Portfolio and Technical Background

MorphSignal Analytics is grounded in PhD-level research in computational bioengineering, biological image analysis, and machine learning. My work spans 2D and 3D microscopy, segmentation, registration, morphology quantification, neuron tracing, vascular tissue analysis, graph-based machine learning, and reproducible computational workflows, as well as taking advantage of agentic AI to streamline results while maintaining a complete understanding of pipelines.

I have developed methods and pipelines for low-contrast image segmentation, neuron morphology analysis, collagen and elastin microstructure quantification, nuclear shape analysis, image and trace registration, and machine learning-based structure extraction from complex image data.

Explore my publications and technical work (google scholar, github, linkedin)

CV

Selected work demos

Mesh analysis of segmented cell nuclei in 3D

3D reconstruction of teeth and skull from CT dental scans using unsupervised learning

3D reconstruction of collagen fibers in a mouse aorta (adapted from Baig, Vargas et al 2026, “An Integrated Multiphoton Imaging Workflow for Quantitative Analysis of Aortic Tissue Microstructure”)

Testing of automated tracing of neurites from the cerebral cortex of a mouse using diffusion-inspired graph neural networks