark (he/they). I just graduated UChicago studying Math/CS
- 🌱 I've been doing this for a while, so my skills are broad (Linux/SysAdmin/Bash, Web/Vue/React, Go, Rust, Python, R, C)
- 🔭 I’m currently working on AI research (mostly in intepretability) with Victor Veitch (https://arxiv.org/abs/2506.01197), Ari Holtzman (https://arxiv.org/abs/2606.00831v1), Micah Goldblum, and Chenhao Tan
- 👯 I'm always looking for more opportunities in machine learning research
- 📫 I'm available at muchane@uchicago.edu
- I started coding when I was very young. My imagination was first sparked by an Intel Core 2 Duo computer that I wanted to put a mail server on.
- From there my learning followed my imagination. After my first explorations I learned Python. Later, I got a rack server and expanded my Linux skills. I got more serious about coding and learned Go. I needed to build apps so I learned web technologies and later Vue. The summer before 9th grade, I was really into systems and learned Rust and C.
- During high school, I built on my young imagination in more concrete ways. I had the opportunity to teach 2 student led classes in Natural Language Processing and Computer Vision, got an app to the national finals in Samsung's Solve for Tommorrow competition, attended and placed at North Carolina's inaugural datathon, and more.
- During junior and senior year of high school, I got the chance to start research. As a junior, I built a completely novel database of Federal court decisions for NLP using the Harvard Law Library, Go, and MongoDB at Stony Brook University with the Simons Research Fellowship. As a senior, I worked in Computational Astrophysics at High Point University which utilized all my skills from Machine Learning in Python to databases and concurrency in Go. This also gave me my first exposure to high performance computing through the Pittsburgh Supercomputing Center.
- I did work at Globus Labs and GenForward on super cool projects in AI/Science and Social Science in my first 2 years at UChicago
- Since then, I've worked in ML/AI research, largely in interpretability