Qualification: 
Ph.D, PGDM, BE
vivekmenon@am.amrita.edu

Dr. Vivek Menon currently serves as an Associate Professor of Computer Science & Engineering in the School of Engineering at Amritapuri Campus.

cisa-logo_0.jpgHe has over 17 years of teaching and research experience across our campuses at Amritapuri, Coimbatore and Kochi and joined AMRITA in 2001 at the Coimbatore campus. A Visiting Research Scientist at the Center for Unified Biometrics and Sensors (CUBS), Dept. of CSE, State University of New York (SUNY) at Buffalo from 2007-09, his ongoing collaborative research on smart environments with SUNY at Buffalo has featured in prestigious international journals and conference proceedings published by the IEEE, Springer-Verlag and Elsevier. He has presented research papers at international conferences and workshops held at St. Louis, Liverpool and Seoul and has delivered invited talks/lectures in various forums. A Cisco Certified Network Associate (CCNA) and Cisco Certified Academy Instructor (CCAI) during 2002-05; he is a Certified Information Systems Auditor (CISA) since 2013. He is a Senior Member of IEEE and IEEE Computer Society, a Senior Member of ACM, and serves as an Academic Advocate of ISACA for Amrita Vishwa Vidyapeetham. He is a Life Member of the Analytics Society of India.

He is a recipient of the prestigious 'Fast Track Young Scientist Scheme' in Engineering Sciences, awarded by the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India for the research project titled “Video Analytics based Identification and Tracking in Smart Spaces"

Qualifications : B. E. (CSE, ASE-Bharathiar), PGDM (Information Systems & Finance, ASB), Ph. D. (Computer Science, AMRITA), CISA

COURSES TAUGHT

  • Machine Learning [M.Tech/PhD]
  • Foundations of Data Science [M.Tech/PhD]
  • Data Science in R and Python [B.Tech]
  • Design Patterns [B.Tech]

POSITIONS

  • Visiting Research Scientist, Center for Unified Biometrics and Sensors (CUBS), State University of New York (SUNY) at Buffalo, USA.
  • Senior Member, IEEE & IEEE Computer Society
  • Senior Member, ACM
  • Academic Advocate, ISACA
  • Life Member, Analytics Society of India

INVITED ARTICLE

  1. ‘Smart Environments’, MANORAMA YEARBOOK 2012, pp. 269-272.

INVITED TALKS/LECTURES/POSTERS

  1. Presented a Poster (co-authored with Lakshmi Mohan) titled 'GPU Accelerated Video Analytics for Identification and Tracking' at the NVIDIA GPU Technology Conference (GTCx), Mumbai held on Dec 6, 2016.
  2. Invited Lecture titled ‘From Data Analysis to Data Analytics: The Road Ahead’ at the Vidyamritam Extramural Expert Lecture Series, Amrita School of Arts and Sciences, Kochi Campus, October 9, 2015.
  3. Invited Short Talk titled ‘Video Analytics driven Identification and Tracking in Smart Spaces’ at the IEEE/ACM sponsored International Summer School on Machine Learning Algorithms and Data Analytics, Thapar University, Patiala, India from June 3 - 13, 2014.
  4. Invited Short Talk titled ‘Unobtrusive Tracking of Residents in Elderly Care Homes’ at NetHealth 2013 workshop, Fifth International Conference on COMmunication Systems and NETworkS (COMSNETS), Bangalore, India from January 7 -10, 2013.
  5. Invited Technical Talk titled ‘Tracking people in Smart Environments’ at the 7th Indo-Australian Conference on IT Security (IACITS 2011), jointly organized by Society for Electronic Transaction and Security (SETS), IIT Madras, and Queensland University of Technology under the auspices of Indo-Australian IT Security Society at IITM Research Park from April 11-12, 2011
  6. Invited White-Paper titled ‘Identification, Tracking, and Querying in Cyber-Physical Spaces’ at the Indo-U.S. Workshop on Developing a Research Agenda in Pervasive Communications and Computing Collaboration (PC3), co-sponsored by the National Science Foundation (NSF), USA and Department of Information Technology (DIT), Govt. of India, held at IIT Delhi from March 9 – 11, 2011.

