About

Tiago A. Almeida is an Associate Professor in the Department of Computer Science at the Federal University of São Carlos (UFSCar), Brazil, and a CNPq Research Productivity Fellow. A senior researcher in Computational Intelligence, he leads a broad research portfolio spanning Data Science, Machine Learning, and Natural Language Processing, with recent funded projects addressing critical challenges in areas such as autonomous driving, healthcare, agriculture, and Industry 4.0.

His contributions extend beyond research to active leadership in the international AI community. He serves on the editorial boards of leading journals, including Machine Learning and Data Mining and Knowledge Discovery (Springer), and has held key organizational roles in major conferences, most recently at the ECML-PKDD, serving as Journal Track Chair (2025) and Area Chair (2026).

With over 100 peer-reviewed scientific publications in high-impact venues, Prof. Almeida combines research excellence with strong educational engagement. He is the author of an award-winning textbook on Machine Learning, widely adopted in Brazil and Portugal for its clear treatment of both the mathematical foundations and practical applications of Artificial Intelligence.

Experience

More than twenty years leading cutting-edge CS research in Computational Intelligence, Machine Learning, and Data Science. Author of an award-winning and best-seller machine learning book and +100 peer-reviewed international high-quality scientific papers. Reviewer of dozens of specialized journals and ad hoc advisor to development institutions. His main areas of expertise include artificial intelligence, machine learning, natural language processing, pattern recognition, recommender systems, big data, and deep learning.

Machine Learning

Large experience in all ML project steps, from data acquisition to in-production ML models.

Natural Language Processing

Deep knowledge in text processing, representation, learning, and model evaluation.

Data Science

Vast know-how in statistics, data mining, and data analysis.