Keynote Speakers

Speaker: Prof. Erol Gelenbe, Imperial College and Polish Academy of Sciences (Inventor of G-Networks and the Random Neural Network)

Title of Talk: Deep Learning with Spiking Random Neural Networks

Biography: Erol Gelenbe is a Fellow of IEEE, ACM and IET (UK), and a Professor in the Department of Electrical and Electronic Engineering at Imperial College, London. He has introduced computer and network performance models based on diffusion approximations, and invented the Random Neural Network Model, as well as G-Networks which are analytically solvable queueing models that incorporate control functions such as work removal and load balancing. His other contributions include the concept and prototype for FLEXSIM, an object oriented discrete event simulation approach for flexible manufacturing systems, and other commercially successful projects such as the QNAP tool for the Performance Evaluation of Computer Systems and Networks. His innovative designs include the first voice-packet switch SYCOMORE, the first fibre optics random access network XANTHOS, and the first implemented Cognitive Packet Network and its adaptive routing protocol. He also designed and published the first optimal protocol for random access communications, and an optimum check-pointing scheme for databases. For his work, he received several prizes from France, the UK, Hungary and Turkey, including the 2010 IET Oliver Lodge Medal, the 2008 ACM SIGMETRICS Life-Time Achievement Award, and the 1996 Grand Prix France Telecom of the French Academy of Sciences. He was awarded Knight of the Legion of Honour and Officer of the Order of Merit of France, and Grand Officer of the Order of the Star and Commander of Merit of Italy. He is a Fellow of the French National Academy of Engineering, the Royal Academy of Sciences, Arts and Letters of Belgium, the Science Academies of Hungary and Poland, and the Science Academy of Turkey. He was awarded Honoris Causa doctorates from the Universities of Liege (Belgium), Roma II (Italy) and Bogazici (Turkey). He has graduated over 73 PhD students, and his recent papers appear in the Physical Review, the Communications of the ACM, and several IEEE and ACM Transactions.

Abstract: Networks in mammalian brains are mainly of a spiking nature so that the manner in which such networks learn are of great philosophical, scientific and engineering interest. Thus several years ago, we developed the first O(n^3) gradient descent learning algorithm for recurrent networks using the spiking and random behaviour of biological neuronal cells. In this presentation we will details how these dense structures can be exploited in deep learning and how they can achieve significantly better performance than standard models. The presentation will be illustrated with numerous practical examples.

Speaker: Prof. Sushmita Mitra, Machine Intelligence Unit of the Indian Statistical Institute, Kolkata, India

Title of Talk: tba

Biography:Sushmita Mitra is a full Professor at the Machine Intelligence Unit (MIU), Indian Statistical Institute, Kolkata. She was the Head of MIU during the period 2010 to 2012. From 1992 to 1994 she was in the RWTH, Aachen, Germany as a DAAD Fellow. She was a Visiting Professor in the Computer Science Departments of the University of Alberta, Edmonton, Canada in 2004, 2007; Meiji University, Japan in 1999, 2004, 2005, 2007; and Aalborg University Esbjerg, Denmark in 2002, 2003. Dr. Mitra received the National Talent Search Scholarship (1978-1983) from NCERT, India, the University Gold Medal in 1988, the IEEE TNN Outstanding Paper Award in 1994 for her pioneering work in neuro-fuzzy computing, and the CIMPA-INRIA-UNESCO Fellowship in 1996.

She is the author of the books "Neuro-Fuzzy Pattern Recognition: Methods in Soft Computing" and "Data Mining: Multimedia, Soft Computing, and Bioinformatics" published by John Wiley, and "Introduction to Machine Learning and Bioinformatics", Chapman & Hall/CRC Press, beside a host of other edited books. Dr. Mitra has guest edited special issues of several journals, is an Associate Editor of "IEEE/ACM Trans. on Computational Biology and Bioinformatics", "Information Sciences", "Neurocomputing", and is a Founding Associate Editor of ``Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery (WIRE DMKD)". She has more than 75 research publications in referred international journals. According to the Science Citation Index (SCI), two of her papers have been ranked 3rd and 15th in the list of Top-cited papers in Engineering Science from India during 1992--2001. Dr. Mitra is a Fellow of the IEEE, and Fellow of the Indian National Academy of Engineering and The National Academy of Sciences, India. She is an IEEE CIS Distinguished Lecturer for the period 2014-2016. She has served in the capacity of Program Chair, Tutorial Chair, Plenary Speaker, and as member of programme committees of many international conferences. Her current research interests include data mining, pattern recognition, soft computing, image processing, and Bioinformatics.

