Raj Singh-Moon, PhD
Shipping ML in FDA-cleared medical devices. Research → production — from optical physics to models that make it onto the device.
Engineer, scientist, developer — one person, all three.
Hybrid scientist-engineer. I build multimodal sensing and ML systems end-to-end — sensor integration and calibration, data processing, model development, failure analysis, and edge deployment — for clinician-facing capabilities in physiological assessment and triage.
PhD, Columbia EE (2019). Dissertation on biomedical optics and computational imaging — light-transport simulation and inversion validated against bench-top phantoms. MSc, Columbia EE (2016). BSc, NYU EE (2012). Ten-plus peer-reviewed papers across optics, clinical imaging, and ML.
Full-stack across the stack. Optical physics and CUDA Monte-Carlo simulation, CV/ML pipelines running on CM4/CM5, Jetson, and Luxonis OAK, production C# for device control, and the team and roadmap behind it all — I operate comfortably at each layer.
Operating ranges
- Biomedical sensingEnd-to-end
- Optical physics · CUDAExpert
- ML / deep learningExpert
- Edge / embeddedCM4 · Jetson · OAK
- FDA-cleared workflowsShipped
- Team leadershipDirector · 1 → N
Models that ship — measured by what they moved.
A selection of production and research work across imaging, ML, and team building. Impact numbers are program-level outcomes, not offline benchmarks.
Physics-informed CV & neural inverse solvers
End-to-end deep-learning models trained against the light-transport forward model — spectral reflectance to absorption and scattering coefficients in sub-ms, with physically consistent predictions on-device.
Object detection & semantic segmentation
Detection and segmentation models that localize regions of interest and classify tissue in multispectral captures — gating downstream inversion so oxygenation and tissue-structure outputs land on the right pixels.
Real-time edge deployment
End-to-end inference on RPi CM4/CM5, Jetson Nano, and Luxonis OAK — power, memory, and performance optimized so the full oxygenation pipeline runs on-device.
Data-science function from scratch
Built and lead a cross-functional team spanning ML research, embedded integration, and product software — research to shipped device feature, in-house.
Applied GenAI for clinical workflows
Foundation-model integration with LoRA adapters and supervised fine-tuning — powering capture guidance, automated triage outputs, and reporting and review tooling.
GPU Monte-Carlo light transport
CUDA photon-transport simulator for layered tissue — the ground truth behind every optical-property inversion, from PhD bench work through production inference.
Multi-sensor embedded integration
Camera sensor boards, custom stereo depth modules, ADC temperature sensing, DAC LED current control — photometric calibration and acquisition synchronization across the full optical chain.
Fifteen years, one direction.
Lead a cross-functional effort to prototype and ship clinician-facing capabilities for a mobile vascular imaging device — oxygenation assessment and burn triage, from sensor to model to on-device inference.
- Built end-to-end physics-informed computer-vision and neural inverse-solver models for the mobile imaging device — constraining training with the light-transport forward model so oxygenation and tissue-structure predictions stay physically consistent.
- Shipped CV/ML pipelines for oxygenation inference and tissue-structure analysis, running on-device from capture to clinician-ready output.
- Integrated multi-sensor hardware — camera sensor boards, custom stereo depth modules, ADC temperature sensing, DAC LED current control — on RPi CM4/CM5, Jetson Nano, and Luxonis OAK.
- Built photometric calibration and acquisition synchronization across the full optical chain, with real-time edge optimization for power, memory, and performance.
- Delivered clinical decision support and workflow automation — capture guidance, automated triage outputs, reporting and review tooling — plus the backend and frontend apps operating the device.
- Applied GenAI: foundation-model integration with LoRA adapters and supervised fine-tuning for clinical workflow automation.
- Own hiring, technical direction, and career development across ML, embedded, and product software.
Led production software and algorithm work for medical device control, acquisition, and inference — OOD, CI/CD, Agile/Jira — with authored system and software design docs.
- Developed object-detection and semantic-segmentation models for the imaging pipeline — localizing regions of interest and segmenting tissue classes to gate the downstream optical-property inversion.
- Delivered ~60× computational speed-up and ~30% accuracy gain via algorithmic redesign and GPU optimization.
- Shipped production-grade C# for device control and data acquisition, with validation planning and root-cause investigations feeding the regulatory path.
Built image-quality assessment pipelines for an FDA-cleared imaging device, and translated Python/MATLAB research prototypes into production C# under TDD.
- Authored the image-quality pipeline that became the cleared-device acceptance test, closing the loop between research and release.
- Established the Python/MATLAB → production-C# translation pattern later teams standardized on.
GPU-accelerated real-time imaging analysis in the Structured Functional Imaging Lab, paired with experimental validation studies.
- Built a GPU imaging-analysis interface for real-time reflectance and OCT data collection.
- Ran experimental validation studies that grounded the computational pipeline in physical measurement.
PhD in Electrical Engineering — biomedical optics and computational imaging.
- Built light-transport simulation and inversion machinery (Monte Carlo / radiative transfer) in CUDA, validated against bench-top tissue phantoms.
- Designed and operated the optical and imaging hardware behind those studies — catheter-based probes and spectroscopic front-ends.
- Co-authored peer-reviewed work on near-infrared tissue quantification and microcirculatory imaging.
Featured peer-reviewed work.
Field notes.
Open to senior-IC and Director-level roles in applied ML, biomedical sensing, and edge intelligence.
Happy to talk about ML against physical systems, shipping inference into regulated devices and onto the edge, and building cross-functional teams that take research all the way to clinician hands.
contact@rajinder.dev