Speakers Info

Title: Granular Data Mining and Uncertainty Modeling: Concepts, Applications and Challenges


The talk has two parts. First it describes the –

  • Components of granular computing and features of granulation
  • Significance of fuzzy sets and rough sets in granular computing
  • Relevance of defining the generalized rough sets and entropy by embedding fuzziness into rough sets; providing a stronger paradigm for uncertainty modeling

The second part deals with various mining applications such as in –

  • Video tracking in ambiguous situations
  • Bioinformatics (e.g., selection of miRNAs for cancer detection)
  • Social network analysis (e.g., community detection)

The applications demonstrate the roles of different kinds of granules, rough lower approximation, and various information measures. Granules considered range from crisp, fuzzy, 1-d, 2-d and 3-d to regular shape and arbitrary shape. While the concept of rough lower approximation in temporal domain provides an initial estimate of object model in video tracking, it enables in determining the probability of definite and doubtful regions in cancer classification. Several examples and results would be provided to explain the aforesaid concepts. The talk concludes mentioning the challenging issues and the future directions of research including the significance of z-numbers and Big data analysis.

Brief Biography:

Sankar K. Pal (www.isical.ac.in/~sankar) is a Distinguished Scientist and former Director of Indian Statistical Institute, and a former Chair Professor of Indian National Academy of Engineering. He is currently a DAE Raja Ramanna Fellow and J.C. Bose National Fellow. He founded the Machine Intelligence Unit and the Center for Soft Computing Research: A National Facility in the Institute in Calcutta. He received a Ph.D. in Radio Physics and Electronics from the University of Calcutta in 1979, and another Ph.D. in Electrical Engineering along with DIC from Imperial College, University of London in 1982.

He worked at the University of California, Berkeley and the University of Maryland, College Park in 1986-87; the NASA Johnson Space Center, Houston, Texas in 1990-92 & 1994; and in US Naval Research Laboratory, Washington DC in 2004. Since 1997 he has been serving as a Distinguished Visitor of IEEE Computer Society (USA) for the Asia-Pacific Region, and held several visiting positions in Italy, Poland, Hong Kong and Australian universities.

Prof. Pal is a Life Fellow of the IEEE, and Fellow of the World Academy of Sciences (TWAS), International Association for Pattern recognition, International Association of Fuzzy Systems, International Rough Set Society, and all the four National Academies for Science/Engineering in India. He is a coauthor of twenty books and more than four hundred research publications in the areas of Pattern Recognition and Machine Learning, Image Processing, Data Mining and Web Intelligence, Soft Computing, Neural Nets, Genetic Algorithms, Fuzzy Sets, Rough Sets, Cognitive Machine and Bioinformatics. He initiated and promoted the soft computing research & teaching in India. He visited forty plus countries as a Keynote/ Invited speaker or an academic visitor.

He received the 1990 S.S. Bhatnagar Prize (which is the most coveted award for a scientist in India), 2013 Padma Shri (one of the highest civilian awards) by the President of India, and many prestigious awards in India and abroad including the 2000 Khwarizmi International Award from the President of Iran, 2000-2001, 1993 NASA Tech Brief Award (USA), 1994 IEEE Trans. Neural Networks Outstanding Paper Award, 1995 NASA Patent Application Award (USA), 1999 G.D. Birla Award, 1998 Om Bhasin Award, 2005-06 Indian Science Congress-P.C. Mahalanobis Birth Centenary Gold Medal from the Prime Minister of India for Lifetime Achievement, 2015 INAE-S.N. Mitra Award, and 2017 INSA-Jawaharlal Nehru Birth Centenary Lecture award.

Prof. Pal is/ was an Associate Editor of IEEE Trans. Pattern Analysis and Machine Intelligence (2002-06), IEEE Trans. Neural Networks [1994-98 & 2003-06], Neurocomputing (1995-2005), Pattern Recognition Letters (1993-2011), Int. J. Pattern Recognition & Artificial Intelligence, Applied Intelligence, Information Sciences, Fuzzy Sets and Systems, Fundamenta Informaticae, LNCS Trans. Rough Sets, Int. J. Computational Intelligence and Applications, IET Image Processing, Ingeniería y Ciencia, and J. Intelligent Information Systems; Editor-in-Chief, Int. J. Signal Processing, Image Processing and Pattern Recognition; a Book Series Editor, Frontiers in Artificial Intelligence and Applications, IOS Press, and Statistical Science and Interdisciplinary Research, World Scientific; a Member, Executive Advisory Editorial Board, IEEE Trans. Fuzzy Systems, Int. Journal on Image and Graphics, and Int. Journal of Approximate Reasoning; and a Guest Editor of IEEE Computer, IEEE SMC and Theoretical Computer Science.

Title: Demystifying Deep Learning


Deep Learning has shown tremendous potential and has been successfully applied across different domains. In this talk basics of Deep learning, current trends, need and the challenges that Deep learning community is facing will be discussed. Also, insights to some of the applications of Deep learning with respect to healthcare will be provided.

