Research Highlights

Our Research

Our contributions as part of the Machine Learning Research Group (MLRG) can be broadly categorized into theoretical analysis and applications of Machine Learning. Our research articles regularly find their rightful places in top-tier venues such as AAAI, IEEE TPAMI, IEEE TNNLS, ICCV, NeurIPS, MICCAI, CVPR, AISTATS, etc. The impact of our publications is further evident by the 2000+ times they are cited over recent years.

Primary Research Interests

Building Theoretical Machine Learning Algorithms

• Theoretical Analysis of Clustering Algorithms

• Deep Generative Algorithms

Machine Learning Algorithms in Applications of Importance

• Addressing the Curse of Class Imbalance

• Machine Learning for Healthcare

• Machine Learning for Social Good

Our Recent Publications

Please visit the Google Scholar page of our faculty member Swagatam Das for a detailed list of our publications. Here we are only presenting our latest works done over the past year.

You can also find implementations in MatLab or Python for many of these works in the GitHub profiles of mlrgisi, Sankha Subhra Mullick, and Shounak Datta.

• Susmita Ghosh and Swagatam Das. 2024. Enhancing Medical Image Analysis with MA-DTNet: A Dual Task Network Guided by Morphological Attention. In Pattern Recognition: 27th International Conference, ICPR 2024, Kolkata.
• Arkaprabha Basu, Sourav Raha, Avisek Gupta, and Swagatam Das. "Improved Alzheimer’s Disease Detection with Dynamic Attention Guided Multi-modal Fusion." In Pattern Recognition: 27th International Conference, ICPR 2024, pp. 432-446.
• Abhishek Kumar, Anish Chakrabarty, Sankha Subhra Mullick and Swagatam Das. "Enhancing Contrastive Clustering with Negative Pair-guided Regularization". Transactions on Machine Learning Research (TMLR) 2024.
• Faizanuddin Ansari, Tapabrata Chakraborti, and Swagatam Das. "Algorithmic Fairness in Lesion Classification by Mitigating Class Imbalance and Skin Tone Bias." International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI - 2024). Cham: Springer Nature Switzerland, 2024.
• Priyobrata Mondal, Faizanuddin Ansari, and Swagatam Das. "CCO: A Cluster Core-Based Oversampling Technique for Improved Class-Imbalanced Learning".IEEE Transactions on Emerging Topics in Computational Intelligence PP (2024): 1-13.
• Faizanuddin Ansari, Agnish Bhattacharya, Biswajit Saha and Swagtam Das, "Mo2E: Mixture of Two Experts for Class-Imbalanced Learning from Medical Images," 2024 IEEE International Symposium on Biomedical Imaging (ISBI), Athens, Greece, 2024, pp. 1-5.
• Shounak Datta, Sankha Subhra Mullick, Anish Chakrabarty and Swagatam Das. "Interval bound interpolation for few-shot learning with few tasks." International Conference on Machine Learning (ICML) 2023; (pp. 7141-7166) PMLR.
• Anish Chakrabarty and Swagatam Das. On translation and reconstruction guarantees of the cycle-consistent generative adversarial networks. In Proceedings of the 36th International Conference on Neural Information Processing Systems (NIPS '22).
• Abhishek Kumar, Ajani S. Oladayo, Swagatam Das, Rammohan Mallipeddi, GridShift: A Faster Mode-seeking Algorithm for Image Segmentation and Object Tracking, IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR) 2022, Accepted for Oral Presentation, June 19 - 24, 2022, New Orleans, USA.
• Anish Chakrabarty and Swagatam Das, Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistency, NeurIPS 2021 (Thirty-fifth Conference on Neural Information Processing Systems), Spotlight Acceptance.
