Artificial Intelligence and Machine Learning

Overview

Artificial Intelligence and Machine Learning (AIML) are playing an increasingly pivotal role in shaping life, with their impact only expected to deepen over time. At TCG CREST, the AIML research group is dedicated to advancing excellence in this domain through fundamental, human-centric, and sustainable research.

Our primary goal is to generate cutting-edge research contributions and foster exceptional doctoral work. To this end, the group actively collaborates with leading institutions both within India and globally, creating a vibrant and collaborative research ecosystem. With a long-term vision of expanding its international research footprint, the group is focused on the following core areas and application domains.

Core Research Areas

Deep Neural Networks
Representation Learning
Deep Generative Models
Optimization
Multiagent Systems
Physics-informed Machine Learning
Mathematics for Machine Learning
Bias and Fairness
Green AI
Reinforcement Learning
Weakly Supervised & Self-Supervised Learning
Geometric Deep Learning

Application Domains

Computer Vision
Medical Image Analysis
Neuroimaging
Large Language Models
Mental Health
Cancer
Speech Technology
Coalition Structure Formation
Manufacturing & Automation
Energy & Utilities
Material Science

Members

Mentor

Swagatam Das, Professor, Indian Statistical Institute, Kolkata, India
Email: swagatam.das[at]tcgcrest.org OR swagatam.das[at]isical.ac.in
Website: https://www.isical.ac.in/~swagatam.das/

Faculty

Angshul Majumdar, Professor, PhD (University of British Columbia, Canada), 2012
Joined Institute in 2024
Email: angshul.majumdar[at]tcgcrest.org
Research Areas: Optimization, Signal Processing, Deep Learning, Bioinformatics, Smartgrid.
Website: https://www.iiitd.edu.in/~angshul/
Google Scholar: https://scholar.google.com/citations?user=nwNIkAUAAAAJ

Md Sahidullah, Assistant Professor, PhD (Indian Institute of Technology Kharagpur, India), 2015
Joined Institute in 2023
Email: md.sahidullah[at]tcgcrest.org
Research Areas: Speech and Audio Processing, Machine Learning, Deepfake Generation and Detection, Green AI.
Website: https://sahidullahmd.github.io/
Google Scholar: https://scholar.google.com/citations?user=jRCYfsQAAAAJ

Sourav Bhaduri, Assistant Professor and Ramalingaswami Fellow, PhD (Ghent University, Belgium), 2019
Joined Institute in 2023
Email: sourav.bhaduri[at]tcgcrest.org
Research Areas: MRI/MRSI, Multimodal Imaging, Biomedical Signal Processing, Medical Physics, Applied Mathematics, Machine Learning.
Website: https://www.tcgcrest.org/people/sourav-bhaduri/
Google Scholar: https://scholar.google.com/citations?user=uZhhZNMAAAAJ

Narayan Changder, Assistant Professor, PhD (National Institute of Technology Durgapur, India), 2021
Joined Institute in 2024
Email: narayan.changder[at]tcgcrest.org
Research Areas: Multi-agent Systems, Algorithm design, and Optimization.
Website: https://www.tcgcrest.org/people/narayan-changder/
Google Scholar: https://scholar.google.com/citations?hl=en&user=Mqlb-KUAAAAJ

Post Doctoral Fellow

Avisek Gupta, PhD (Indian Statistical Institute Kolkata, India), 2021
Field of Research: Machine Learning, Weak Supervised Learning, Machine Learning for Combinatorial Problems.
Google Scholar: https://scholar.google.com/citations?hl=en&user=mVhNXEWludgC

Sourav Raha, PhD (University of Florida, USA), 2020
Field of Research: Machine Learning
Google Scholar: https://scholar.google.com/citations?hl=en&user=Qvq0NqQAAAAJ

PhD Students

2020

Arijit Shaw (Supervisor: Kuldeep S. Meel)
Degree Granting Institute: Chennai Mathematical Institute
Field of Research: Artificial Intelligence
Google Scholar: https://scholar.google.com/citations?hl=en&user=YG9gqKcAAAAJ

Srinjoy Roy (Supervisor: Swagatam Das)
Degree Granting Institute: Chennai Mathematical Institute
Field of Research: Reinforcement Learning
Google Scholar: https://scholar.google.com/citations?hl=en&user=hIMgML8AAAAJ

2021

Rajdeep Mondal (Supervisor: Soumitra Samanta)
Degree Granting Institute: Ramakrishna Mission Vivekananda Educational and Research Institute (RKMVERI)
Field of Research: Variational Methods for Small Molecules Embedding

