Romulo Stringhini
Documentation Technician @SMT Intelligence
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WORK HISTORY
Documentation Technician @SMT Intelligence
Laval, QC, CA
Manage and maintain product Bill of Materials (BOM) within the company\'s ERP system, ensuring data accuracy and alignment with client technical requirements- Verify and validate technical documentation and component lists, identifying discrepancies in quantities, references, and assembly instructions before production release- Process structured technical data by transferring and organizing submission files, supporting procurement and manufacturing workflows- Ensure data integrity and traceability by tracking BOM modifications and communicating updates to engineering and production teams- Coordinate shipping and logistics documentation, including delivery notes, customs documentation, and carrier information- Apply strong analytical and data-validation skills to maintain documentation quality and support efficient production planning.
EDUCATION
Universidade Federal de Santa Maria
Master of Science - MSc
Federal University of Rio Grande do Sul
Doctor of Philosophy - PhD, Computer Science
Universidade Federal de Santa Maria
Bachelor's degree, Computer Science
ABOUT ROMULO STRINGHINI
Machine Learning Scientist with a PhD specializing in Machine Learning and Computer Vision, combining strong theoretical foundations with extensive hands-on experience in applied research and advanced R&D. Experienced in designing, developing, and evaluating machine learning systems for complex visual and geometric data.Specialized in developing and training modern architectures including Convolutional Neural Networks (CNNs), Vision Transformers (ViTs), and hybrid attention-based models. Experienced across supervised, unsupervised, and self-supervised learning, with a strong focus on representation learning and feature extraction in high-dimensional visual data. Research background in 2D and 3D visual understanding, including object classification, object detection, segmentation, depth estimation, gravity alignment, spherical image processing, and medical image analysis.Experienced in designing and maintaining end-to-end machine learning pipelines, covering data preprocessing, augmentation, training, validation, hyperparameter tuning, and quantitative evaluation using task-specific metrics. Adept at conducting ablation studies, debugging complex training dynamics, and optimizing models for stability, scalability, and reproducibility. Published researcher with multiple peer-reviewed papers in relevant international conferences. Actively engaged with the scientific community as a reviewer for the Journal of the Brazilian Computer Society (JBCS).Programming Languages: Python, SQL, MATLAB Machine Learning Frameworks: PyTorch, TensorFlow, Keras, Scikit-learn, Hugging FaceData Processing and Visualization: NumPy, Pandas, OpenCV, Seaborn, Matplotlib, Nibabel, Pydicom3D and Geometry Processing: Trimesh, Open3D, MeshLab, Blender (Python API)
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