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Keynote Speakers


Dr. Parag Kulkarni, PhD, DSc,  CEO and Chief Scientist, EKLaT, Pune, India

Title of Talk: New Paradigm of Machine Learning for Big-data Mining

Biography: Dr. Parag is machine learning researcher, innovation strategist and keynote speaker. Through his research in ML, strategic consultations and innovative leadership Parag turned around fortune of more than one dozen startups in last two decades. Parag is Founder and CEO of the EKLaT. He holds PhD from IIT Khargpur and management education from IIM Kolkata. UGSM Monarch Business School, Switzerland conferred DSc – higher doctorate on him for his work on strategic knowledge innovation. He is inventor of over half a dozen patents, published over 250 research papers and written more than one dozen books. Parag is the author of “Knowledge Innovation Strategy – Why Cats don’t Take Part in Rat Race” - Bloomsbury, “Reinforcement and Systemic Machine Learning for Decision Making” – IEEE/Wiley and is co-author of “E-Business”- OUP, “AI – Building Intelligent Systems”- PHI, “IT Strategy”- OUP, Mining Unstructured Data – Big Data Perspective and “Deliverance from Success”. Parag delivered more than 100 keynote addresses and numerous talks on strategy and managing start-ups and delivered extraordinary impact on strategic perspective of thousands of professionals. Parag is consultant to over two-dozen organizations and has played key part in building more than one dozen commercially successful innovative products. He is visiting professor and Adjunct professor at many institutes of repute like IITs, IIMs. COEP, Masaryk University, etc. He is associated as consultant and mentor with more than one dozen organizations.

Talk description: Our traditional machine learning methods are simply not capable enough to deal with big data and some of the complexities in real life decision-making. The simple event and pattern-based learning can handle routine scenarios but fail in case of dynamic scenario. Though Reinforcement Learning and some of its variants deal with dynamic scenarios like gaming, there is need of holistic learning methods to cope up with these challenges. What are the new methods those can cope up with this challenge? This talk will introduce new methods and paradigms of systemic machine learning. The talk will throw light on incremental, co-operative and multi-perspective learning while elaborating concept of systemic machine learning.

Prof. Hideyuki TAKAGI, Kyushu University, Japan

Hideyuki Takagi received the degrees of Bachelor and Master from Kyushu Institute of Design in 1979 and 1981, and the degree of Doctor of Engineering from Toyohashi University of Technology in 1991.He was a researcher at Panasonic Central Research labs in 1981 - 1995 and a visiting researcher at UC Berkeley in 1991-1993 hosted by Prof. L. A. Zadeh. He moved to Kyushu Institute of Design as an Associate Professor in 1995 and is now a Professor of Kyushu University.
 
He had worked on neuro-fuzzy systems in 1987 - early 1990's and extended his interests to fusing neuro-fuzzy-genetic algorithms and human factors. Now, he aims humanized computational intelligence and is focusing on interactive evolutionary computation (IEC) as a tool for this research direction. Well cited his IEC papers and others can be found at Google Scholar Citations.He has been a volunteer for IEEE Systems, Man, and Cybernetics (SMC) Society. Some of his contributions are: Vice President in 2006 - 2009: a member of Administrative Committee/Board of Governors in 2001 - 2010: Chair of SMC Japan Chapter: Technical Committee (TC) Coordinator in 2004-2005: Chair of TC on Soft Computing in 1998-2004 and since 2008: SMC Distinguished Lecturer in 2006 - 2011: Associate Editor of IEEE Transactions on SMC, Part B and now Cybernetics since 2001. He is/was an organizer of the special sessions at SMC2006 -SMC2014. Other his voluntary jobs are an Executive Committee member of Japan Society for Evolutionary Computation in 2010-2012, that of Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT) in 1999-2001 and 2001-2003, and others. See his detail at http://www.design.kyushu-u.ac.jp/~takagi/

Dr. Ajith Abraham,  MIR Labs, USA

Title of Talk: Cyber Physical Systems: Challenges from a Data Analysis Perspective

Biography: Ajith Abraham received the Ph.D. degree in Computer Science from Monash University, Melbourne, Australia. He is currently the Director of Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, USA, which has members from more than 100 countries. He has a worldwide academic and industrial experience of over 24 years. He works in a multi disciplinary environment involving machine intelligence, network security, various aspects of networks, e-commerce, Web intelligence, Web services, computational grids, data mining, and their applications to various real-world problems. He is an author/co-author of more than 1,000 publications, with an h-index of 65+ and has given more than 70 plenary lectures and conference tutorials in these areas. Since 2008, he is the Chair of IEEE Systems Man and Cybernetics Society Technical Committee on Soft Computing and a Distinguished Lecturer of IEEE Computer Society representing Europe (2011-2013). He is the founder of several IEEE sponsored conferences, which are now annual events. More information at: http://www.softcomputing.net/.

Talk description: We are blessed with the sophisticated technological artifacts that are enriching our daily lives and the society. It is believed that the future Internet is going to provide us the framework to integrate, control or operate virtually any device, appliance, monitoring systems, infrastructures etc. The challenge is to design intelligent machines and networks that could communicate and adapt according to the environment. In this talk, the concept of digital ecosystem will be presented and then various research challenges from several application perspective will be illustrated. Some real world applications involving the analysis of complex data / aplications would be illustrated..





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