2022

Pierre-François Maillard (Supervisors: Sudipta Das and Bimal Kumar Roy)
Degree Granting Institute: Ramakrishna Mission Vivekananda Educational and Research Institute (RKMVERI)
Field of Research: Machine Learning in Cybersecurity
Google Scholar: https://scholar.google.com/citations?hl=en&user=d-h6XcwAAAAJ

Nikhil Raghav (Supervisors: Swami Punyeshwarananda and Md Sahidullah)
Degree Granting Institute: Ramakrishna Mission Vivekananda Educational and Research Institute (RKMVERI)
Field of Research: Speaker Diarization
Google Scholar: https://scholar.google.com/citations?hl=en&user=0Fv67UMAAAAJ

2023

Subhanon Bera (Supervisor: Sourav Bhaduri)
Degree Granting Institute: Academy of Scientific and Innovative Research (AcSIR)
Field of Research: Medical Image Processing

Tanmoy Jana (Supervisor: Angshul Majumdar)
Degree Granting Institute: Academy of Scientific and Innovative Research (AcSIR)
Field of Research: Optimization
Google Scholar: https://scholar.google.com/citations?hl=en&user=1CtC81AAAAAJ

Subhajit Saha (Supervisor: Angshul Majumdar)
Degree Granting Institute: Academy of Scientific and Innovative Research (AcSIR)
Field of Research: Reinforcement Learning & Optimization
Google Scholar: https://scholar.google.com/citations?user=P0S60L0AAAAJ&hl=en

Sankar Narayan Misra (Supervisors: Sourav Bhaduri and Angshul Majumdar)
Degree Granting Institute: Academy of Scientific and Innovative Research (AcSIR)
Field of Research: Medical Image Processing

2024

Prosondip Sadhukhan (Supervisors: Md Sahidullah and Goutam Mukherjee)
Degree Granting Institute: Academy of Scientific and Innovative Research (AcSIR)
Field of Research: Mathematics for Large Language Modeling

Activities & Achievements

Angshul Majumdar’s project proposal has been accepted for the Prime Minister’s Early Career Research Grant.
Sourav Bhaduri was awarded a travel grant worth £3,000 by the International Photoacoustic Standardisation Consortium (IPASC), UK, for a visit to the Centre for Preclinical Imaging, University of Liverpool, UK to work on Knowledge Exchange Partnership in Photoacoustic Imaging.
Subhajit Saha will be visiting Inria (Paris-Saclay, France) for a research stay from February to April 2025.
Nikhil Raghav received a dataset worth $600 from the Linguistic Data Consortium (University of Pennsylvania, USA) to support his research.
Sankar Narayan Misra received the Sigma Xi Grant-in-Aid of Research (GIAR) for the Fall 2024 cycle and was awarded $5000 by Sigma Xi, USA, for his research proposal titled “Advanced Segmentation, Coregistration, and Skull Stripping for Brain Tumor Treatment Planning Using Deep Learning Techniques.”
Subhanon Bera has been awarded the Trainee Educational Stipend for presenting an abstract at the International Society for Magnetic Resonance in Medicine (ISMRM) 2025.

