☾ MACHINE LEARNING · RESEARCH · PYTHON ☾
CS student at Beijing Institute of Technology. I research how models learn when data is scarce, focusing on small-sample learning, conformal prediction, and reinforcement learning.
I build open-source libraries that bridge the gap between theoretical guarantees and practical implementations — from robust gradient boosting to calibrated neural networks. When I'm not writing code, I'm reading papers or solving integrals.
Ⅰ. SmallGBM Gradient boosting for small datasets
A gradient boosting implementation focused on tabular learning when data is scarce. Native C core, robust leaf regularization.
ROC-AUC: 0.9101|22 datasets|PyPI
Ⅱ. SmallMLP Non-parametric regression & conformal sets
Adaptive MLP that replaces manual architecture search with dataset-derived hyperparameters, producing calibrated prediction intervals.
25/45 wins|31/32 wins (width)|PyPI
Ⅲ. RL-from-scratch Value-based reinforcement learning
Implementing algorithms from first principles to understand the mechanics behind agents, rather than treating them as black boxes.
DQN|Double DQN|Dueling DQN
☾ BEIJING · CHINA · 2026 ☾