Here's a brief bio of me that includes snippets of my life.
I was born and raised in Seoul, Korea up until middle school, but went to high school in Jeju island-a volcanic island at the southern tip of Korea. I spent a lot of time biking and diving in the waters. Seeing the beauties of the waters made me want to study marine science in college.
A lot changed my first semester there. I took an introductory cs class taught in Snap!(berkeley's version of scratch) with the nudge of a friend and loved it. Maybe it was the combination of having a friend to do fun projects with along with many dorm friends also taking cs classes.
When covid struck I came back to Korea and served in the Korean Navy. I was on a battleship as a boatswain's mate, but towards the end of my service I met a friend who introduced me to ML. It was a happy combination of science and engineering. After my service, I did my first ML project in underwater CV where I turned my distorted diving photos into ones that were clean.
I got into lots of rabbit holes in this era, mostly in computer vision. It was an exciting time where big ideas were coming out each month.
And I started to wonder what it would be like to do these rabbit hole dives full time, away from classes for a while.
In the Spring of 2024, I started my gap year to study things full-time.
I discovered that explanability in approaches is a large quality I value and that it's so much fun making programs go fast.
And this got me into the world of making ML more efficient and faster.
I'm interested in ideas that share a theme of "doing more with less".
- Q. How can we make powerful and flexible, but also energy-efficient hardware fit for ML? [less energy]
- Q. How can we accelerate synthetic data-generation? [efficient post-training]
- Q. How could we create steerable-AI that's shaped through interactions? [less finetuning]
- Q. What are good ways to compress data? (i.e. How do we train smarter models using less data?) [efficient pre/post-training]
If any of this interests you, please reach out!
email: ko.hyeonmok at berkeley dot edu
github: henryhmko
The formats in this blog were inspired by Lilian Weng, Simon Boehm, and Fabien Sanglard.