Recent Publications

2025

Subhajit Saha, Angshul Majumdar, Swagatam Das, and Avisek Gupta, “Kernelized Low-Rank Matrix Recovery: Application in Collaborative Representation-Based Classification,” accepted in International Joint Conference on Neural Networks (IJCNN), 2025
Tuhin Kumar Biswas, Avisek Gupta, Narayan Changder, Swagatam Das, Redha Taguelmimt, Samir Aknine, and Animesh Dutta, “Compact agent neighborhood search for the SCSGA-MF-TS: SCSGA with multi-dimensional features prioritizing task satisfaction”, Information Sciences, vol. 706, pp. 122021, 2025. doi: 10.1016/j.ins.2025.122021.
Anal Roy Chowdhury, Avisek Gupta, and Swagatam Das, “Deep multi-view clustering: A comprehensive survey of the contemporary techniques”, Information Fusion, vol. 119, pp. 103012, 2025. doi: 10.1016/J.INFFUS.2025.103012.
S. N. Misra, S. Bera, S. Basak, S. Sarkar, A. Rajan, S. Mohan, H. Poptani, S. Chawla, S. Bhaduri, “Comparative Analysis of Deep Learning Models for Brain Tumor Segmentation in MRI Scans Using BraTS and Experimental Datasets”, International Society for Magnetic Resonance in Medicine (ISMRM), Honolulu, 2025.
S. N. Misra, S. Bera, A. Rajan, S. Mohan, S. Chawla, S. Bhaduri, “Comparative Analysis of Glioblastoma Segmentation Using U-Net Variants with Transfer Learning: nnU-Net, Attention U-Net, ELUNet, and U-Net++ on BraTS and Experimental MRI Dataset”, International Conference on Artificial Intelligence and Computer Vision in Medical Domain (AICVMD), BHU, Varanasi, 2025.
S. N. Misra, S. Bera, A. Rajan, S. Mohan, H. Poptani, S. Chawla, S. Bhaduri, “Comparative Analysis of Existing Deep Learning Models and New Approaches for Brain Tumor Segmentation in MRI Scans”, International Society for Magnetic Resonance in Medicine (ISMRM) Indian Chapter, Hyderabad, 2025.
S. Bera, S. Basak, S. N. Misra, G. W. Kostrzanowska, A. Rajan, S. Chawla, H. Poptani, S. Bhaduri, “Regional Comparison of DCE-MRI based Pharmacokinetic Model Fitting Accuracy in Glioblastoma”, International Society for Magnetic Resonance in Medicine (ISMRM) Conference, Honolulu, 2025.
S. Basak, G. W. Kostrzanowska, S. Bera, A. Rajan, S. Chawla, H. Poptani, S. Bhaduri, “Clustering-Based Multiparametric MRI for Differentiation Between True Tumor Progression and Pseudoprogression in Glioblastoma”, International Society for Magnetic Resonance in Medicine (ISMRM) Conference, Honolulu, 2025.
R. Taguelmimt, S. Aknine, D. Boukredera, N. Changder, and T. Sandholm, “A Multiagent Path Search Algorithm for Large-Scale Coalition Structure Generation,” in Proc. AAAI 2025.
T. Jana, N. Raghav, A. Majumdar, and M. Sahidullah, “Exact Rotation Invariant Robust PCA,” in Proc. ICASSP 2025.
N. Raghav, A. Gupta, M. Sahidullah, and S. Das, “Self-Tuning Spectral Clustering for Speaker Diarization,” in Proc. ICASSP 2025.
X. Liu, J. Yamagishi, M. Sahidullah, and T. Kinnunen, “Explaining Speaker and Spoof Embeddings via Probing,” in Proc. ICASSP 2025.
S. A. Sheikh, Y. Kaloga, M. Sahidullah, and I. Kodrasi, “Graph Neural Networks for Parkinson’s Disease Detection,” in Proc. ICASSP 2025.
S. Sahu, K. Kumar, A. Majumdar, A. A. Kumar and M. G. Chandra, “Graph Regularized AutoFuse: Robust Sensor Fusion With Noisy Labels,” in IEEE Sensors Letters, vol. 9, no. 2, pp. 1-4, Feb. 2025, Art no. 6002904, doi: 10.1109/LSENS.2025.3527058.
N. Saha, N. Changder, R. Taguelmimt, S. Aknine, and A. Dutta, “Hide Exposures by Removing Mastermind’s External Sources on Social Network (Student Abstract),” in Proc. AAAI 2025.

