From TinyML to Agentic Edge AI: When Intelligence Earns Its Place at the Edge
Marcelo Rovai
Volunteer Professor
Universidade Federal de Itajubá
Marcelo Rovai is a Brazilian engineer based in Chile, working on Edge AI and TinyML education. He is a volunteer professor at the Federal University of Itajubá (UNIFEI), Brazil, where he holds the title of Professor Honoris Causa and teaches embedded machine learning courses that have reached students across Latin America and beyond.
His open e-books and tutorials, published on GitHub and Hackster.io, are used by universities and makers worldwide.
He is Co-Chair of the AIEng4D Academic Network (formerly TinyML4D) and of the EDGE AI Foundation’s Academia-Industry Partnership (EDGE AIP), initiatives that bring AI engineering education to universities in Latin America, Africa, and Asia.
Before moving to academia, he built a career in industry at Avibras Aerospace, AT&T, NCR, and IGT, where he served as Vice President for Latin America. He holds an engineering degree from UNIFEI, a specialization from the Polytechnic School of the University of São Paulo (POLI/USP), an MBA from IBMEC (INSPER), and a Master’s in Data Science from Universidad del Desarrollo (UDD), Chile.
LinkedIn: https://www.linkedin.com/in/marcelo-jose-rovai-brazil-chile/
Lectures, books, and tutorials: https://github.com/Mjrovai/TinyML4D
Beyond Prediction: Building Enterprise Decision Systems with Machine Learning, Optimization, and Agentic AI
What happens after an artificial intelligence model produces a prediction?
In enterprise environments, predicting demand, estimating risk, identifying anomalies, or estimating probabilities is only the beginning. Real value emerges when these predictions are transformed into decisions that are feasible, measurable, and aligned with business objectives.
This keynote explores the evolution from predictive models to enterprise decision systems that combine machine learning, optimization, operational knowledge, human judgment, and agentic AI. Rather than treating AI agents as universal autonomous solvers, the talk positions them as components of hybrid systems capable of coordinating specialized models, tools, workflows, and people.
The future of enterprise AI will not be defined only by our ability to build better models, but by our ability to design better decisions.
Dr. Carlos Jesús Vega Pérez
Senior Data Scientist
Wizeline
Carlos J. Vega holds a Doctor of Science degree in Electrical Engineering from Cinvestav, Mexico, as well as Master’s and Bachelor’s degrees in Electronic Engineering from Universidad Industrial de Santander, Colombia. He was also a Postdoctoral Fellow at the University of Naples Federico II, Italy.
He is currently a Senior Data Scientist at Wizeline and a professor in the Master’s Program in Data Science at Universidad Santo Tomás. With more than ten years of experience spanning academic research, technology, and industrial applications, he has contributed to the development of artificial intelligence and advanced analytics solutions for complex decision-making problems across multiple industries. His work has included industrial process optimization, digital twins, predictive maintenance, demand forecasting, recommendation systems, agentic AI, and enterprise machine learning platforms.
He is the coauthor of the book Nonlinear Pinning Control of Complex Dynamical Networks and has published his research in leading international journals in control systems, neural networks, smart grids, and artificial intelligence. His research and professional interests include enterprise AI, machine learning, mathematical optimization, agentic systems, complex networks, nonlinear optimal control, and neural networks.
Workshop: Hands-On Agentic Edge AI: SLMs, VLMs, and Agents on local devices
Jesús Alfonso López Sotelo
Full Professor
Universidad Autónoma de Occidente
Jesús Alfonso López Born in Cali, Colombia, he is an Electrical Engineer with a Master’s degree in Automation and a PhD in Engineering.
He has over 25 years of experience in teaching and developing projects related to Artificial Intelligence. His areas of interest include artificial neural networks and deep learning, edge AI, AI education, and the potential impact of this technology on society.
He is a Senior Researcher in the Colombian National Science, Technology, and Innovation System (SNI) of the Ministry of Science, Technology, and Innovation (MinCiencias). He is a Senior Professional Member of the IEEE, where he belongs to the Colombian chapter of the Society for Computational Intelligence. He also belongs to the AIEng4D Academic Network (formerly TinyML4D), an initiative that brings AI engineering education to universities in Latin America, Africa, and Asia.
He is currently affiliated with the Universidad Autónoma de Occidente in Cali, Colombia.
He has published numerous articles, book chapters, and books on Artificial Neural Networks, Deep Learning, and other artificial intelligence techniques. Among his intellectual output, his textbook, Deep Learning, Theory and Applications, stands out and is used as reference material in different higher education centers.