The Fourth IASTED International Conference on
Signal Processing, Pattern Recognition and Applications
SPPRA 2007
February 14 – 16, 2007
Innsbruck, Austria
TUTORIAL SESSION
Pattern-based Classification and Decision Making
Abstract
Objectives
Tutorial Materials
Target Audience
Background Knowledge Expected of the Participants
Qualifications of the Instructor(s)
Parag Kulkarni is Chief Scientist and Director-Research and Development at Capsilon Research Labs, Pune, India. He is an alumnus of IIT and IIM. He completed his Ph.D. in Computer Engineering from IIT Kharagpur. He has been working in the IT industry for the last 16 years. He has worked as operations head, GM, and was involved in bringing two startups up to speed. Under his leadership and guidance, one Singapore startup has grown from 5 to 30 employees and launched its product to market successfully. His research and ideas in the areas of image processing and clustering resulted in products that later became commercially successful.
He has more than 35 International publications and two patents pending in US PTO. He has written many research articles and his one book on decision science is in press. He is a member of an IASTED technical committee, WSEAS working committee, board of studies of two institutes, and is supervising 8 Ph.D. students. He has conducted more than 15 tutorials at various international conferences and was a keynote speaker for three international conferences. He is also invited as visiting faculty to conduct special sessions at IIMs, IITs, Symbiosis, FTMS, NICM Pune, and Pune University. He is honored with the title of honorary professor by two prime institutes in Pune. He is involved in active research-work in the areas of mind maps, knowledge management, and forecasting.
He has more than a decade of experience in product development in the area of decision systems and forecasting. His areas of research and product development include M-maps, text mining, image processing, Decision systems, forecasting, knowledge management, IT strategy, classification, distributed computing, AI, and machine learning.
References
| [1] |
