Senior AI research engineering for medical imaging, computer vision, and spatial AI.

I help biotech and frontier-AI teams turn difficult R&D into working prototypes, validated pipelines, and production-oriented systems.

Selected experience includes Meta Reality Labs, QuantumCyte, AGAT Software, medical-imaging AI, spatial-AI research, computer vision, LLM systems, and mathematics-driven research engineering.

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Selected experience includes

Meta Reality Labs

Spatial AI, AR/VR, wearable-device prototypes.

QuantumCyte

Medical-imaging AI, precision workflows, patent filings.

AGAT Software

AI R&D leadership, LLM systems, customer-facing AI capabilities.

Mathematics background

M.Sc. Mathematics, modeling, abstraction, research reasoning.

Where I am most useful

I work where the risk is not normal software execution: messy scientific data, ambiguous research, precision-sensitive imaging, spatial representation, or AI claims that need serious technical review.

  • The method is research-heavy
  • The data is difficult or non-standard
  • The system must connect AI output to real workflows
  • The team needs senior judgment plus hands-on implementation

Services

Medical Imaging / Computer Vision R&D

Primary focus

For: Biotech and medical-AI teams

Senior technical support for imaging-heavy products involving segmentation, registration, calibration, spatial alignment, microscopy, pathology, or precision image-processing workflows.

  • Clear technical risk assessment
  • Prototype or production-oriented imaging pipeline
  • Improved spatial alignment, calibration, or workflow reliability
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Research Engineering Sprint

For: AI startups and R&D teams

Focused research-to-product execution for teams that need a prototype, paper implementation, benchmark, feasibility answer, or architecture path through an ambiguous technical problem.

  • Feasibility assessment
  • Working prototype or benchmark
  • Technical constraints and failure modes
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AI / CV Technical Due Diligence

For: Founders, investors, and technical leaders

Independent assessment of whether an AI, computer-vision, medical-imaging, or spatial-AI claim is technically real, scalable, defensible, and execution-ready.

  • Clear technical risk map
  • Feasibility and scalability assessment
  • Architecture and data-quality review
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Selected Work

Medical Imaging AI Pipeline for Precision Workflows

Medical Imaging AI

AI-based software connecting medical segmentation, scan interpretation, geometric calibration, spatial alignment, and precision output generation.
Close to 5 µm precision workflow / 2 patent filings

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Spatial AI and Wearable Interaction Prototypes

Spatial AI / AR-VR

Research and prototype work involving wearable-device interaction, spatial representations, and multimodal embeddings for AR/VR environments.
Working demo device-oriented prototype / NDA-sensitive research context

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TrackEverything — Model-Agnostic Tracking Layer

Computer Vision / Tracking

A Python package that can wrap detection or classification models and improve video predictions using temporal consistency, tracking, and statistical evidence across frames.
Open-source technical package / Model-agnostic integration design

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Doppler Radar Target Classification

Signal Processing / Machine Learning

Signal-processing and machine-learning pipeline for Doppler-pulse radar target classification using spectrograms, filtering, and constrained deep-learning experimentation.
30th competition placement / 1,000+ participants

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Best Fit / Not a Fit

Best fit

  • Biotech or medical-AI teams with difficult imaging, segmentation, registration, calibration, or spatial-alignment problems
  • AI startups translating research ideas into working prototypes
  • Founders, CTOs, or investors who need senior technical judgment before committing budget
  • Teams that need hands-on implementation, not just strategy

Not a fit

  • Commodity dashboard work
  • Low-budget MVP factories
  • Academic ghostwriting
  • Crypto projects
  • Vague AI automation work without a serious technical problem

Engagement model

1. Technical assessment

Clarify feasibility, risks, data constraints, and execution path.

2. Sprint or architecture plan

Build a prototype, benchmark, or implementation roadmap.

3. Retainer or advisory

Provide ongoing senior R&D capacity and technical ownership.

Have a difficult imaging, computer-vision, or spatial-AI problem?

Send a short technical brief with the problem, current stack or data type, timeline, and what failure would cost.

Available for selected contract, advisory, and retained R&D engagements.

Request a technical assessment