Brief Biography:

Ravindra Patil works as Senior Scientist at Philips Research Lab, Bangalore. Prior to that he was with Siemens Bangalore. He secured his B.E with University rank from Visvesvaraya Technology University, Karnataka and post which he completed his Masters from IIT Madras in Image processing and pattern recognition. He has 13 patent filings and 20 publications in various journals and international conferences. He specializes in areas of artificial intelligence, image processing and machine learning and has close to 10 years of corporate research lab experience.

Title: A Frequent Pattern Mining Approach to Analysis of Symbolic Time Series data


Over the last couple of decades, frequent pattern mining has emerged as a very useful approach for many data analysis problems. In this talk we begin with a short introduction to the general frequent pattern mining idea. We then discuss the temporal data mining problem of analyzing time series data where the data may not be real-valued. Frequent pattern discovery is useful for characterizing temporal dependencies in such data. We introduce one specific method in temporal data mining, namely, frequent episode discovery. Here the data is viewd abstractly as a sequence of events with each event characterized by a symbolic event-type and a time of occurrence. The episode patterns are meant to capture temporal dependencies in the data. We will discuss many application examples where this is a useful framework. We then discuss some algorithms for frequent episode mining, some statistical analysis of such methods and some example applications. The lecture would provide a general overview of this framework in temporal data mining and would also discuss some of the research results from our laboratory.

Brief Biography:

P.S. Sastry received his B.Sc in Physics from Indian Institute of Technology, Kharagpur, and B.E. in Electrical Communication Engineering and Ph.D. from the Department of Electrical Engineering, both from IISc, Bangalore. He has been a faculty member in the department of Electrical Engineering, IISc, since 1986. He was the chairman of the department during 2010-2015. He has held visiting positions at University of Massachusetts, Amherst; University of Michigan, Ann Arbor; and General Motors Research Laboratories, Warren. His research interests include Pattern Recognition, Machine Learning, Data Mining and Computational Neuroscience. Prof. Sastry received C.V.Raman Award for Young Scientists from Government of Karnataka, Hari Om Ashram Dr. Vikram Sarabhai Research Award from PRL, Ahmadabad, Most Valued Colleague Award from General Motors Corporation, USA, and the Alumni Award for Excellence in Research from IISc. He is a Fellow of the Indian National Academy of Engineering and the National Academy of Sciences, India.

Title: Automated Driving – How it works?


The continuing evolution of automotive technology aims to deliver even greater safety benefits and Automated Driving Systems that one day can handle the whole task of driving when we don’t want to or can’t do it ourselves. This talk mentions the trends in automotive domain, and go into details on automated driving (AD). The talk explains different levels of automation and demystifies the technology behind automated driving. Specifically, it focusses on the role of sensors, its setup, data, computer vision and machine learning algorithms for processing the data from those sensors.

Brief Biography:

Education: Ph.D. from Computer Vision Center, Autonomous University of Barcelona, Spain M.Sc. and M.Sc.Tech. (by research) at University of Mysore.

Experience: Over above 12 years of experience in research and development and over 3 years in teaching as. He has several publications to his credit in international journals and conferences.

Currently working as Technical Architect at Continental Automotive Components (India) Pvt. Ltd. He is part of the Project House Automated Driving and works on Environment Perception around vehicle for automated driving.

Previously worked at: Samsung R&D center, Warsaw, Poland HCL Technologies, Bangalore

Title: Machine Learning is impacting every aspect of the O&G business.


In this talk, we present two such use-cases, as diverse as the reach of Machine Learning. On one hand, we discuss how Machine Learning models can be used to improve the market trader's decisions and has a potential of several million dollars benefit and on the other hand, we propose Machine Learning as a new radial approach to the classical problem of identifying main structures in the subsurface, which is the key during the hydrocarbon exploration phase.

Brief Biography:

Pandu Devarakota is a Machine Learning professional with vast experience in proposing and developing many ML algorithms aiming to solve data analytics problems in various industrial domains. Currently, Pandu is a Team Lead for Machine Learning group in Computational Technologies Centre of Excellence in Shell. Previously, he worked as Research Scientist in Siemens and Technical Leader for Canon Inc. He completed his PhD in Signal Processing from Royal Institute of Technology (KTH), Stockholm in 2007 and Masters at Indian Institute of Technology (IIT), New Delhi in 2003. He was an exchange student at the Laboratory of Communication Engineering, Karlsruhe Institute of Technology, Germany in 2012.

Title: Big Data Analytics Solutions as Learning Systems (for day today living)


I am intending to touch upon the following aspects in the course of the talk:

  • Emergence of Big Data Analytics Use Cases
  • Big Data Analytics System Design Considerations
  • Significance of Unique IDs and Deduplication for Big Data Analytics Solutions
  • Outcomes of Big Data Analytics Solutions – Expected and Not So Expected
  • Big Data Analytics Systems and Solutions: Enable Learning ???

Brief Biography:

  • Over 25 years of experience in Software Engineering, Technology Consultancy and Realization of End-to-End Solutions
  • Technologies Covered – Big Data, Machine Learning, Enterprise Technologies, SOA, Web Technologies, Open Source Software
  • Domains Covered – Telecommunications, Automotive, Banking Financial Service & Insurance (BFSI), Information Security
  • Published Articles in IEEE Transactions, IETE Journal, National & International Conferences, Organizational Conferences