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Dr. Joydeep Ghosh

Dr. Joydeep Ghosh

Schlumberger Centennial Chair Professor of Electrical and Computer Engineering, University of Texas at Austin, USA

Dr Ghosh is currently the Schlumberger Centennial Chair Professor of Electrical and Computer Engineering at the University of Texas, Austin. He also serves as the Chief Scientist of Cognitive Scale, which was selected in 2018 to be among the 61 technology pioneers worldwide by the World Economic Forum, among other recognitions. Dr Ghosh joined the UT-Austin faculty in 1988 after being educated at IIT Kanpur (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), an IEEE Fellow (2004), and the 2015 recipient of IEEE CS Technical Achievement Award. 

Dr. Ghosh has taught graduate courses on data mining and web analytics to both UT students and to industry, for over two decades. His research interests lie primarily in data mining and web mining, responsible AI, scalable machine learning algorithms, especially for predictive and prescriptive analytics, and applications to a wide variety of complex real-world problems, including health informatics. He has published more than 450 refereed papers and 50 book chapters, and co-edited over 20 books. His research has been supported by the NSF, Yahoo!, Google, Paypal, ONR, ARO, AFOSR, Intel, IBM, etc.

Research Interests:
  • Adaptive Multi-Learner Systems
  • Intelligent Data Analysis
  • Data and Web Mining
  • Doctor of Philosophy (PhD) in Computer Science, University of Southern California, USA
  • Bachelors in Technology in Electrical Engineering, Indian Institute of Technology, Kanpur, India
  • X Wu, V Kumar, JR Quinlan, J Ghosh, Q Yang, H Motoda, GJ McLachlan, Top 10 algorithms in data mining, Knowledge and information systems 14 (1), 1-37.
  • A Strehl, J Ghosh, Cluster ensembles---a knowledge reuse framework for combining multiple partitions, Journal of machine learning research 3 (Dec), 583- 617.
  • A Banerjee, S Merugu, IS Dhillon, J Ghosh, Clustering with Bregman divergences, Journal of machine learning research 6 (Oct), 1705-1749
  • A Banerjee, S Merugu, I Dhillon, J Ghosh, Clustering with Bregman Divergences, Proceedings of the Fourth SIAM International Conference on Data Mining 117, 234.
  • A Strehl, J Ghosh, R Mooney, Impact of similarity measures on web-page clustering, Workshop on artificial intelligence for web search (AAAI 2000) 58, 64.
  • A Banerjee, IS Dhillon, J Ghosh, S Sra, Clustering on the Unit Hypersphere using von Mises-Fisher Distributions, Journal of Machine Learning Research 6 (9).
  • J Ham, Y Chen, MM Crawford, J Ghosh, Investigation of the random forest framework for classification of hyperspectral data, IEEE Transactions on Geoscience and Remote Sensing 43 (3), 492-501.
  • X Wu, V Kumar, The top ten algorithms in data mining, CRC press
  • K Tumer, J Ghosh, Error correlation and error reduction in ensemble classifiers, Connection science 8 (3-4), 385-404.
  • YJ Lee, J Ghosh, K Grauman, Discovering important people and objects for egocentric video summarization, 2012 IEEE conference on computer vision and pattern recognition, 1346-1353
  • A Banerjee, I Dhillon, J Ghosh, S Merugu, DS Modha, A generalized maximum entropy approach to bregman co-clustering and matrix approximation, Journal of Machine Learning Research 8 (Aug), 1919-1986
  • K Tumer, J Ghosh, Analysis of decision boundaries in linearly combined neural classifiers, Pattern recognition 29 (2), 341-348
  • S Zhong, J Ghosh, A unified framework for model-based clustering, Journal of machine learning research 4 (Nov), 1001-1037
  • S Kumar, J Ghosh, MM Crawford, Best-bases feature extraction algorithms for classification of hyperspectral data, IEEE Transactions on Geoscience and remote sensing 39 (7), 1368-1379
  • A Banerjee, J Ghosh, Clickstream clustering using weighted longest common subsequences, Proceedings of the web mining workshop at the 1st SIAM conference
  • Y Shin, J Ghosh, The pi-sigma network: An efficient higher-order neural network for pattern classification and function approximation, IJCNN-91-Seattle international joint conference on neural networks 1, 13-18
  • JT Draper, J Ghosh, A comprehensive analytical model for wormhole routing in multicomputer systems, Journal of Parallel and Distributed Computing 23 (2), 202-214
  • S Rajan, J Ghosh, MM Crawford, An active learning approach to hyperspectral data classification, EEE Transactions on Geoscience and Remote Sensing 46 (4), 1231-1242
  • S Merugu, J Ghosh, Privacy-preserving distributed clustering using generative models, Third IEEE International Conference on Data Mining, 211-218
  • S Zhong, J Ghosh, Generative model-based document clustering: a comparative study, Knowledge and Information Systems 8 (3), 374-384