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SKA Labs provides focused technical support and consultation for applied R&D efforts, early technical de-risking, and validation-oriented problems. We are best matched to projects that involve sensing, modeling, simulation, synthetic data, or difficult inference problems.
Physics-Informed Modeling and Simulation
We develop mechanistic and reduced-order models for physiological, optical, and dynamical systems. These models support concept development, feasibility studies, algorithm design, and proposal-driven research. Examples include:
Hemodynamic and physiological modeling
Sensor and measurement simulation for optical and fluidic systems
Reduced-order and surrogate model development
Scenario generation for difficult or rare conditions
Synthetic Data and Digital Twin Development
We build synthetic data pipelines and simulation-grounded validation assets when real data are limited. Examples include:
Synthetic waveform generation
Virtual pateint or virtual population frameworks
Benchmark and evaluation dataset design
Simulation-driven validation datasets
Machine Learning, AI, and Bayesian methods
We design inference pipelines that estimate hidden state from indirect measurements while preserving uncertainty. Examples include:
Sequential estimation and state tracking of physiological variables from sensors
Inverse problem formulation from mechanistic models and sensing schemes
Parameter inference from noisy measurements with posterior uncertainty
Sensor Design and Optimization
We support simulation-based sensor design workflows including optimization for target or adverse operating conditions. Examples include:
Population-robust wearble sensor design studies
Multi-fidelity simulation pipelines to discover non-intuitive sensor designs
Bayesian optimization workflows for sensor design and signal quality
Feature sensitivity and design tradeoff (Pareto frontier) analysis
Medical Computer Vision and Multimodal Perception
We develop computer vision and multimodal machine learning system for medical applications, especially where video and other sensor streams must be integrated.
Video understanding and workflow recognition
Multimodal systems combining video, sensor, and physiological data
Weakly supervised and data efficient learning from clinical video settings
Validation-oriented computer vision pipelines for ML evaluation
Technical Collaboration and Consultation
SKA Labs can support government-facing, industry, and university-partnered R&D efforts as a prime, subcontractor or technical collaborator. Examples include:
Technical concept development
Work package definition
Feasibility framing and methods support
Modeling and validation strategy
Best Fit Projects
We are especially interested in projects involving:
Cardiovascular sensing and physiological monitoring
Biosignals and multimodal sensor fusion
Medical computer vision and multimodal perception
Optical sensing, light tissue modeling, and optofluidics
Digital twins and synthetic data for AI
Healthcare and mission-critical sensing problems
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