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.

  • Statistical Learning Theory
  • Deep Neural Networks
  • Representation Learning
  • Deep Generative Models
  • Computer Vision
  • Natural Language Processing
  • Large Language Models
  • Climate Informatics
  • AI in Law
  • Privacy-preserving Learning






  • Developed a search engine on Legal summarization.

  • Maintained a Covid Bulletin in Indian context for last two years (ongoing). [Covid-19 R_0 Estimation]
  • We have 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

  • X. Liu, M. Sahidullah, K.A. Lee, T. Kinnunen, (2024) Generalizing speaker verification for spoof awareness in the embedding space. IEEE/ACM Transactions on Audio, Speech, and Language Processing (doi: 10.1109/TASLP.2024.3358056).

  • A. Shaw, B. Juba, K. S. Meel: An Approximate Skolem Function Counter. AAAI 2024. https://ojs.aaai.org/index.php/AAAI/article/view/28650
  • V.P. Singh, M.Sahidullah, T. Kinnunen, (2024). ChildAugment: Data augmentation methods for zero-resource children’s speaker verification. The Journal of the Acoustical Society of America (doi: 10.1121/10.0025178).

  • S. S. Chaudhury, P. Sadhukhan, K. Sengupta: Explainable AI using the Wasserstein Distance. IEEE Access 2024.

  • S. Palit, P. Sadhukhan: Parameter-free Undersampling for Multi-label Data. ICAART 2024 (Nominated for the Best Industrial Paper Award).

  • H.-j. Shim, R. Gonzalez Hautamäki, M. Sahidullah, T. Kinnunen (2023) How to construct perfect and worse-than-coin-flip spoofing countermeasures: A word of warning on shortcut learning. In Proc. INTERSPEECH 2023, 785-789. doi: 10.21437/Interspeech.2023-1901

  • V.P. Singh, M. Sahidullah, T. Kinnunen (2023) Speaker verification across ages: Investigating deep speaker embedding sensitivity to age mismatch in enrollment and test speech. In Proc. INTERSPEECH 2023, 1948-1952. doi: 10.21437/Interspeech.2023-2052

  • J. Yang, A. Shaw, T. Baluta, M.Soos, K. S. Meel: Explaining SAT Solving Using Casual Reasoning. SAT 2023. https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2023.28
  • X. Liu, M. Sahidullah, K.A. Lee, T. Kinnunen (2023) Speaker-aware anti-spoofing. In Proc. INTERSPEECH 2023, 2498-2502. doi: 10.21437/Interspeech.2023-1323

  • S.H. Mun, H.-j. Shim, H. Tak, X. Wang, X. Liu, M. Sahidullah, M. Jeong, M.H. Han, M. Todisco, K.A. Lee, J. Yamagishi, N. Evans, T. Kinnunen, N.S. Kim, J.-w. Jung (2023) Towards single integrated spoofing-aware speaker verification embeddings. In Proc. INTERSPEECH 2023, 3989-3993. doi: 10.21437/Interspeech.2023-1402

  • X. Liu, X. Wang, M. Sahidullah, J. Patino, H. Delgado, T. Kinnunen, M. Todisco, J. Yamagishi, N. Evans, A. Nautsch, K.A. Lee (2023) ASVspoof 2021: Towards spoofed and deepfake speech detection in the wild. IEEE/ACM Transactions on Audio, Speech, and Language Processing. doi: 10.1109/TASLP.2023.3285283

  • S.A. Sheikh, M. Sahidullah, F. Hirsch, S. Ouni (2023) Stuttering detection using speaker representations and self-supervised contextual embeddings. International Journal of Speech Technology. doi: 10.1007/s10772-023-10032-1 

  • P. Sadhukhan, L. Halder, S. Palit: Approximate DBSCAN on obfuscated data. Journal of Information Security and Applications 2023.

  • P. Sadhukhan, S. Palit: Be Infomed of the known to Catch the unknown. PRICAI 2023.

  • P. Sadhukhan, A. Pakrashi, S. Palit, B. Mac Namee: Integrating Unsupervised Clustering and Label-Specific Oversampling to Tackle Imbalanced Multi-Label Data. ICAART 2023 (Nominated for the Best Paper Award). 
  • S. Datta, S. Mullick, A. Chakrabarty, S. Das: Interval Bound Interpolation for Few-shot Learning with Few Tasks. 40th International Conference on Machine Learning (ICML 2023).

  • C. Chakraborty, S. Paul, S. Chakraborty, S. Das: Clustering High-dimensional Data with Ordered Weighted $\ell_1$ Regularization. International Conference on Artificial Intelligence and Statistics (2023), 7176-7189.
  • Abhishek Kumar, Oladayo S Ajani, Swagatam Das, Rammohan Mallipeddi: GridShift: A Faster Mode-seeking Algorithm for Image Segmentation and Object Tracking, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 8131-8139.

  • Abhishek Kumar, Swagatam Das, and Rammohan Mallipeddi: An Efficient Differential Grouping Algorithm for Large-Scale Global Optimization. IEEE Transactions on Evolutionary Computation, Accepted for publication. (ISSN: 1089-778X) 2022.
  • Amit Kumar, Basant Kumar Sethi, Abhishek Kumar, Devender Singh, and Rakesh Kumar Misra: Three-Level Hierarchical Management of Active Distribution System With Multimicrogrid. IEEE Systems Journal, Accepted for publication. (ISSN: 1932-8184).
  • Abhishek Kumar, Swagatam Das, and Vaclav Snášel: Improved Spherical Search with Local Distribution induced Self-Adaptation for Hard Non-convex Optimization with and without Constraints. Information Sciences, Accepted for Publication. (ISSN: 1872-6291).

  • 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