Publications

Publication Type: Conference Paper

Year of Publication Publication Type Title

2018

Conference Paper

Smibi M.J. and Dr. Vivek Menon, “Modeling Compensation of Data Science Professionals in BRIC Nations”, in International Conference on Emerging Technologies in Data Mining and Information Security (IEMIS 2018), Kolkata [To appear in Springer Advances in Intelligent Systems and Computing (AISC) Series], 2018.

2016

Conference Paper

C. Mohanan and Dr. Vivek Menon, “Disaster management in India - An analysis using COBIT 5 principles”, in GHTC 2016 - IEEE Global Humanitarian Technology Conference: Technology for the Benefit of Humanity, Conference Proceedings, 2016, pp. 209-212.[Abstract]


From a limited focus on post-disaster activities of response and recovery, disaster management frameworks have evolved over time to include pre-disaster activities of prevention and preparedness. Disaster risk governance, disaster risk reduction and resilience are core aspects of contemporary disaster management frameworks. India is one of the most disaster prone areas in the world with challenges aplenty in effective disaster management. Aligning with the Sendai Framework for Disaster Risk Reduction 2015-2030, India evolved the National Disaster Management Plan (NDMP) of 2016 as a comprehensive disaster management framework that proposes an integrated approach involving relevant stakeholders for addressing diverse natural and human-induced hazards. The five principles of ISACA's COBIT 5 framework, a widely accepted comprehensive IT governance and management framework for enterprise IT, is used to analyze the NDMP 2016 to identify areas for improvement. © 2016 IEEE.

More »»

2012

Conference Paper

Dr. Vivek Menon, Jayaraman, B., and Govindaraju, V., “Spatio-Temporal Querying in Smart Spaces”, in Proc. of 3rd International Conference on Ambient Systems, Networks and Technologies (ANT-2012), Ontario, Canada, 2012, vol. 10, pp. 366–373.[Abstract]


A ‘smart space’ is one that automatically identifies and tracks its occupants using unobtrusive biometric modalities such as face, gait, and voice in an unconstrained fashion. Information retrieval in a smart space is concerned with information about the location of people at various points in time. Towards this end, we abstract a smart space by a probabilistic state transition system in which each state records the probabilities of presence of a set of individuals who are present in various zones of the smart space. We formulate a data model based upon an occupancy relation with a real-valued probability attribute and describe some of the spatio-temporal queries in SQL and CLP(R), focusing on the computation of probabilities, an aspect that is novel to this model. We define concepts of precision and recall to quantify the performance of this model based on its ability to answer various spatio-temporal queries and discuss results from our experimental prototype.

More »»

2011

Conference Paper

Dr. Vivek Menon, Jayaraman, B., and Govindaraju, V., “Spatio-Temporal Reasoning in Biometrics Based Smart Environments”, in Proc. of 2nd International Conference on Ambient Systems, Networks and Technologies (ANT-2011), Ontario, Canada, 2011, vol. 5, pp. 378–385.[Abstract]


We discuss smart environments that identify and track their occupants using unobtrusive recognition modalities such as face, gait, and voice. In order to alleviate the inherent limitations of recognition, we propose spatio-temporal reasoning techniques based upon an analysis of the occupant tracks. The key technical idea underlying our approach is to determine the identity of a person based upon information from a track of events rather than a single event. We abstract a smart environment by a probabilistic state transition system in which each state records a set of individuals who are present in various zones of the smart environment. An event abstracts a recognition step and the transition function defines the mapping between states upon the occurrence of an event. We define the concepts of ‘precision’ and ‘recall’ to quantify the performance of the smart environment. We provide experimental results to show performance improvements from spatio-temporal reasoning. Our conclusion is that the state transition system is an effective abstraction of a smart environment and the application of spatial-temporal reasoning enhances its overall performance. More »»

