Lead Machine Learning Engineer and Professor specializing in AI systems, deep learning, and MLOps. Building innovative solutions at the intersection of machine learning and software engineering with expertise in generative AI, computer vision, and large language models.
As a Professor of Software Engineering at UCU, I have been actively involved in teaching and mentoring students across various courses in software engineering, data structures, and artificial intelligence using Team-Based Learning (TBL) methodologies.
Leading machine learning projects across various industries, focusing on MLOps, generative AI, forecasting, and computer vision applications.
Led technical development of products transforming traditional instruction manuals into interactive visual guides using advanced technologies.
Key Achievements:
Led development of an end-to-end human resources system for employee attendance and work hours, integrating with various biometric devices.
Enhanced electronic billing web solution and supported various side projects, improving frontend features and developing desktop clients.
Core focus on software engineering and computer science fundamentals.
Grade: 4.58/6
Skills: Machine Learning, Deep Learning, Node.js, Docker, Git, English, Artificial Intelligence, Python, C#, HTML, Java, JavaScript, SQL, Computer Vision
High school education with focus on engineering principles.
Expertise in developing and deploying ML models
Full-stack development with modern technologies
University professor using Team-Based Learning methodology
Specialized technical capabilities
Building applications across various domains
Leadership and professional contributions
Certification in AI agents and their fundamentals.
Comprehensive course on large language models (LLMs) covering fundamental deep learning techniques and practical applications.
Professor: Giovanni Trappolini – Universidad de Roma La Sapienza
Key Topics: Deep learning fundamentals, Attention mechanisms, Transformer architectures (BERT, GPT), Transfer learning, fine-tuning, prompting, and Retrieval-Augmented Generation (RAG)
Course covering estimation of 3D shape of humans from images or video, a key task for applications like AR/VR, telepresence, and entertainment.
Professor: Victoria Fernández Abrevaya
Focus on both classic and modern techniques for estimating body and face in 3D from images.
Validates expertise in designing, building, and productionizing ML models. Valid until July 2025.
Course on deep reinforcement learning techniques and applications.
Certification on handling research data and specimens. Valid until May 2025.
Certifications in Data Cleaning, Game AI and Reinforcement Learning, Natural Language Processing, Computer Vision, Intermediate Machine Learning, Intro to Deep Learning, and Intro to Machine Learning.
Advanced English language certification.
Intermediate English language certification.
Article on fine-tuning Large Language Models and implementing distributed training techniques to improve conversational AI applications.
Technical guide on optimization techniques for transformer-based models to improve inference performance and efficiency.
The differential diagnosis of respiratory diseases is usually a challenge for medical specialists in the first line of care, increased under the current COVID-19 pandemic. A Clinical Decision Support System -CDSS- is being developed using Bayesian Networks – BNs – to help physicians diagnose respiratory diseases, including those related to COVID-19. Network structure has been elicited from expert physicians, and network parameters (diseases prevalence, symptoms, findings, and lab results conditional probabilities) were extracted from relevant bibliography or currently standard global information sources. The CDSS is being tested using case studies taken from real situations, provided and validated by physicians. The resulting system demonstrates the suitability and flexibility of BNs for diagnosis support and healthcare training.