Joydeep Ghosh is currently the Schlumberger Centennial Chair Professor of Electrical and Computer Engineering at the University of Texas, Austin. He joined the UT-Austin faculty in 1988 after being educated at, (B. Tech ’83) and The University of Southern California (Ph.D’88). He is the founder-director of IDEAL (Intelligent Data Exploration and Analysis Lab) and a Fellow of the IEEE. His research interests lie primarily in data mining and web mining, predictive modeling / predictive analytics, machine learning approaches such as adaptive multi-learner systems, and their applications to a wide variety of complex real-world problems such as healthcare.
He has published more than 400 refereed papers and 50 book chapters, and co-edited over 20 books. His research has been supported by the NSF, Yahoo!, Google, ONR, ARO, AFOSR, Intel, IBM, and several others. He has received 14 Best Paper Awards over the years, including the 2005 Best Research Paper Award across UT and the 1992 Darlington Award given by the IEEE Circuits and Systems Society for the overall Best Paper in the areas of CAS/CAD. Dr. Ghosh has been a plenary/keynote speaker on several occasions such as ICDM’13, (Health Informatics workshops at) KDD14, ICML13 and ICHI13; MICAI’12, KDIR’10 and ISIT’08, and has widely lectured on intelligent analysis of large-scale data. He served as the Conference Co-Chair or Program Co-Chair for several top data mining oriented conferences, including SDM’13, SDM’12, KDD 2011, CIDM’07, ICPR’08 (Pattern Recognition Track) and SDM’06. He was the Conf. Co-Chair for Artificial Neural Networks in Engineering (ANNIE)’93 to ’96 and ’99 to ’03. He has also co-organized workshops on health informatics, high dimensional clustering, Web Analytics, Web Mining and Parallel/ Distributed Knowledge Discovery.
Dr. Ghosh has served as a co-founder, consultant or advisor to successful startups in addition to consulting for large corporations.
2015 Technical Achievement Award
“For foundational contributions to multi-learner systems.”
Learn more about the Technical Achievement Award