Carlos Sánchez Mendoza

Led the AI/research function, the evaluation discipline, and the full processing backend behind Let's Enhance and Claid.ai. Shipped generative-image pipelines (diffusion, GANs, vision-language models, LLMs) for branded visual content in e-commerce and marketing, and led the R&D and architecture of visual agentic orchestration for the next-generation platform.

Led the AI/research function, the evaluation discipline, and the full processing backend behind Let's Enhance and Claid.ai. Shipped generative-image pipelines (diffusion, GANs, vision-language models, LLMs) for branded visual content in e-commerce and marketing, and led the R&D and architecture of visual agentic orchestration for the next-generation platform.

Leading two Data Science/ML innovation teams sitting in the Product pillar, and cross-functionally a product analytics and a productization team.

Bootstrapping (hiring, mentoring, developing guidelines, managing projects and roadmaps, bringing new content personalization models trained on multi-billion record datasets to production, and aligning C-level) the new Data Science department in a successful ed-tech scale- up with over 330,000 daily primary education students in the Netherlands, Belgium, Spain and the US.

I founded TEXALIGN to host product development and commercialization of technology conceived during my 3-year Postdoctoral M+Vision Fellowship at Massachusetts Institute of Technology

Leading several initiatives related to vision (face recognition, person tracking, affective vision), NLP (topic modeling, voice recognition, conversational agents) and IoT systems, in knowledge discovery and retail applications and solutions.

Currently leading a team of 12 engineers and developers.

European Commission Marie Skłodowska-Curie fellowship for the translation and commercialization of MIT-originated biomedical technology.

Postdoctoral Fellowship on Translational Biomedical Technology Innovation

Innovative program in Translational Biomedical Technology for development of high-impact, independent, fast-paced research plans for novel need-driven medical technologies, from conception to commercialization, based on the biomedical technology innovation model developed at MIT Health Science and Technology Division.
Acti

Lecturer and Researcher

Computational anatomy of the pediatric cranium for diagnosis and surgical planning of craniosynostosis
Deformable models and graph-cuts for pediatric renal ultrasound segmentation

Researching medical image processing algorithms with a focus on 3D medical image segmentation, texture analysis for medical applications and shape analysis for allograft surgical planning.

Lecturing Digital Communications, Image Processing, Medical Image Processing, Signal Processing, Communication Theory

Developed a new approach for automatic characterization of emphysematous tissue in HRCT

Developed a new approach for automatic characterization of emphysematous tissue in HRCT

Ph.D Thesis: "Image Processing in Medicine. Advances for Phenotype Characterization, Computer-Assisted Diagnosis and Surgical Planning"

Focus on Image Processing.

Final Thesis on Digital Image Processing. "Design, Implementation and Benchmarking of Texture-Based Colour Image Segmentation".