Overview
Background
Dr. Arnab Bhattacharjee is an early-career AI and applied optimization researcher specializing in energy systems, power engineering, and machine learning. He earned his Ph.D. with distinction in Electrical Engineering and Computer Science from the University of Queensland and the Indian Institute of Technology Delhi Academy of Research, where he advanced AI-driven modeling for energy systems and cybersecurity. Previously, he completed his B.Tech. (Honors) in Electrical & Electronics Engineering with a minor in Computer Applications at NIT Trichy, graduating as valedictorian. His research experience spans leading institutions, including IIT Delhi, UQ, and Max Planck Institute for Intelligent Systems, where he has contributed to optimization, deep learning, and reinforcement learning applications for sustainable energy and intelligent systems. His expertise includes developing empirical mutual information estimation tools, battery degradation modeling, and cyber-physical security solutions for smart grids. Dr. Bhattacharjee has also collaborated with TATA Power, Alt Mobility, and government agencies to enhance energy forecasting, EV infrastructure planning, and AI-driven optimization techniques.
Availability
- Dr Arnab Bhattacharjee is:
- Available for supervision
Qualifications
- Bachelor (Honours) of Electrical and Electronic Engineering, National Institute of Technology Tiruchirappalli
- Doctor of Philosophy of Electrical Engineering and Computer Science, The University of Queensland - Indian Institute of Technology Delhi Academy of Research
Research interests
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Large Sequence Modelling and multimodal AI for Energy Applications
Developing large-scale holistic sequence models for extremely long, rugged and high dimensional time series data and multimodal AI for exploring novel methodologies and paradigms for better energy system operation - these include graph models, point cloud models and sequence models in general.
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Generative AI for uncertainty quantification in energy systems
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Applied Optimization
Model Predictive Control, Convex Optimization, Optimization on networks, Dynamical systems, Combinatorial Optimization, Surrogate Modelling
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Multi-agent Reinforcement Learning
Developing multi-agent reinforcement learning models for identifying new energy market models, community energy optimization and power system resiliency.
Research impacts
Dr. Bhattacharjee’s research significantly impacts energy systems, AI applications, and cybersecurity in modern power grids. His contributions include deep adversarial attack mitigation strategies for cyber-physical systems, optimized energy trading models, and AI-driven modelling and optimization for renewable integration. His work on Li-ion battery modelling and degradation prognosis provides crucial insights into battery longevity, facilitating improved EV infrastructure and fleet management. Through collaborations with TATA Power and Alt Mobility, he has developed advanced forecasting algorithms for EV load management, enhancing power grid stability and operational efficiency. His research extends beyond energy systems, as seen in his foundational contributions to conditional mutual information estimation and deep-learning techniques for medical imaging applications, notably for COVID-19 screening. His peer-reviewed publications in IEEE journals and conferences underscore his influence in AI-driven energy optimization and cyber-secure power networks. By integrating machine learning, optimization, and domain-specific expertise, Dr. Bhattacharjee’s research advances scalable, intelligent solutions for modern energy challenges, reinforcing the sustainability and reliability of future power networks.
Works
Search Professor Arnab Bhattacharjee’s works on UQ eSpace
2024
Journal Article
DeeBBAA: a benchmark deep black box adversarial attack against cyber-physical power systems
Bhattacharjee, Arnab, Bai, Guangdong, Tushar, Wayes, Verma, Ashu, Mishra, Sukumar and Saha, Tapan K. (2024). DeeBBAA: a benchmark deep black box adversarial attack against cyber-physical power systems. IEEE Internet of Things Journal, 11 (24), 40670-40688. doi: 10.1109/jiot.2024.3454257
2024
Other Outputs
Advancing energy system modelling and cybersecurity through holistic AI development
Bhattacharjee, Arnab (2024). Advancing energy system modelling and cybersecurity through holistic AI development. PhD Thesis, School of Information Technology and Electrical Engineering, The University of Queensland. doi: 10.14264/8c101e2
2023
Journal Article
Deep latent space clustering for detection of stealthy false data injection attacks against AC state estimation in power systems
Bhattacharjee, Arnab, Mondal, Arnab Kumar, Verma, Ashu, Mishra, Sukumar and Saha, Tapan K. (2023). Deep latent space clustering for detection of stealthy false data injection attacks against AC state estimation in power systems. IEEE Transactions on Smart Grid, 14 (3), 1-1. doi: 10.1109/tsg.2022.3216625
2022
Conference Publication
Deep adversary based stealthy false data injection attacks against AC state estimation
Bhattacharjee, Arnab, Mishra, Sukumar and Verma, Ashu (2022). Deep adversary based stealthy false data injection attacks against AC state estimation. 2022 IEEE PES 14th Asia-Pacific Power and Energy Engineering Conference (APPEEC), Melbourne, Australia, 20-23 November 2022. Piscataway, NJ, United States: Institute of Electrical and Electronics Engineers. doi: 10.1109/appeec53445.2022.10072221
2022
Journal Article
xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography
Mondal, Arnab Kumar, Bhattacharjee, Arnab, Singla, Parag and Prathosh, A. P. (2022). xViTCOS: Explainable Vision Transformer Based COVID-19 Screening Using Radiography. IEEE Journal of Translational Engineering in Health and Medicine, 10 1100110, 1-10. doi: 10.1109/jtehm.2021.3134096
2021
Journal Article
Estimating state of charge for xEV batteries using 1D convolutional neural networks and transfer learning
Bhattacharjee, Arnab, Verma, Ashu, Mishra, Sukumar and Saha, Tapan (2021). Estimating state of charge for xEV batteries using 1D convolutional neural networks and transfer learning. IEEE Transactions on Vehicular Technology, 70 (4) 9372902, 1-1. doi: 10.1109/tvt.2021.3064287
2020
Conference Publication
C-MI-GAN : Estimation of Conditional Mutual Information Using MinMax Formulation
Mondal, Arnab Kumar, Bhattacharjee, Arnab, Mukherjee, Sudipto, Prathosh, A. P., Kannan, Sreeram and Asnani, Himanshu (2020). C-MI-GAN : Estimation of Conditional Mutual Information Using MinMax Formulation. Conference on Uncertainty in Artificial Intelligence (UAI), Online, 3-6 August 2020. San Diego, CA United States: Proceedings of Machine Learning Research.
2019
Conference Publication
A Novel Stochastic Optimization Algorithm Inspired From The Biology Of Plant Reproduction
Sundareswaran, Kinattingal and Bhattacharjee, Arnab (2019). A Novel Stochastic Optimization Algorithm Inspired From The Biology Of Plant Reproduction. 2019 IEEE International Conference on Electrical, Computer and Communication Technologies (ICECCT), Coimbatore, India, 20-22 February 2019. Piscataway, NJ United States: IEEE. doi: 10.1109/icecct.2019.8869007
Supervision
Availability
- Dr Arnab Bhattacharjee is:
- Available for supervision
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