Vijay Pakka currently works as a Senior Lecturer at the Institute of Energy and Sustainable Development, De Montfort University, Leicester, UK. He has worked on EPSRC projects involving modelling and analysis of Smart Grids and has worked with a range of modelling and analysis tools such as multiagent systems, statistical models, machine learning algorithms and metaheuristic techniques. He has special interests in modelling and analysis of electrical grids, stochastic dynamic systems, and in statistical analysis. Prior to joining DMU, Vijay completed his PhD in Computer Science from the University of Southampton, UK, and his MSc from the Indian Institute of Science, Bangalore, India. His PhD thesis investigated Complexity Issues in Gene Regulatory Networks using Machine Learning and Stochastic Analysis Techniques.
Research Keywords & Expertise
Electricity Markets
Optimization
Power Distribution
Power Transmission
Smart Grid
Trading Strategies
Blockchain
Electrical Power Engin...
Distributed energy res...
spot markets
Stochastic dynamical s...
Gene regulatory networ...
Statistical analysis
Machine learning (ML)
Peertopeer energy trad...
Fingerprints
17%
Electricity Markets
17%
Optimization
9%
Gene regulatory networks (GRNs)
9%
Statistical analysis
Short Biography
Vijay Pakka currently works as a Senior Lecturer at the Institute of Energy and Sustainable Development, De Montfort University, Leicester, UK. He has worked on EPSRC projects involving modelling and analysis of Smart Grids and has worked with a range of modelling and analysis tools such as multiagent systems, statistical models, machine learning algorithms and metaheuristic techniques. He has special interests in modelling and analysis of electrical grids, stochastic dynamic systems, and in statistical analysis. Prior to joining DMU, Vijay completed his PhD in Computer Science from the University of Southampton, UK, and his MSc from the Indian Institute of Science, Bangalore, India. His PhD thesis investigated Complexity Issues in Gene Regulatory Networks using Machine Learning and Stochastic Analysis Techniques.