2008

Conference Paper

Dr. Vivek Menon, Jayaraman, B., and Govindaraju, V., “Integrating Recognition and Reasoning in Smart Environments”, in Proc. of the 4th IET Conference on Intelligent Environments (IE’ 08), Seattle, USA, 2008.[Abstract]


Our goal is to develop 'smart indoor environments' that are monitored unobtrusively by biometric capture devices, such as video cameras, microphones, etc. Such environments will keep track of their occupants and be capable of answering queries about the occupants' whereabouts. In order to develop a unified model that is applicable across diverse biometric modalities, we propose an abstract state transition framework in which different recognition steps are abstracted by events, and the reasoning necessary to effect state transitions is abstracted by a transition function. We define the metrics of 'precision' and 'recall' of a smart environment to evaluate how well it tracks its occupants. We show how the overall performance of our smart environment can be improved through the use of spatio-temporal knowledge of the environment. A prototype based upon our proposed abstract framework indicates that integrating recognition and reasoning capabilities substantially improves the overall performance of the environment. More »»

2008

Conference Paper

Dr. Vivek Menon, Jayaraman, B., and Govindaraju, V., “Biometrics Driven Smart Environments: Abstract Framework and Evaluation”, in Proc. of the 5th International Conference on Ubiquitous Intelligence and Computing (UIC-08), Oslo, Norway, 2008, vol. 5061 LNCS, pp. 75-89.[Abstract]


We present an abstract framework for ‘smart indoor environments’ that are monitored unobtrusively by biometrics capture devices, such as video cameras, microphones, etc. Our interest is in developing smart environments that keep track of their occupants and are capable of answering questions about the whereabouts of the occupants. We abstract the smart environment by a state transition system: Each state records a set of individuals who are present in various zones of the environment. Since biometric recognition is inexact, state information is probabilistic in nature. An event abstracts a biometric recognition step, and the transition function abstracts the reasoning necessary to effect state transitions. In this manner, we are able to accommodate different types of biometric sensors and also different criteria for state transitions. We define the notions of ‘precision’ and ‘recall’ of a smart environment in terms of how well it is capable of identifying occupants. We have developed a prototype smart environment based upon our proposed concepts, and provide experimental results in this paper. Our conclusion is that the state transition model is an effective abstraction of a smart environment and serves as a basis for integrating various recognition and reasoning capabilities. More »»

2006

Conference Paper

Dr. Vivek Menon, “Adaptation in Pervasive Computing Environments: A Multi-Agent Approach”, in Proc. of International Symposium on Ad Hoc and Ubiquitous Computing (ISAHUC'06), Surathkal, India, 2006.[Abstract]


This paper proposes a multi-agent based context-aware adaptive system that can autonomously adapt the levels of performance of applications/ services in event of a significant mismatch between demand and supply of resources in a pervasive computing environment. In event of scarcity of resources, the system autonomously chooses a best-fit adaptation strategy relevant to the context based on user preferences. The primary emphasis here is to continue to provide acceptable levels of a service, avoiding as far as possible, a direct involvement of the user. More »»

2003

Conference Paper

Dr. Vivek Menon and Diwakar, S., “Exploiting Mobile Multiagent Systems for Multiclass Proximal Support Vector Classification”, in Proc. of 13th International Conference on Artificial Neural Networks in Engineering (ANNIE 2003), St. Louis, USA, 2003.

Publication Type: Journal Article

Year of Publication Publication Type Title

2014

Journal Article

Dr. Vivek Menon, Jayaraman, B., and Govindaraju, V., “Probabilistic Spatio-Temporal Retrieval in Smart Spaces”, Journal of Ambient Intelligence and Humanized Computing, Springer-Verlag Berlin Heidelberg, vol. 5, no. 3, pp. 383–392, 2014.[Abstract]


