Research

Independent technical research

Recent work focuses on machine learning, signal processing, synthetic data, and scientific computing, with an emphasis on building and documenting methods rather than treating research as purely theoretical.

Research projects

Methods made concrete through implementation.

These projects show how research ideas are translated into working systems, technical writing, and reproducible experiments.

Wavelet-GAN Bitcoin Forecasting

A multi-year research arc on wavelet-conditioned GANs for short-horizon Bitcoin price-change forecasting, from the original concept paper through to a controlled re-evaluation against matched baselines.

Role: Independent researcher and ML systems designer
Methods: Dual-tree complex wavelets, conditional GANs, MATLAB and Python, ridge and TCN baselines, chronological validation
Outcome: Threads four linked reports together: concept, first WaveGAN experiment, refined architecture, and a controlled re-evaluation that ultimately favors simple baselines.

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Deep Wave Research

Selected Topics in Quant Finance

Six standalone quantitative-finance studies: FOMC statement sentiment against a 2-year yield proxy, intraday spectral signatures in SPY and BTC, order-book imbalance as a one-second predictor, a change-point detector bake-off on SPX realized volatility, cross-sectional intraday reversal at multiple horizons, and a public-data replication audit of a real-time equity-ML paper.

Role: Independent researcher and quantitative analyst
Methods: Event studies, NLP sentiment scoring, signal processing, market microstructure, change-point detection, cross-sectional factor research, replication auditing, bootstrap and significance testing, Python
Outcome: A set of careful, reproducible studies, each an honest result about what does and does not hold in intraday, cross-sectional, and risk-signal data.

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Deep Wave Research

The research company I started in 2020 for my independent studies — first algorithmic trading and web-3 systems, then a run of quantitative-finance investigations, and now signal-processing and deep-learning research well outside markets.

Role: Founder and independent researcher
Methods: Pre-registered study design, explicit baselines, cost- and regime-aware evaluation, signal processing, probabilistic forecasting, model governance
Outcome: Holds three research programmes to one standard of evidence — explicit baselines, pre-declared gates, and every finding reported at the confidence its evidence supports — with the company's own research library published at deepwave.ca.

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AI and Art

A compact exploration of how AI tools shape image-making, authorship, and creative workflows.

Role: Researcher and visual explorer
Methods: Comparative analysis, image generation, critical review
Outcome: Captured a concise perspective on AI as both a technical medium and a creative tool.

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Deep Wave Research

Regime-Adaptive Probabilistic Forecasting

A staged research programme on distributional forecasting of BTCUSDT perpetual futures: groundwork on frozen foundation-model embeddings, a pre-registered scan of where the series carries learnable signal, and a completed walk-forward comparison of from-scratch and foundation-model forecasters staged on those verdicts.

Role: Independent researcher and ML systems engineer
Methods: PyTorch, return and excursion quantile supervision, Chronos-T5 and Moirai-MoE backbones, LoRA fine-tuning, conformalized quantile regression, pre-registered verdict thresholds, purged walk-forward evaluation
Outcome: Establishes that the usable signal is distributional rather than directional, maps where it lives across timeframes and horizons, and shows a small from-scratch encoder is the quality-versus-cost frontier over foundation-model lanes — with fine-tuning helping exactly where a frozen representation is deficient.

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Doppler-Domain Micro-Motion Tomography

A reproduction, critical analysis, and benchmark of single-SLC Doppler-domain micro-motion tomography: the published method rebuilt as an extended CUDA pipeline, then tested against synthetic ground truth and falsification-oriented null tests.

Role: Independent researcher and software engineer
Methods: C++, CUDA, OpenMP, TerraSAR-X SSC processing, synthetic ground truth, null tests, 48-hour benchmark suite
Outcome: Proves five signal-level structural results the source papers omit, implements the corrections they imply, and separates what the method delivers from what it does not: the front end recovers injected micro-motion, while tomographic localization does not clear its own pre-registered shuffle-pairs and sign-flip floors.

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Deep Wave Research

Wavelet-Domain ENSO Forecasting

Eleven linked documents on forecasting tropical-Pacific sea-surface temperature inside an exactly invertible complex-wavelet space: a standalone publication, a measurement charter, and nine reports running from system validation to a live-deployment handoff.

Role: Independent researcher and ML systems engineer
Methods: Dual-tree complex wavelets, Earthformer cuboid attention, CMIP6 forecast pretraining, sequence diffusion, EMOS and ECC-Q calibration, hash-gated sealed testing, moving-block bootstrap governance
Outcome: Threads a whole research programme together with its evidence tiers intact: one pristine sealed-test win, two controlled studies that narrowed the design space, a probabilistic stack whose validation advantage only partly transferred, and a deployment contract handed to a prospective live test.

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