• Debolina Paul, Saptarshi Chakraborty, Swagatam Das, Jason Xu, Uniform Concentration Bounds toward a Unified Framework for Robust Clustering, NeurIPS 2021 (Thirty-fifth Conference on Neural Information Processing Systems), Spotlight Acceptance.
• Saptarshi Chakraborty, Debolina Paul and Swagatam Das, Automated clustering of highdimensional data with a feature weighted mean shift algorithm, AAAI 2021 (35th AAAI Conference on Artificial Intelligence), pp. 6930-6938, February 2 - 9, 2021.
• Saptarshi Chakraborty, Debolina Paul and Swagatam Das, t-Entropy: a new measure of uncertainty with some applications, IEEE International Symposium on Information Theory (ISIT 2021), 12-20 July 2021, Melbourne, Victoria, Australia.
• Saptarshi Chakraborty, Debolina Paul, Swagatam Das, and Jason Xu, "Entropy regularized power k-means clustering", AISTATS 2020 (The 23rd International Conference on Artificial Intelligence and Statistics), Accepted 2020.
• Sankha Subhra Mullick, Shounak Datta, Swagatam Das. Generative Adversarial Minority Oversampling,International Conference on Computer Vision (ICCV 2019), 2019. (arxiv link: 1903.09730).
• Debolina Paul, Saptarshi Chakraborty, Swagatam Das, and Ivan Zelinka. On the non-convergence of differential evolution: some generalized adversarialconditions and a remedy. In Proceedings of the Genetic and Evolutionary Computation Conference Companion (GECCO '19), 2019, DOI:10.1145/3319619.3322007.
• Sankha Subhra Mullick, Shounak Datta, Sourish Gunesh Dhekane and Swagatam Das, "Appropriateness of performance indices for imbalanced data classification: an analysis", Pattern Recognition, Elsevier, DOI: 10.1016/j.patcog.2020.107197, Accepted, 2020.
• Arka Ghosh, Swagatam Das, Asit Kr. Das, and Liang Gao, "Reusing the past difference vectors in differential evolution - a simple but significant improvement", IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2019.2921602, Accepted 2019.
• Avisek Gupta, Shounak Datta and Swagatam Das, "Fuzzy clustering to identify clusters at different levels of fuzziness: an evolutionary multiobjective optimization approach", IEEE Transactions on Cybernetics, DOI: 10.1109/TCYB.2019.2907002, Accepted 2019.
• Saptarshi Chakraborty and Swagatam Das, "On the strong consistency of feature weighted k-means clustering in a nearmetric space", STAT, Wiley, DOI: 10.1002/sta4.227, Accepted 2019.
• Shounak Datta, Sayak Nag and Swagatam Das, "Boosting with Lexicographic Programming: Addressing Class Imbalance without Cost Tuning", IEEE Transactions on Knowledge and Data Engineering, DOI: 10.1109/TKDE.2019.2894148, Accepted, 2019.

Our Members

Faculty Member

Senior Research Fellows

Junior Research Follows

Alumni & Adjunct members

Shounak Datta
Senior Research Engineer, ARM Inc., Austin TX, USA

Sankha Subhra Mullick
Senior AI Reseracher, Dolby Laboratories, India

Avisek Gupta
PostDoctoral Fellow, TCG-CREST, India

Abhishek Kumar
Visiting Professor VSB - Technical University of Ostrava

Arkajyoti Saha
Postdoctoral researcher, Statistics, University of Washington

Saptarshi Chakraborty
PhD Candidate, Department of Statistics, University of California, Berkeley

Debolina Paul
PhD Candidate, Department of Statistics, Standford University

Imon Banerjee
PhD Candidate, Purdue Department of Statistics.

Sayantan Kumar
PhD Candidate, Deaprtment of Computer Science & Engineering, Washington University in St. Louis.

Arka Ghosh
Assistant Professor, Bennett University, India

© reserved to MLRG, ECSU, ISI (2020 – 2025).
Developed by, Sankha Subhra Mullick, over Hydeout project, on 29th of January, 2020.
Updated on February 03, 2025; 05:05 (+0000 UTC)