A ‘smart space’ is one that automatically identifies and tracks its occupants using unobtrusive biometric modalities such as face, gait, and voice in an unconstrained fashion. Information retrieval in a smart space is concerned with the location and movement of people over time. Towards this end, we abstract a smart space by a probabilistic state transition system in which each state records the probabilities of presence of individuals in various zones of the smart space. We carry out track-based reasoning on the states in order to determine more accurately the occupants of the smart space. This leads to a data model based upon an occupancy relation in which time is treated discretely, owing to the discrete nature of events, but probability is treated as a real-valued attribute. Using this data model, we show how to formulate a number of spatio-temporal queries, focusing on the computation of probabilities, an aspect that is novel to this model. We present queries both in SQL syntax and also in CLP(R), a constraint logic programming language (with reals) which facilitates succinct formulation of recursive queries. We show that the answers to certain queries are better displayed in a graphical manner, especially the movement tracks of occupants of the smart space. We also define query-dependent precision and recall metrics in order to quantify how well the model is able to answer various spatio-temporal queries. We show that a query-dependent metric gives significantly better results for a class of occupancy-related queries compared with query-independent metrics.

More »»

2013

Journal Article

Dr. Vivek Menon, Jayaraman, B., and Govindaraju, V., “Enhancing Biometric Recognition with Spatio-Temporal Reasoning in Smart Environments”, Journal of Personal and Ubiquitous Computing, vol. 17, pp. 987–998, 2013.[Abstract]


We discuss smart environments that identify and track their occupants using unobtrusive recognition modalities such as face, gait, and voice. In order to alleviate the inherent limitations of recognition, we propose spatio-temporal reasoning techniques based upon an analysis of the occupant tracks. The key idea underlying our approach is to determine the identity of a person based upon information from a track of events rather than a single event. We abstract a smart environment by a probabilistic state transition system in which each state records a set of individuals who are present in various zones of the smart environment. An event abstracts a recognition step, and the transition function defines the mapping between states upon the occurrence of an event. We express two forms of spatio-temporal reasoning in the form of transition functions: a track-based transition function and an error-correcting transition function. We also define the concepts of ‘precision’ and ‘recall’ to quantify the performance of the smart environment and provide experimental results to clarify the performance improvements from spatio-temporal reasoning. Our conclusion is that the state transition system is an effective abstraction of a smart environment and the application of spatial-temporal reasoning enhances the overall performance of a biometric recognition system.

More »»

2011

Journal Article

Dr. Vivek Menon, Jayaraman, B., and Govindaraju, V., “The Three Rs of Cyberphysical Spaces”, IEEE Computer, vol. 44, pp. 73–79, 2011.[Abstract]


The ability to identify people and answer questions about their where- abouts in a cyberphysical space is critical to many applications. Inte- grating recognition with spatiotemporal reasoning enhances the overall performance of information retrieval. More »»

2010

Journal Article

Dr. Vivek Menon, Jayaraman, B., and Govindaraju, V., “Multimodal Identification and Tracking in Smart Environments”, Journal of Personal and Ubiquitous Computing, Special Issue on Multimodal Systems, Services and Interfaces for Ubiquitous Computing, vol. 14, pp. 685–694, 2010.[Abstract]


We present a model for unconstrained and unobtrusive identification and tracking of people in smart environments and answering queries about their whereabouts. Our model supports biometric recognition based upon multiple modalities such as face, gait, and voice in a uniform manner. The key technical idea underlying our approach is to abstract a smart environment by a state transition system in which each state records a set of individuals who are present in various zones of the environment. Since biometric recognition is inexact, state information is inherently probabilistic in nature. An event abstracts a biometric recognition step, and the transition function abstracts the reasoning necessary to effect state transitions. In this manner, we are able to integrate different biometric modalities uniformly and also different criteria for state transitions. Fusion of biometric modalities is also supported by our model. We define performance metrics for a smart environment in terms of the concepts of ‘precision’ and ‘recall’. We have developed a prototype implementation of our proposed concepts and provide experimental results in this paper. Our conclusion is that the state transition model is an effective abstraction of a smart environment and serves as a good basis for developing practical systems. More »»
207
PROGRAMS
OFFERED
6
AMRITA
CAMPUSES
15
CONSTITUENT
SCHOOLS
A
GRADE BY
NAAC, MHRD
8th
RANK(INDIA):
NIRF 2018
150+
INTERNATIONAL
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