§3.04 Desktop E2EE messenger implementing full Signal cryptographic stack (e.g., X3DH and Double Ratchet) totalling ~20,000 lines of code. Security properties are formally verified using TLA+ and Alloy, with in-transit ciphertext validated against the NIST 800-22 statistical test suite. This as a course project that far exceeded baseline requirements.
§3.05 Explores the accuracy-latency Pareto frontier in AIoT systems. Existing dispatchers sacrifice one for the other. I implement Lyapunov drift-plus-penalty with auto-tuned V and a time-quantized dynamic programming planner that jointly optimize over the decision space. The DP planner pushes the Pareto frontier furthest, achieving near full-model accuracy with under 1% deadline misses across all tested arrival patterns.
§3.07 Proposes a unified framework that moves clinical ML beyond binary prediction. Instead of treating classification, clustering, and pattern mining as isolated tasks, I connect them through principled information flow: calibrated risk estimates guide manifold analysis, which in turn generates clinical hypothesis. The framework surfaced numerous clinically validated phenomena.
§3.07 Empirically investigated whether pretrained foundation models obsolete both classical methods and manual feature engineering on tabular data. Through comprehensive ablations, our findings: they do, and the best preprocessing is none at all. We also managed achieved Top-10 result on Kaggle's House Price Prediction challenge.