2024

Arkaprabha Basu, Sourav Raha, Avisek Gupta, and Swagatam Das, “Improved Alzheimer’s disease detection with dynamic attention guided multi-modal fusion”, in Proceedings of the International Conference on Pattern Recognition (ICPR), Kolkata, India, 2024, pp. 432–446. Cham, Switzerland: Springer Nature, 2025. doi: 10.1007/978-3-031-78195-7_29.
S. A. Hosseini, S. Servaes, B. Hall, S. Bhaduri, A. Rajan, P. Rosa-Neto, S. Brem, L. A. Loevner, S. Mohan, and S. Chawla, “Quantitative Physiologic MRI Combined with Feature Engineering for Developing Machine Learning-Based Prediction Models to Distinguish Glioblastomas from Single Brain Metastases”, Diagnostics, vol. 15, no. 1, p. 38, Dec. 2024. doi: 10.3390/diagnostics15010038.
Nikhil Raghav and Md Sahidullah, “Assessing the Robustness of Spectral Clustering for Deep Speaker Diarization”, in Proc. 21st IEEE India Council International Conference (IEEE INDICON), 2024.
S. Dey, Md Sahidullah, and G. Saha, “Towards Cross-Corpora Generalization for Low-Resource Spoken Language Identification”, in IEEE/ACM Transactions on Audio, Speech, and Language Processing. doi: 10.1109/TASLP.2024.3492807.
A. Goel and Angshul Majumdar, “Semi-Supervised Graphical Deep Dictionary Learning for Hyperspectral Image Classification From Limited Samples”, in Proc. IEEE ICIP, 2024.
K. Kumar, Angshul Majumdar, A.A. Kumar, and M.G. Chandra, “Multi-Modal Image Super-Resolution via Deep Convolutional Transform Learning”, in Proc. EUSIPCO, 2024.
G. AlRegib, M. Prabhushankar, K. Kokilepersaud, P. Chowdhury, Z. Fowler, S.T. Corona, L. Thomaz, and Angshul Majumdar, “Super Resolved Maize Plant Leaves Disease Detection Using Optimal Generative Adversarial NetworkOphthalmic Biomarker Detection: Highlights from the IEEE Video and Image Processing Cup 2023 Student Competition”, IEEE Signal Processing Magazine, vol. 41, no. 4, pp. 96-104, July 2024. doi: 10.1109/MSP.2024.3405667.
Arijit Shaw, Kuldeep S. Meel, “Model Counting in the Wild”, Proceedings of the 21st International Conference on Principles of Knowledge Representation and Reasoning, 2024.
S. Mukherjee, S. Bhaduri, R. Harwood, P. Murray, B. Wilm, R. Bearon, H. Poptani, “Multiparametric MRI based assessment of kidney injury in a mouse model of ischemia reperfusion injury”, Scientific Reports, vol. 14, no. 1, 19922, Aug. 27, 2024. doi: 10.1038/s41598-024-70401-x.
T. Alrashidi, S. Bhaduri, N. Non Gash, M. Moothancher, C. Ball, M. Maguire, L . Ressel, H. Poptani, “1H MR spectroscopy and IVIM-DWI to evaluate the effect of a choline kinase inhibitor and temozolomide therapy in a mouse model of glioblastoma”, International Society for Magnetic Resonance in Medicine (ISMRM) Conference, Singapore, 2024.
X. Wang, H. Delgado, H. Tak, J. Jung, H.J. Shim, M. Todisco, I. Kukanov, X. Liu, M. Sahidullah, T. Kinnunen, N. Evans, K.A. Lee, J. Yamagishi, “ASVspoof 5: Crowdsourced speech data, deepfakes, and adversarial attacks at scale”, ASVspoof Workshop 2024 – INTERSPEECH 2024 Satellite Event.
H.J. Shim, M. Sahidullah, J.W., Jung, S. Watanabe, T. Kinnunen, “Beyond Silence: Bias Analysis through Loss and Asymmetric Approach in Audio Anti-Spoofing”, SynData4GenAI 2024 – INTERSPEECH 2024 Satellite Event.
P. Gupta, A. Goel, A. Majumdar, E. Chouzenoux, G. Chierchia, “Deconfcluster: Deep convolutional transform learning based multiview clustering fusion framework”, Signal Processing, vol. 224, p. 109597, 2024.
K. Kumar, A. Majumdar, A. Anil Kumar, M. Girish Chandra, “Unsupervised Domain Adaptation for Machine Fault Diagnosis via Subspace Interpolation Using Deep Transforms”, IEEE Sensors Journal, vol. 24, no. 13, pp. 20959-20969, July 1, 2024. doi: 10.1109/JSEN.2024.3398364.
K. Kumar, A. Majumdar, A. A. Kumar, M. G. Chandra, “Convolutional Analysis Sparse Coding for Multimodal Image Super-Resolution”, IEEE Sensors Letters, vol. 8, no. 6, Art no. 6006404, pp. 1-4, June 2024. doi: 10.1109/LSENS.2024.3403179.
A. Majumdar, “Energy Disaggregation via Deep Convolutional Dictionary Learning”, IEEE Sensors Letters, vol. 8, no. 6, Art no. 7003104, pp. 1-4, June 2024. doi: 10.1109/LSENS.2024.3396295.
A. Goel, A. Majumdar, “Sparse Subspace Clustering Incorporated Deep Convolutional Transform Learning for Hyperspectral Band Selection”, Earth Science Informatics, vol. 17, no. 3, pp. 2727-2735, 2024.
A. Goel, A. Majumdar, “Contrastive Deep Convolutional Transform K-Means Clustering”, Information Sciences, vol. 661, p. 120191, 2024.
P. Rai, A. Jain, S. Kumar, D. Sharma, N. Jha, S. Chawla, A. Raj, A. Gupta, S. Poonia, A. Majumdar, T. Chakraborty, G. Ahuja, D. Sengupta, “Literature mining discerns latent disease-gene relationships”, Oxford Bioinformatics, vol. 40, no. 4, 2024.
Arijit Shaw, Kuldeep S. Meel, “CSB: A Counting and Sampling tool for Bitvectors”, Proc. International Workshop on Satisfiability Modulo Theories, 2024.
Pierre-François Maillard, Avisek Gupta, “Classification of Return-Oriented Programming Gadgets: A Machine Learning approach”, Journal of Computer Virology and Hacking Techniques, vol. 20, no. 4, pp. 751-763, Nov. 2024. doi: 10.1007/s11416-024-00517-1.
Arindam Majee, Avisek Gupta, Sourav Raha, Swagatam Das, “Enhancing MRI-Based Classification of Alzheimer’s Disease with Explainable 3D Hybrid Compact Convolutional Transformers”, in Proceedings of the 2024 International Joint Conference on Neural Networks (IJCNN-2024), Yokohama, Japan, 2024. doi: 10.1109/IJCNN60899.2024.10650462.
V.P. Singh, F. Malato, V. Hautamäki, Md Sahidullah, T. Kinnunen, “ROAR: Reinforcing original to augmented data ratio dynamics for wav2vec2.0 based ASR”, Proc. INTERSPEECH 2024.
Subhajit Saha, Md Sahidullah, Swagatam Das, “Exploring green AI for audio deepfake detection”, Proc. EUSIPCO 2024.
R. Taguelmimt, S. Aknine, D. Boukredera, Narayan Changder, T. Sandholm, “A Faster Optimal Coalition Structure Generation via Offline Coalition Selection and Graph-Based Search”, IJCAI 2024.
Arijit Shaw, B. Juba, K. S. Meel, “An Approximate Skolem Function Counter”, AAAI 2024. https://ojs.aaai.org/index.php/AAAI/article/view/28650.
X. Liu, Md Sahidullah, K.A. Lee, T. Kinnunen, “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.
V.P. Singh, Md Sahidullah, T. Kinnunen, “ChildAugment: Data augmentation methods for zero-resource children’s speaker verification”, The Journal of the Acoustical Society of America. doi: 10.1121/10.0025178.
Shion Samadder Chaudhury, Payel Sadhukhan, Kousik Sengupta, “Explainable AI using the Wasserstein Distance”, IEEE Access, 2024.
S. Palit, Payel Sadhukhan, “Parameter-free Under sampling for Multi-label Data”, ICAART 2024 (Nominated for the Best Industrial Paper Award).
P. F. Maillard, S. Das, and B. K. Roy, “Minimal Adversarial Attacks on Predictive Maintenance,” 2024 8th International Conference on System Reliability and Safety (ICSRS), Sicily, Italy, 2024, pp. 167-176, doi: 10.1109/ICSRS63046.2024.10927430.

