Artificial Intelligence and Machine Learning (AI-ML) are intertwined with every aspect of our daily lives. Their pertinence is increasing with time as well. The AI-ML group at TCG CREST is motivated to achieve excellence in this field through fundamental, human-centric and sustainable research. The primary goal is to deliver path-breaking research outputs and fantastic PhD theses. The researchers in this group are already carrying out collaborative research with premier institutes in India and abroad. The group strives to expand its research network globally in the long run.  The group mostly works in the following areas of specialization.

  • Class imbalance learning
  • Open set classification
  • Privacy-preserving learning
  • Reinforcement learning
  • Differential Evolution
  • Neural Architecture Search
  • Computer Vision
  • Generative Adversarial Network
  • Image Synthesis
  • Vision Transformers
  • Short-text retrieval
  • Legal information retrieval
  • Quantum Machine Learning
  • Machine Learning applications in Digital Library and Teaching Learning
  • Machine intelligence techniques for performance improvement in Wireless Networks
  • Authentication and access control for Network Security
  • Smart assisted living



  • Developed a search engine on Legal summarization.

  • Maintained a Covid Bulletin in Indian context for last two years (ongoing). [Covid-19 R_0 Estimation]
  • Prof. Samiran Chattopadhyay became the P.I. of an industry sponsored project on “Development of sentiment and emotion analysis algorithms from market news, blogs, social network data and evaluation of these algorithms by computing cross correlation between outputs of these algorithms with typical stock market index such as nifty fifty”

Recent Publications

  • Samiran Chattopadhyay, Improving Performance of High Throughput Wireless Access Networks – An Experience in Learning, 23rd International Conference on Distributed Computing and Networking (A CORE Ranked Conference), January 2022 Pages 229–231, https://doi.org/10.1145/3491003.3493331

  • Mondal, Anindita .S., Mukhopadhyay, Anirban and Chattopadhyay, Samiran. Machine learning-driven automatic storage space recommendation for object-based cloud storage system. Complex Intelligent. System. 8, 489–505 (2022). https://doi.org/10.1007/s40747-021-00517-4

  • Avishek Banerjee, Sudip De, Koushik Majumder, Dinesh Dash and Samiran Chattopadhyay. Construction of energy minimized WSN using GA-SAMP-MWPSO and K-mean clustering algorithm with LDCF deployment strategy. J Supercomput (2022). https://doi.org/10.1007/s11227-021-04265-7

  • Arjun Pakrashi, Payel Sadhukhan, Brian Mac Namee, ML-NCA: Multi-label Neighbourhood Component Analysis, Proceedings of the Third International Workshop on Learning with Imbalanced Domains: Theory and Applications, PMLR 154:35-48, 2021

  • Dipanwita Thakur, Suparna Biswas, Edmond S. L. Ho and Samiran Chattopadhyay, ConvAE-LSTM: Convolutional Autoencoder Long Short-Term Memory Network for Smartphone-Based Human Activity Recognition, IEEE Access, vol. 10, pp. 4137-4156, 2022. https://doi.org/10.1109/ACCESS.2022.3140373

  • R. Karmakar, G. Kaddoum and S. Chattopadhyay, SmartCon: Deep Probabilistic Learning Based Intelligent Link-Configuration in Narrowband-IoT Towards 5G and B5G, in IEEE Transactions on Cognitive Communications and Networking (Early Access), 2021, https://doi.org/10.1109/TCCN.2021.3130985

  • Sadhukhan, P, Palit, S. Oversampling the minority class using a dedicated fitness function and genetic algorithmic progression. Concurrency Computat Pract Exper. 2021;e6648. https://doi/10.1002/cpe.6648

  • Soumya Banerjee, Debarshi Kumar Sanyal, Samiran Chattopadhyay, P. K. Bhowmick and P. P. Das, Automatic Recognition of Learning Resource Category in a Digital Library, 2021 ACM/IEEE Joint Conference on Digital Libraries (JCDL, QUALIS A Conference), 2021, pp. 289-290, https://doi/10.1109/JCDL52503.2021.00039