Academic Profile : Faculty

Assoc Prof Arvind Easwaran.JPG picture
Assoc Prof Arvind Easwaran
Associate Professor, School of Computer Science and Engineering
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Arvind Easwaran is an Associate Professor in the School of Computer Science and Engineering at Nanyang Technological University (NTU), Singapore. He received a PhD degree in Computer and Information Science from the University of Pennsylvania, USA, in 2008. Prior to joining NTU in 2013, he has been an Invited Research Scientist at the Polytechnic Institute of Porto, Portugal, between 2009 and 2010, and a Research & Development Scientist at Honeywell Aerospace, USA, between 2010 and 2012. In NTU, he is leading (has led) several research projects including real-time scheduling theory for mixed-criticality systems, model-in-the-loop intelligent framework for smart manufacturing, real-time optimisation for building energy systems, and assured safety architectures for learning-enabled cyber-physical systems. He was a ACM Distinguished Speaker between 2018-2020, a Cluster Director in the Future Mobility Solutions research programme at the Energy Research Institute @ NTU, and a recipient of the Nanyang Education Award (School) in 2020.
Cyber-Physical Systems (CPS) encompass systems in which the cyber world of computation and communication closely interacts with the physical world of sensors and actuators. These are highly networked and deeply embedded systems such as those found in modern day aircrafts, automotives, factories, medical devices, smart phones, electric grids, etc. From driver-less cars and air traffic management using sense and avoid, to plug-and-play operating rooms and smart electric grids that integrate traditional and renewable energy sources, intelligent automation of an enormous scale is finding its way into many of these systems.

Primary interests of the CPS Research Group @SCSE,NTU are in the design and analysis of safety-critical and time-critical CPS, with a particular focus on cyber-resource management and safety assurance. The current research themes can be broadly classified as follows:

Resource Management in IoT and Edge
Safety Assurance in Learning Enabled Components
Model-driven Operational Optimization
 
  • Assured-Safety Architecture for Machine Learning based CPS
  • Intelligent Modelling for Decision-making in Critical Urban Systems (DESCARTES)
  • Intelligent Modelling for Decision-making in Critical Urban Systems (DESCARTES): Main Account SCSE (Sub-project 1)
  • Trust to Train and Train to Trust: Agent Training Programs for Safety-Critical Environments (SCSE)