JOYDEEP GHOSH, Schlumberger Centennial Chair Professor
Director, UT-MINDS (Machine INtelligence and Decision Systems)
- Professor, Dept. of Electrical & Computer Engineering, Univ of Texas, Austin
- Member, GSC, Dept. of Computer Sciences, UT and Dept. of Biomedical Engineering, UT
- Courtesy Faculty: Dept of Population Health, Dell Medical School
- Courtesy Faculty: Dept of IROM at McCombs School of Business
- Member, WNCG (Wireless Networking & Comm. Group)
Director, IDEAL (Intelligent Data Exploration and Analysis Lab)
(links to selected current/past/future course offerings)
Contact
- Office: EER 6.812; phone: (512) 471-8980; FAX: 471-2893. Office Hours: (Spring 2024) Tu, Th: 2:00-3:00pm Location: 6th Floor, South Tower of EER Building (NE corner of Speedway and 24th). Map showing convenient walking path from San Jacinto into East side of building.
- E-mail: jghosh -at- utexas (dot) edu
- Mailing Address: 2501 Speedway, EER 6.812, University of Texas, Austin, TX 78712
Bio
Joydeep Ghosh is currently the Schlumberger Centennial Chair Professor of Electrical and Computer Engineering at the University of Texas, Austin.
Dr. Ghosh (Fellow, IEEE 2004) 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)
Dr. Ghosh has taught graduate courses on data mining and web analytics to both UT students and to industry, for over three decades. He has been voted as "Best Professor" multiple times
by students in the Software Engineering Executive Education Program at UT, and in the Masters of Science in Business Analytics (MSBA) program within the UT McCombs School of Business.
The most recent of these recognitions was given
by the MSBA Class of 2022. He has also received several top awards for lifetime research contributions, including the
IEEE CS Technical Achievement Award (2015) and ICDM Research Contributions Award (2020, Citation ) for lifetime research contributions to Data Mining and Machine Learning.
Dr. Ghosh's research interests lie primarily in data mining and web mining, ethical/trustworthy/responsible AI, scalable machine learning algorithms, specially for
predictive and prescriptive analytics,
and applications to a wide variety of complex real-world problems, including health informatics. He has published more than 500 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. He has received 17 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 at several venues including
KDD, ICML,ICDM, SDMand ICHI, and has widely lectured on intelligent analysis of large-scale data.
Dr. Ghosh has served as a co-founder, consultant or advisor to successful startups (including Ojolabs, Accordion Health, CognitiveScale and Neonyoyo) and as a consultant to large corporations such as IBM, TBS and Vinson & Elkins.
As Chief Scientist of CognitiveScale, he guided their Machine Learning/Algorithmic Sciences team in successfully completing several large AI-driven projects for multiple Fortune 500 customers over a 5+ year span.
CognitiveScale was selected in 2018 as one of 61 technology pioneers worldwide by the World Economic Forum,
among other
recognitions, before being acquired by a publicly listed international company.
Professional service activities include serving as the Conference Co-Chair or Program Co-Chair for several top machine learning and data mining oriented conferences, including KDD, SDM, CIDM, ANNIE and ICPR.
He was the founding chair of the Data Mining Tech. Committee of the IEEE Computational Intelligence Society. He has also co-organized workshops on health informatics, high dimensional clustering, Web Analytics, Web Mining and Parallel/ Distributed Knowledge Discovery, among other topics.