Computational psychiatry on 12,883 Iowa Gambling Task choices from heroin users, amphetamine users and controls (Ahn et al., 2014). Fitted three reinforcement-learning models (PVL-Delta, VPP, ORL) to every person in PyTorch, then ran a fair forecast test: a GRU trained only on other people predicted each person's next 40 choices better than every psychology model. Tested whether decisions reveal addiction (weak signal: heroin partly, amphetamine not), and built an LLM agent that plays the same task with and without chain-of-thought. Personal, 2026.

Japanese and English product search on Amazon's ESCI data: BM25 with MeCab, multilingual E5 embeddings, a tuned hybrid and cross-encoder reranking, both models fine-tuned on ESCI, each scored on 1,000 held-out queries per language. Also tested an open LLM as a relevance judge (it did not match human labels). Plus time-split purchase recommendation (EASE, ALS) and a FastAPI service timed on a laptop CPU. Found that Japanese word segmentation beats any model change, and that "buy it again" beats every learned recommender on repeat purchases. Personal, 2026.

Visual inspection (PatchCore on VisA: PCBs, capsules, cashews) and bearing-vibration monitoring (CWRU), both trained on healthy data only, then shrunk to int8 ONNX and timed on one CPU thread. Found that nearest-neighbour search, not the network, sets edge latency, and that a perfect AUROC still gave 100% false alarms at an unseen motor load until a 2-second recalibration. Personal, 2026.

CNN-LSTM and a hybrid deep + physiological-feature model on chest ECG, EDA and breathing signals (WESAD), evaluated only on people the model never saw. Rescued a first version that scored below chance, with an ablation study, seed ensembles and CI-tested code. Personal, 2026.