Machine Learning & Vision .

Three vision models vote on malware rendered as images, across 17 families. Now a first-author paper on arXiv.

My thesis. I turned malware files into pictures and taught three vision models to recognize the family on sight. They vote together and agree 94 percent of the time across 17 families. It became a first-author paper, now on arXiv.

A 1D CNN on raw phone sensor windows detected falls at 99.35 percent, and 98.1 with every test subject unseen.

Fall detection and activity recognition from raw smartphone accelerometer and gyroscope data, comparing classical models, deep networks on raw windows and transfer learning from spectrograms. A 1D CNN detected falls with 99.35 percent accuracy, and held 98.1 percent when every test subject was unseen.

Four models study every palm scan and vote, reaching 99.75 percent AUC.

Contactless identity from the palm of your hand. Four models study every scan and vote on the verdict, and together they reach 99.75 percent AUC.

A feature network identified ten walkers at 90 percent from six-channel phone data; an LSTM on the raw signal underfit at 57.

Identifying and verifying people from the way they walk, using a phone's accelerometer and gyroscope. A feature-based network identified walkers with 90 percent accuracy and verified them at a 3 percent equal error rate; an LSTM on the raw signal verified at 2 percent.

A two-tower retrieval model and a neural ranker, built in PyTorch.

A two-stage hybrid movie recommender, built the way production systems are. A two-tower neural model retrieves candidates through Proxima, my own C++ vector search engine; a neural ranker reorders them. It answers free-text requests like 'a slow-burn sci-fi like Arrival but funnier' and includes movies in theaters this week.

XGBoost on the cleaned data to find what actually makes a song popular.

A million messy music records that shared no common keys. I joined them with C++ MapReduce on a Hadoop cluster, then asked the clean data what actually makes a song popular.