Mario Graff

Mario Graff is an Investigador por México at the Ministry of Science, Humanities, Technology, and Innovation (SECIHTI), commissioned to INFOTEC, where he has worked since 2014 and currently coordinates the PhD Program in Data Science. He is a Level II member of Mexico’s National System of Researchers. He earned his PhD in Computer Science from the University of Essex under the supervision of Riccardo Poli, and holds both a Master’s degree in Electrical Engineering (Computational Systems) and a Bachelor’s degree in Electrical Engineering from the Universidad Michoacana de San Nicolás de Hidalgo, where his thesis advisor was Juan J. Flores.

He has held academic positions as Visiting Assistant Professor at Colgate University during his 2021–2022 sabbatical leave and previously served as Assistant Professor at the Faculty of Electrical Engineering at the Universidad Michoacana de San Nicolás de Hidalgo from 2011 to 2014. Since joining INFOTEC, he has led research initiatives, contributed to graduate program development, and participated in national and international evaluation committees. He also serves as an Academic Editor for PLOS One and has contributed to the organization of scientific events such as ROPEC (IEEE), roles that complement his sustained commitment to advancing research, graduate education, and technological innovation in Mexico.

His research interests include Machine Learning, Evolutionary Computation (EC), and Natural Language Processing (NLP), with a particular focus on applying Genetic Programming (GP) to supervised learning tasks such as text classification and sentiment analysis. He has published extensively in indexed journals, international conferences, and book chapters, and has contributed to software tools used in academic and industrial settings.

This site is organized into five sections. Research & Software groups the work into three lines: legal text analytics (LegalIA), statistical comparison of algorithms (CompStats), and text classification — covering bag-of-words tools (EvoMSA, B4MSA, μTC) as well as dialect identification (dialectid). Publications links to the complete record on Google Scholar and highlights the products marked as featured in the SECIHTI research profile. Alumni lists the Ph.D. and Master’s students graduated from the program. Course Textbooks points to the open textbooks on Machine Learning and NLP (in Spanish). Talks collects the presentations on outreach and text classification.