IEEE WCCI 2026 Peer Reviewer · ML Researcher · Systems Developer @ Amazon Audible
Systems Developer at Amazon Audible, peer reviewer for IEEE WCCI 2026, and independent ML researcher with work currently under journal review.
I work on resource-constrained machine learning, compiler design, and production systems engineering. From migrating Player Services across AWS regions to building a single-pass SSA compiler in Rust, and proposing uncertainty-aware classifiers for TinyML deployment validated on NASA IMS and SemEval.
Most of what I build is small, fast, and deterministic. Open to collaborations on AI safety, selective classification, and embedded ML.
Revisiting Rosenblatt Perceptron: Robust High-Entropy Classification via Uncertainty Margins First-author paper introducing an uncertainty-aware linear classifier with adaptive abstention margin for TinyML deployment. ~1 KB memory footprint, 9 ms latency, benchmarked against Bonsai, FastGRNN, ProtoNN, and LSTM.
- Paper source: uncertainty-simple-perceptron.tex
- Implementation: uncertainty-simple-perceptron
Edge Python — Single-pass SSA compiler and threaded-code VM for a sandboxed Python subset. NaN-boxed values, dual inline caching, super-instruction fusion, pure-function memoization, mark-sweep GC; classes with inheritance and dunder protocol, async/await, pattern matching, and packages.json imports. around 170 KB WebAssembly module.
- Website: edgepython.com
- Live demo: demo.edgepython.com
- Source: edge-python
Edge Python Official Packages:
- Host Capabilities (repository): Official .wasm packages that embed host-side bridge code (e.g., JS) and expose it to Python through the capability protocol. Includes modules like requests for networking and DOM bindings for browser interaction.
