Descripción

Soy ingeniera especializada en Computer Vision e Inteligencia Artificial, y ayudo a empresas a extraer información útil de imágenes y vídeos mediante soluciones basadas en deep learning.

Trabajo con tecnologías como Python, OpenCV, PyTorch, Vision Transformer, YOLO..., desarrollando soluciones que van desde el análisis de imagen hasta sistemas completos de inferencia.

Tengo experiencia trabajando con diferentes tipos de imágenes (RGB, térmicas, profundidad e hiperespectrales), lo que me permite adaptarme a problemas muy diversos: desde detección de objetos hasta análisis más avanzados donde la información no es visible a simple vista.

Me encantaría ayudarte en tareas como:

- Detección y clasificación de objetos (YOLO, CNNs)

- Segmentación de imagen (instance / semantic segmentation)

- Procesamiento de imagen con OpenCV / scikit-image

- Análisis de vídeo y tracking de objetos

- Mejora y preprocesado de imágenes

- Desarrollo de modelos de deep learning adaptados a tu caso

Me enfoco en construir soluciones prácticas, eficientes y adaptadas al problema real, combinando conocimiento técnico con experiencia en distintos tipos de datos visuales.

Ámbito de especialización en el sector

Idiomas

Español
Bilingüe o nativo
Inglés
Competencia profesional completa
Francés
Nociones

Preferencias de lugar de trabajo

Solo teletrabajo

Lleva a cabo sus proyectos principalmente en remoto

AINIA,
Senior Computer Vision Engineer
BIOTECNOLOGÍA
julio de 2024 - Hoy (1 año y 11 meses)
Valencia, Spain
• Lead the design and deployment of industrial computer vision systems for process automation and quality inspection, including RGB, thermal, UV and 3D vision solutions.
• Develop and optimize deep learning models for detection, segmentation and inspection tasks, focusing on robustness and real-time performance.
• Drive end-to-end AI projects: from use-case definition and data strategy to deployment in industrial environments.
• Provided technical leadership in AI projects, defining architectures and guiding development decisions.
• Coordinated multidisciplinary teams across data, software and industrial integration.
• Built deep learning pipelines (YOLO, segmentation models) for defect detection, object tracking and scene understanding under challenging industrial conditions (occlusions, variability, lighting).
• Integrate AI vision modules with robotic systems to enable automated decision-making and closed-loop control.
• Evaluate and transfer state-of-the-art AI technologies into reliable, maintainable production solutions.
• Work directly with clients and stakeholders during design, implementation and deployment phases.
• Lead public and private R&D projects, including proposal writing, technical planning, execution and reporting.
Computer Vision Transformers YOLO multimodal scikit-image
INCLIVA,
Computer Vision Engineer
SALUD & BIENESTAR
julio de 2021 - junio de 2024 (2 años y 11 meses)
Valencia, Spain
• • Developed predictive AI models for therapeutic response and relapse risk in colon cancer patients using clinical and imaging data.
• • Designed multimodal ML pipelines combining medical imaging, clinical variables and radiomics features.
• • Applied supervised and unsupervised learning methods (Random Forest, SVM, KNN, Logistic Regression, DL models) for decision support systems.
• • performed data harmonization, feature engineering and exploratory analysis on large, heterogeneous dataset.
• • Collaborated closely with clinicians and researchers to translate domain needs into usable AI solutions.
• • Contributed to multicenter studies and peer-reviewed scientific publications in medial AI.
Data Annotation Image segmentation Data science Prediction Healthtech
Polytechnic University of Valencia,
Doctoral Researcher
SALUD & BIENESTAR
enero de 2018 - julio de 2021 (3 años y 6 meses)
Valencia, Spain
• • Implement advanced methods using deep learning to accurately segment spinal cords in MRIs of multiple sclerosis patients.
• • Develop, implement, and execute research protocols for effective data collection and analysis.
• • Perform analysis on real-world datasets from different sources, ensuring proper structuring and utilization in ML and DL models.
• • Analyze relevant literature to gain insight into the research project's key concepts, frameworks, and findings.
• • Coordinate with faculty members to ensure successful completion of research projects.
• • Perform rigorous testing and validation of computer vision algorithm, ensuring their effectiveness in real operational environments.
• • Research state-of-the-art architectures and algorithms used in deep neural networks.
• • Perform data pre-processing, feature engineering, and hyperparameter tuning for deep learning models.
• • Implement advanced deep learning techniques to detect and classify objects in images through the use of algorithms.
• • Optimize neural networks to improve accuracy and speed of model performance.
• • Explore 3D medical imaging data and advanced vision techniques, strengthening expertise in volumetric analysis and multimodal integration.
• • Document and present research developments, both internally and externally, improving project understanding and acceptance.
• • Design of a method that surpassed state-of-the-art solutions present in the state of the art.
PhD Computer Vision Image segmentation CNN Labelling

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Ph.D., Telecommunications.
Polytechnic University of Valencia
2024
Ph.D., Telecommunications.
Intensive Technical Data Science Program
Datamecum
2023
Intensive Technical Data Science Program