Ki-Ung Song
AI grounded in mathematics
Edge AI, AI for Science, AI for Finance
Deepest
Deep Learning Research Society at Seoul National University
Overview
Deepest gave me a way to keep doing serious deep learning work and testing ideas outside my job, with strong peers.
How I Engaged With Deepest
I took part through weekly seminars, focused study groups, seasonal team projects, and challenge teams. The value was in moving between reading, presenting, implementing, and competing rather than staying in one mode of learning.
Selected Explorations
Seminar Presentations
Ongoing
A recurring part of Deepest for me was preparing and giving seminars on mathematically demanding deep learning topics, including:
- Neural Tangent Kernels
- Singular learning theory
- SO(3)-equivariance
- Deep learning for simulation
Parallel Computing Study
Oct 2023 - Feb 2024
A study group focused on GPU computing, CUDA, and performance-oriented deep learning workloads, which helped strengthen my systems intuition around efficient deep learning.
NTIRE 2022 Challenge Team
Nov 2021 - Apr 2022
A competition-focused team project on image super-resolution, where we explored high-frequency detail and achieved strong workshop rankings. This work later led to a publication.
Offline RL Project
Nov 2021 - Feb 2022
An implementation-focused project for understanding offline reinforcement learning through direct experimentation in PyTorch, which made the topic much more concrete than reading alone.
What It Kept Alive
Deepest kept my technical curiosity and ambition active. It helped me stay current and keep a wider sense of the field than day-to-day specialization usually allows.