Domains
3D imaging workflows for quality assurance, non-destructive testing, and failure analysis. Typical use cases include porosity and inclusion detection, wall-thickness measurement, nominal-versus-actual comparison, dimensional inspection, and defect review for aerospace, automotive, energy, electronics, and advanced-manufacturing parts.
3D imaging and quantitative analysis for biomedical research, medical-device evaluation, anatomical structure analysis, CT- and MR-based modeling, and non-clinical workflow development. The emphasis is on visualization, segmentation, measurement, and generating analysis-ready outputs for researchers and engineering teams.
I am actively involved in AI-based segmentation and workflow design for volumetric datasets. That includes integrating automated segmentation into engineer-reviewed pipelines, building mask-validation steps, supporting batch execution, and turning AI outputs into measurable, reproducible 3D imaging workflows rather than one-off demos.
From segmented image data to watertight surfaces, repaired meshes, and downstream engineering assets, the workflow often continues beyond visualization. This area includes surface extraction, mesh cleanup, alignment, measurement, and preparation for CAD/CAE, additive manufacturing, or simulation pipelines.
A large part of my current interest is in applied 3D imaging R&D for materials analysis, additive-manufacturing quality assessment, device engineering, and advanced measurement workflows. These engagements often combine algorithm design, workflow definition, and production-grade software implementation.
I focus on technically demanding cases where complex 3D image data needs to become a measurable, reliable workflow: inspection, segmentation, quantitative analysis, geometry generation, automation, and downstream engineering integration.
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