Dr. Ghosh currently holds the Schlumberger Centennial Chair Professorship at the University of Texas, Austin. He is recognized worldwide for his seminal contributions to a wide variety of sub-areas in data mining and machine learning, including clustering, theory and practice of ensembles, scalable recommender systems, web mining, video summarization, federated learning and computational phenotyping. These contributions are recorded in over 500 peer-reviewed publications (30K+ citations, h-index of 68). He has received 17 Best Paper Awards so far in his career, ranging from the 1992 Darlington Award (best paper among all conferences and journals published by IEEE CAS), to the 2017 Distinguished Clinical Informatics Research Paper Award from the American Medical Informatics Association (AMIA). Other honors include IEEE Fellow (2004) and the highly selective IEEE Computer Society Technical Achievement Award (2015), given for best research/impact across all computing topics in the last 10 to 15 years.
Dr. Ghosh has served as program or conference co-chair for the top data mining conferences, and also keynoted at such venues, including at ICDM 2013 (Dallas), where he made it despite much of the city shutting down because of an unprecedented ice-storm! At the University of Texas, he has received several "Best Professor" awards from students (most recently in 2019), and graduated 43 PhD students, with many of them securing tenured or tenure-track positions in academia (including the University of Illinois at Urbana-Champaign, University of Minnesota, Emory University, University of Southern California, and University of Alberta) or top positions in industry. He also has extensive direct collaborations with industry, ranging from the usual tech titans to data mining startups. In particular, for the past three years, he has been serving as Chief Scientist of CognitiveScale, which was selected in 2018 to be among the 61 technology pioneers worldwide (across all industries) by the World Economic Forum, for its efforts in developing trustworthy machine learning solutions.
2020 IEEE ICDM Nomination and Evaluation Committees