Speaker: Dr. Alex Pappachen James, Nazarbayev University, Kazakhstan

Title of Talk: tba

Biography: Alex Pappachen James received his Ph.D. from Griffith School of Engineering, Griffith University completing in short 2 year duration in the area of bio-inspired cognitive face recognition system. He is currently a professorial faculty with School of Engineering, Nazarbayev University and CEO/founder of the start-up Enview R&D Labs. He has an extensive industry and academic experience working on research projects in the area of board design, embedded systems, and integrated circuits for neuron inspired systems. His research deals with bio-inspired circuit, algorithms and systems in particular to applications involving low level vision processing, sensor data fusion and decisions in the domains of use in biomedical, nanosystems and sustainable engineering.".Dr James is a senior member of IEEE and is actively involved in the development of leading societies in the area of brain inspired systems. He is active as a reviewer, external examiner to PhD thesis, conference committees, authoring in journals and mentor to graduate students. He is widely travelled and enjoys research collaborations with like-minded researchers from across the world. More info:

Speaker: Dr. Asharaf S, IIITM-Kerala, India

Title of Talk: tba

Biography: Asharaf S is an Associate Professor at Indian Institute of Information Technology and Management Ė Kerala. He is also serving as a visiting faculty in Indian Institute of Space Science and Technology, Trivandrum and as a Mentor in Kerala Startup Mission. He received his PhD and Master of Engineering degrees in Computer Science from Indian Institute of Science, Bangalore. He graduated in Computer Engineering from Cochin University of Science and Technology. After his PhD he has worked with America Online (AOL) and IIM Kozhikode. He is a recipient of IBM outstanding PhD student award 2006 and IBM Shared University Research Grant, 2015. He has published two books and more than 25 research papers in international journals and conferences. His areas of interest include technologies and business models related to machine learning, information retrieval, web mining and intelligent internet of things. Web Link :

Speaker: Dr. Satheesh Kumar, Department of Futures Studies, University of Kerala

Title of Talk: Social network analysis: applications and computational tools

Biography: Satheesh Kumar is an Assistant Professor in the Department of Futures Studies at the University of Kerala. Before joining the University of Kerala, he was a Lecturer in St. Johnís College, Postdoctoral Research Fellow at Monash University, Australia and Postdoctoral Scientist in POSTECH, South Korea. He completed his Ph. D. at NIIST (CSIR), Trivandrum. His research interests lie in the area of computational modelling and simulation, Chaotic Dynamics, Social Network Analysis and Wind modelling and Forecasting, Machine Learning and Data Analysis.

Abstract: Social network analysis (SNA) refers to the investigation of social relations using graph theory. It has become a powerful tool over the years for analysing social structure alongside the quantitative methods of statistics. The literature in this area has grown extensively over the years, especially in the last decade owing to developments in the field of graph theory and computing. Applications of SNA varies from economics to industrial engineering and protein structures to citation networks. In the first part of the session, we discuss various applications and basic concepts such as centrality measures, clusters and small world phenomenon. In the second part, we focus on practical aspects of SNA by demonstrating the usage of popular computational tools for the analysis of real world networks. Finally, we show how citation network analysis can be carried out as a systematic method of literature review.

Speaker: Dr. Deepak Mishra, Indian Institute of Space Science and Technology(IIST), Trivandrum

Title of Talk: Tutorial on Deep Learning and its Applications

Biography: Having done his B.E., in Electrical Engg. (2000) BIT Durg, and M Tech in Instrumentation (2003) from Devi Ahilya University Indore, Dr. Mishra pursued his PhD at IIT Kanpur (2007) in the Electrical Engg. Department. His Thesis title was "Novel Biologically Inspired Neural Network Models". Later He joined as a postdoc researcher at the University of Louisville, KY, USA in the field of signal processing and system neuroscience. After a brief stint 2009-2010 as a senior software engineer at CMC limited Hyderabad. He opted to work as an academic faculty at Indian Institute of Space Science and Technology Trivandrum in 2010 and continued to work as Associate Professor in the department of Avionics. He is responsible for both research and teaching UG and PG students, moreover he was the coordinator for MTech program in Digital Signal Processing and developed a Virtual Reality center of excellence during his stay at IIST. He was also awarded Young Scientist award from System Society of India for his research work in the year 2012. His research interest includes Neural networks and Machine learning, Computer vision and Graphics, Image and Video Processing. He has published research papers both in International and National Journal of repute and presented his research work in various international and national conferences.

Abstract: Deep learning methods are now prevalent in the area of machine learning, and are now used invariably in many research areas. In recent years it received significant media attention as well. The influx of research articles in this area demonstrates that these methods are remarkably successful at a diverse range of tasks. Namely self driving cars, new kinds of video games, AI, Automation, object detection and recognition, surveillance tracking etc.

The proposed tutorial aims at introducing the foundations of Deep learning to various professionals.

BROAD TOPICS THAT WILL BE discussed in the tutorial are:
* Basics of Neural Networks and Convolution Neural Network applications
* Discussion Alexnet and VGG architectures for recognition