Profile
I build end-to-end data systems that actually run in production.
My work sits at the intersection of data engineering, machine learning, and infrastructure: designing pipelines that move from raw
ingestion to reliable, monitored inference. I care about systems that do not just work once, but keep working: versioned data,
reproducible training, observable services, and models that survive real-world usage.
Background in biomedical engineering and research, now applied in industry solving high-volume, real-time data problems. I have worked
across the full lifecycle: data collection, modeling, deployment, and feedback loops, with a strong bias toward practical impact over
academic elegance.
This space is a hub for my work: experiments, systems, and projects bridging research and production.
Core Areas
Real-time & streaming data systems
ML lifecycle: training to monitoring
Experimentation, tracking & reproducibility
Data modeling, contracts & governance
Observability and production reliability
Edge data collection: IoT / LoRa / sensors
Beyond Core ML
IoT and edge systems: LoRa, sensors, distributed data collection
Data visualization: turning raw signals into decisions
Independent products: building data-driven tools and platforms
Photography: a slower way of observing systems in the real world
Research & Publications
Research remains part of the foundation: evidence, method, and disciplined iteration. My publication history sits mostly in the
biomedical engineering and applied research space, and it continues to shape how I build production systems today.
Biomedical engineering foundation
Grounded early work in experimental design, measurement discipline, and translating data into usable conclusions.
Applied research mindset
Reinforced the habit of reproducibility, traceability, and validating results instead of trusting intuition.
Production impact today
That same rigor now shows up in lifecycle-aware ML systems, governed data flows, and deployable inference services.