AI/ML projects

Each project leads with its outcome, says how it was measured and states its limits. 3 projects.

Multilingual scam detection for Indian SMS and WhatsApp, measured against a cheap baseline

Flags payment scams in code-mixed Indian SMS and WhatsApp messages, for the people who receive them.

Raised scam-detection F1 from 0.908 to 0.998 on a largely synthetic 1,554-message validation set by fine-tuning XLM-R with adversarial augmentation.

AI/MLLLMShipped

Scam F1, validation: 0.998; Baseline: TF-IDF + logistic regression 0.908; XLM-R base with adversarial augmentation on a 1,554-message validation split of a largely synthetic corpus, threshold 0.5, 95% CI 0.995–1.000 (results/evaluation_with_ci_aug.json). Evidence about the method, not real traffic

ScamShield flagging an example English scam message, with its urgency and suspicious-link tactics and the evidence for each

A recommender whose most important finding is that recency-weighted popularity wins

Recommends each shopper's next products from a catalogue of over a million items, with a reason for each.

Showed that recency-weighted popularity (HR@20 0.024 on 20,000 warm users) beat every learned model, including two-tower at 0.00025, by evaluating on a strict global timeline.

AI/MLIn progress

Warm-user HR@20, best source: 0.024; Baseline: Two-tower 0.00025, ALS 0.0053; Recency-weighted popularity on 20,000 warm users, exact full-catalogue ranking, one held-out target each (results/evaluation_electronics_0core.json); every learned model's paired-bootstrap difference against it excludes zero

Bar chart of RankStack's HR@20 on warm users by source: recency-weighted popularity 0.02395, popularity 0.0069, ALS 0.00525, co-occurrence 0.00295, two-tower 0.00025

Remaining-useful-life prediction for turbofan engines, with the test engines still unopened

Predicts how long each turbofan engine has before maintenance, with an honest error bar. A research demonstration.

Reduced remaining-life error to 7.40 cycles RMSE, against 8.90 for cycle count alone, in cross-validation over 19 engines, by training a 1D CNN; 10 test engines are unscored.

AI/MLIn progress

RMSE, best model (cycles): 7.40; Baseline: 8.90 from cycle count alone, with no sensors; 1D CNN, 5-fold cross-validation grouped by engine over 19 engines (results/phase3_sequence.json), 95% interval 4.61–10.20 resampled by engine; the 10-engine test group has not been scored yet

Bar chart of Prognos RMSE by model with 95% intervals: 1D CNN 7.40, Transformer 7.43, LightGBM 8.15, cycle count only 8.90, LSTM 13.00, training mean 23.76, per-channel trend 25.87