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Faculty Directory Chiradeep Mukherjee
Chiradeep Mukherjee

Chiradeep Mukherjee

Associate Professor

[email protected]
Vidwan Profile Google Scholar

Academic Information

PhD, NIT Durgapur, 2022

Research Areas

Artificial Intelligence Machine Learning Deep Learning Natural Language Processing

Research Interests

My research interests primarily focus on machine learning (ML) and deep learning (DL) for intelligent and energy-efficient computing systems. I work on designing and optimizing ML/DL models for pattern recognition, image analysis, and classification tasks, including biometric recognition and plant-leaf disease detection. Alongside this, I explore the integration of ML-driven optimization techniques with emerging hardware paradigms such as low-power and reversible digital architectures. My broader interest lies in combining data-driven learning approaches with efficient computational models to achieve scalable, accurate, and resource-aware intelligent systems suitable for next-generation applications.

Courses Taught

Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Quantum Computing

Positions Held

Associate Professor

Publications

[1] C. Mukherjee, S. Panda, A. K. Mukhopadhyay, and B. Maji, “Towards modular binary to gray converter design using LTEx module of quantum-dot cellular automata,” Microsystem Technologies, vol. 25, no. 5, pp. 2011–2018, 2019. [2] C. Mukherjee, S. Panda, A. K. Mukhopadhyay, and B. Maji, “Utilization of LTEx Feynman Gate in Designing the QCA based Reversible Binary to Gray and Gray to Binary Code Converters,” Micro and Nanosystems, vol. 12, no. 1, 2020. [3] C. Mukherjee, S. Panda, A. K. Mukhopadhyay, and B. Maji, “The design, analysis, and cost estimation of a generic add... methodology with a quantum-dot cellular-automata-based approach,” Journal of Computational Electronics, vol. 20, no. 4, pp. 1611–1624, 2021. [4] M. Dhar, C. Mukherjee, A. Banerjee, D. Manna, S. Panda, and S. Mukhopadhyay, “Predicting Energy Dissipation in QCA-Based Lay...h Machine-Learning Models,” Journal of Electronic Testing, 2024. [5] C. Mukherjee, P. Mondal, K. Sarkar, S. Paul, A. Saha, and A. Gajbhiye, “Enhanced artificial neural network-based SER m...anguage,” International Journal of Information Technology, 2024. [6] A. Chakraborty, C. Mukherjee, R. Ganguly, and M. Mitra, “Predicting Optical Properties of Planar Photonic Crystals using machine learning,” Journal of Optics, pp. Jan-15, 2025. [7] S. S. Mekale, M. Chakraborty, and C. Mukherjee, “TS-GCN: a hybrid...gonemia,” International Journal of Information Technology, 2025. [8] A. Chakraborty, C. Mukherjee, R. Ganguly, and M. Mitra, “Performance...g Machine Learning,” IETE Journal of Research, pp. Jan-18, 2025. [9] S. S. Mekale, M. Chakraborty, and C. Mukherjee, “Diabetic Fo...t and Information Systems,” KSII Transactions on Internet and Information Systems, vol. 19, no. 11, pp. 3750–3777, 2025. [10] M. Dhar, C. Mukherjee, S. Panda, B. Maji, and A. Majumder, “Predictive Analysis of Energy Dissipation in Layered-T QCA Circuits under Cell Displacement Defects and Polarization: A Machine-Learning Approach,” Integration, 2025.