A multi-level multi-scale approach to study essential genes in Mycobacterium tuberculosis. - Soma Ghosh; Priyanka Baloni; Sumanta Mukherjee; Praveen Anand; Nagasuma R. Chandra (BMC Syst. Biol., 2013)

Current trends in modeling host-pathogen interactions. - Sumanta Mukherjee; Awanti Sambarey; Karyala Prashanthi; Nagasuma R. Chandra (WIREs Data Mining Knowl. Discov., 2013)

PLIC: protein-ligand interaction clusters. - Praveen Anand; Deepesh Nagarajan; Sumanta Mukherjee; Nagasuma R. Chandra (Database J. Biol. Databases Curation, 2014)

HLaffy: estimating peptide affinities for Class-1 HLA molecules by learning position-specific pair potentials. - Sumanta Mukherjee; Chiranjib Bhattacharyya; Nagasuma Chandra (Bioinform., 2016)

Role of genetic heterogeneity in determining the epidemiological severity of H1N1 influenza. - Narmada Sambaturu; Sumanta Mukherjee; Martín López-García; Carmen Molina-París; Gautam I. Menon; Nagasuma Chandra (PLoS Comput. Biol., 2018)

Semi-Supervised Method using Gaussian Random Fields for Boilerplate Removal in Web Browsers. - Joy Bose; Sumanta Mukherjee (CoRR, 2019)

Attention based Multi-Modal New Product Sales Time-series Forecasting. - Vijay Ekambaram; Kushagra Manglik; Sumanta Mukherjee; Surya Shravan Kumar Sajja; Satyam Dwivedi; Vikas Raykar (KDD, 2020)

Robust resource demand estimation using hierarchical Bayesian model in a distributed service system. - Sumanta Mukherjee; Krishnasuri Narayanam; Nupur Aggarwal; Digbalay Bose; Amith Singhee (COMAD/CODS, 2021)

Explainable AI based Interventions for Pre-season Decision Making in Fashion Retail. - Shravan Sajja; Nupur Aggarwal; Sumanta Mukherjee; Kushagra Manglik; Satyam Dwivedi; Vikas Raykar (COMAD/CODS, 2021)

Intersection Patterns in Optimal Binary (5, 3) Doubling Subspace Codes. - Anirban Ghatak; Sumanta Mukherjee (CoRR, 2021)

Physics-based multiple time-series univariate forecasting. - Sumanta Mukherjee; Arindam Jati; Kanthi K. Sarpatwar; Roman Vaculín (IEEE Big Data, 2022)

Hierarchy-guided Model Selection for Time Series Forecasting. - Arindam Jati; Vijay Ekambaram; Shaonli Pal; Brian Quanz; Wesley M. Gifford; Pavithra Harsha; Stuart Siegel; Sumanta Mukherjee; Chandra Narayanaswami (CoRR, 2022)

AI Explainability 360 Toolkit for Time-Series and Industrial Use Cases. - Giridhar Ganapavarapu; Sumanta Mukherjee; Natalia Martinez Gil; Kanthi K. Sarpatwar; Amaresh Rajasekharan; Amit Dhurandhar; Vijay Arya; Roman Vaculín (KDD, 2023)

Hierarchical Proxy Modeling for Improved HPO in Time Series Forecasting. - Arindam Jati; Vijay Ekambaram; Shaonli Pal; Brian Quanz; Wesley M. Gifford; Pavithra Harsha; Stuart Siegel; Sumanta Mukherjee; Chandra Narayanaswami (KDD, 2023)

TsSHAP: Robust model agnostic feature-based explainability for time series forecasting. - Vikas C. Raykar; Arindam Jati; Sumanta Mukherjee; Nupur Aggarwal; Kanthi K. Sarpatwar; Giridhar Ganapavarapu; Roman Vaculín (CoRR, 2023)

Semi-supervised counterfactual explanations. - Surya Shravan Kumar Sajja; Sumanta Mukherjee; Satyam Dwivedi (CoRR, 2023)

Tiny Time Mixers (TTMs): Fast Pre-trained Models for Enhanced Zero/Few-Shot Forecasting of Multivariate Time Series. - Vijay Ekambaram; Arindam Jati; Pankaj Dayama 001; Sumanta Mukherjee; Nam Nguyen; Wesley M. Gifford; Chandra Reddy; Jayant Kalagnanam (NeurIPS, 2024)

Activations Through Extensions: A Framework To Boost Performance Of Neural Networks. - Chandramouli Kamanchi; Sumanta Mukherjee; Kameshwaran Sampath; Pankaj Dayama 001; Arindam Jati; Vijay Ekambaram; Dzung Phan (CoRR, 2024)

Towards Unbiased Evaluation of Time-series Anomaly Detector. - Debarpan Bhattacharya; Sumanta Mukherjee; Chandramouli Kamanchi; Vijay Ekambaram; Arindam Jati; Pankaj Dayama 001 (ICASSP, 2025)

TSPulse: Dual Space Tiny Pre-Trained Models for Rapid Time-Series Analysis. - Vijay Ekambaram; Subodh Kumar; Arindam Jati; Sumanta Mukherjee; Tomoya Sakai; Pankaj Dayama 001; Wesley M. Gifford; Jayant Kalagnanam (CoRR, 2025)