Joydeep Ghosh is a faculty associate for The University of Texas at Austin’s McCombs School of Business. Within the Department of Information, Risk, and Operations Management, Ghosh teaches courses on machine learning, data science principles, and advanced data mining.

Within UT’s Chandra Family Department of Electrical and Computer Engineering, he holds the Schlumberger Centennial Chair in Electrical Engineering. He is also a courtesy professor with UT’s Department of Population Health.

Ghosh is a well-respected researcher for his contributions to the field of data mining and web analytics. His expertise in data mining algorithms and cluster ensembles, and his investigation into random forest frameworks for classifying hyperspectral data, and error correlation and error reduction in ensemble classifiers have been sought and supported by the National Science Foundation, Google, IBM, the Office of Naval Research, and the U.S. Air Force Office of Scientific Research. He has also refereed or co-authored more than 70 educational books and has received over a dozen best-paper awards during his academic career.

Along with his academic work, Ghosh has had a successful business career and has served as co-founder, consultant, or adviser to organizations such as IBM. He is the founder of IDEAL, the Intelligent Data Exploration and Analysis Lab at The University of Texas at Austin Cockrell School of Engineering, which uses statistical machine learning, data mining and AI techniques to analyze and mine complex data—including data forms such as signal streams, images, and videos—and understand any underlying phenomena and make actionable theories and insights.

Ghosh earned a Bachelor of Technology, Electrical and Electronics Engineering from the Indian Institute of Technology Kanpur. He earned a Ph.D. in computer engineering from the University of Southern California.