Dr. Juan Ignacio Gonzalez-Espinosa is a Clinical Assistant Professor in the Quantitative Methods Area at the Mitch Daniels School of Business at Purdue University. He is a seasoned university professor with over 15 years of teaching in higher education institutions. His expertise spans data science, artificial intelligence, predictive analytics, and business strategy. He holds a Ph.D. in Management Science from EGADE Business School, Monterrey (ITESM), with specialized studies in quantitative methods at Ohio State University. He has completed postgraduate programs in Data Science, Business Analytics, Artificial Intelligence, and Machine Learning from UT Austin, Data Science and Machine Learning for Decision-Making (MIT-IDSS), and Generative AI for Natural Language Processing.

Before joining Purdue, Dr. Gonzalez-Espinosa was a full-time faculty member at Saint Louis University, where he developed and taught graduate courses in Predictive Analytics, Machine Learning, Deep Learning, Data Visualization, and Generative AI, while guiding students through advanced AI projects. He also served at the Universidad de Monterrey (UDEM) as a full-time professor, Director of Intelligence, and Chief Data Scientist, where he led the development and deployment of machine learning models for student retention and success, spearheaded data governance initiatives, and fostered a university-wide data-driven culture.

With over 15 years of industry experience, Dr. Gonzalez-Espinosa has held senior roles in marketing intelligence, business strategy, and product development at companies such as Axalta-Dupont, Sigma Alimentos, Whirlpool, and Coca-Cola Bottling Group of Mexico. His portfolio includes leading data-driven projects in predictive modeling, customer analytics, and market strategy for top-tier organizations like HEB, DeAcero, and Metalsa in Mexico.

He is passionate about integrating real-world challenges into the classroom through experiential learning and consulting projects. His research interests include generative AI applications for decision-making, machine and deep learning, competitive intelligence systems, and advanced data visualization.

Areas of Expertise

Decision-Making based on Predictive Analytics, Machine and Deep Learning.  Generative AI and Natural Language Processing. Business Intelligence and Data Visualization. Quantitative Methods and Statistical Modeling. Experiential Learning and Applied Analytics in Business. Forecasting and Time Series Analysis