Résumé (PDF) · Runnable experiments & results · Portfolio · LinkedIn

AI and product engineer building creative tools, applied machine-learning systems, and production software. I have shipped music products used at scale, built research prototypes for DARPA INGOTS with NARF Industries and Dartmouth's LISP Lab, and completed theses spanning an AI-native DAW, neural audio restoration, and music composition and production.

My work centers on music, machine learning, signal processing, and product engineering. I care about systems that are useful beyond a demo: clear evaluation, honest limitations, human review where it matters, and documentation that lets another engineer reproduce the result.

• MODULO — an AI-native desktop music workstation developed as my M.S. thesis and product research platform.
• Automatic music mastering — waveform and neural-codec experiments for learned music mastering.
• DataGo — retrieval-augmented search research with runnable cosine-retrieval and adversarial-tree diagnostics; full Go integration remains unfinished.
• Federated ASR gradient inversion — CTC-aware reconstruction research, with a reproducible synthetic privacy diagnostic and explicitly documented full-speech requirements.
• Prediction-market research agent — human-reviewed evidence discovery and monitoring for prediction-market research.
• Transformer melody generation — a readable TensorFlow encoder–decoder Transformer for symbolic music generation.

For saved plots, MIDI/audio, baselines, test runs and honest experiment limits, start with the 22-project reproducibility index.

Python · TypeScript · C++ · PyTorch · React · Node.js · PostgreSQL · Docker · GCP · JUCE · DSP

My recent work includes real-time collaborative products, audio-model evaluation, LLM tool and agent systems, and graduate machine-learning instruction.

• Portfolio and writing: takakhoo.com
• LinkedIn: linkedin.com/in/takakhoo
• Research paper: Reconstructing Long-Form Speech from Federated ASR Gradients