Paul Ramirez-Lopez

Applied Computer Vision Engineer with 3+ years of paid R&D experience developing practical computer-vision and image-processing workflows for PCB and microelectronics inspection, hardware assurance, and automated inspection systems.

Experience spans object detection, OCR and text analysis, component classification, anomaly-detection research, dataset engineering, large-scale annotation, multimodal imaging research, customer/SBIR delivery, and technical project leadership.

Research & Development Engineer at Caspia Technologies (2022-10 – 2024-10)

Applied CV/R&D role focused on PCB and microelectronics inspection, AutoBOM and counterfeit-detection research, dataset engineering, and technical delivery.

• Contributed to AutoBOM and counterfeit-detection R&D programs that converted PCB imagery into structured component, marking, text, orientation, and inspection metadata through computer vision, OCR, classification, and visual-association workflows.
• Developed and supported PCB inspection subsystems including YOLO-based integrated-circuit detection, surface-mount component classification, board/component-marking recognition, OCR/orientation workflows, and component-to-reference-designator association.
• Expanded a PCB vendor-logo and marking dataset to ~7,000 instances across ~1,300 marking types and contributed to ~300,000 total manual annotations spanning components, markings, text, orientation, reference-designator relationships, and OCR-support metadata.
• Investigated counterfeit and defect-detection approaches under severe data scarcity, including synthetic-data augmentation experiments and image-based surface inspection workflows for microelectronic components.
• Authored an independent ~40-page multimodal inspection feasibility study covering SWIR, infrared, X-ray, and other sensing methods; helped justify acquisition of a high-end SWIR imaging system and established initial experimental PCB imaging workflows.
• Produced weekly technical presentations and monthly SBIR-style deliverables documenting model development, dataset status, technical risks, and project milestones; onboarded and coordinated junior engineers across Physical Assurance R&D workstreams.
• Served as NSF I-Corps Entrepreneurial Lead for a seven-week commercialization program, conducting 100+ stakeholder interviews across electronics manufacturing, inspection, procurement, recycling, and supply-chain security.

Research Assistant at Florida Institute for Cybersecurity Research (2021-05 – 2022-10)

Hardware-assurance research foundation in PCB inspection, image processing, dataset construction, and ML experimentation.

• Supported early hardware-assurance and PCB inspection research that later expanded into AutoBOM and counterfeit-detection workstreams, contributing across dataset construction, annotation, image processing, and machine-learning experimentation.
• Developed PCB image auto-scaling and resolution-estimation workflows using reference-object detection, PCB boundary detection, edge/contour analysis, and known physical-reference geometry to estimate image PPI under inconsistent capture conditions.
• Built the initial PCB vendor-logo and marking dataset, curating ~3,000 examples across ~800 marking types for downstream vendor-logo, standards-marking, and board-marking recognition.
• Produced ~70,000 PCB annotations across components, markings, and text-related metadata, including localization, orientation, and class labels.
• Explored GAN-based synthetic counterfeit/defect generation for hardware-assurance datasets, identifying scarce confirmed negative examples and high visual variability as primary limitations to usable augmentation.

Account Development Representative → Customer Success Representative at Admiral (2018-07 – 2021-08)

Earlier SaaS customer-facing experience in outbound sales, customer success, issue resolution, and account communication.

• Supported outbound sales, customer success, issue resolution, and account communication for a SaaS platform serving digital publishers.
• Built early experience translating customer needs and product issues between users and internal teams.

B.S. in Business Administration, Management; Minor in Entrepreneurship – University of Florida (2020-05)

Specialization in Computer Vision Specialization – University of Colorado Boulder

Course in PyTorch for Deep Learning – DeepLearning.AI

Specialization in Deep Learning Specialization – DeepLearning.AI

Specialization in Machine Learning Specialization – Stanford/DeepLearning.AI

Course in Mathematics for Machine Learning – Imperial College London

Course in Python for Everybody – University of Michigan

Course in Statistics with Python – University of Michigan