2023

J. Yang, A. Shaw, T. Baluta, M. Soos, K. S. Meel, “Explaining SAT Solving Using Causal Reasoning”, SAT 2023. https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SAT.2023.28.
H.-j. Shim, R. Gonzalez Hautamäki, M. Sahidullah, T. Kinnunen, “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, “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.
X. Liu, M. Sahidullah, K.A. Lee, T. Kinnunen, “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, “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, “ASVspoof 2021: Towards spoofed and deepfake speech detection in the wild”, IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2023. doi: 10.1109/TASLP.2023.3285283.
S.A. Sheikh, M. Sahidullah, F. Hirsch, S. Ouni, “Stuttering detection using speaker representations and self-supervised contextual embeddings”, International Journal of Speech Technology, 2023. 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 Informed 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.
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 ℓ1 Regularization”, International Conference on Artificial Intelligence and Statistics, 2023, pp. 7176-7189.
R. Taguelmimt, S. Aknine, D. Boukredera, N. Changder, and T. Sandholm, “Optimal Anytime Coalition Structure Generation Utilizing Compact Solution Space Representation”, 32nd International Joint Conference on Artificial Intelligence (IJCAI-2023).
R. Taguelmimt, S. Aknine, D. Boukredera, and N. Changder, “Parallel Index-based Search Algorithm for Coalition Structure Generation (Student Abstract)”, Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23).
R. Taguelmimt, S. Aknine, D. Boukredera, N. Changder, and T. Sandholm, “A Multiagent Path Search Algorithm for Large-Scale Coalition Structure Generation”, 14th Workshop on Optimization and Learning in Multiagent Systems at AAMAS 2023.
R. Taguelmimt, S. Aknine, D. Boukredera, N. Changder, and T. Sandholm, “Faster Optimal Coalition Structure Generation via Offline Coalition Selection and Graph-Based Search”, 14th Workshop on Optimization and Learning in Multiagent Systems at AAMAS 2023.
R. Taguelmimt, S. Aknine, D. Boukredera, N. Changder, and T. Sandholm, “Efficient Size-based Hybrid Algorithm for Optimal Coalition Structure Generation”, 14th Workshop on Optimization and Learning in Multiagent Systems at AAMAS 2023.