second-year mathematics student at the university of waterloo.
i'm interested in ml, deep learning, and ai, from the math underneath to the systems built on top. lately that's meant implementing things from scratch and training models to see what actually happens.
languages
ml & data
tools
gpt-2 small (124m) from scratch in pytorch, checked against hugging face. kv caching reached 175 tokens/s on an nvidia gb10, a 10.5× speedup for a 992-token prompt + 32 generated tokens.
a scalar autograd engine and neural net library from scratch in c++20. numerical gradient checks, xor training, and 93.8% mnist test accuracy without ml libraries.
a study assistant over my linear algebra notes, with cited answers and local models. reranking raised test recall@1 from 80.9% to 92.6%, measured on labeled evidence in a frozen 40-question set.
a cnn trained on pathology images, with corruption tests and label-free shift detection. mmd caught the clinical-center shift in all 100 trials at 200 images per batch, with a 5% false-alarm rate on clean batches.