Cross-Disciplinary Innovation Award
Shankho Subhra Pal
Indian Institute of Technology Kharagpur, India
| Shankho Subhra Pal |
| Affiliation |
Indian Institute of Technology Kharagpur |
| Country |
India |
| Scopus ID |
57212574678 |
| Documents |
10 |
| Citations |
20 |
| h-index |
2 |
| Subject Area |
Computer Science |
| Event |
International Phenomenological Research Awards |
| ORCID |
0000-0003-1036-3166 |
The Cross-Disciplinary Innovation Award recognizes scholarly contributions that integrate computational intelligence, data science, remote sensing, machine learning, and interdisciplinary technological applications. Shankho Subhra Pal of the Indian Institute of Technology Kharagpur has developed research spanning time-series prediction, satellite image analysis, clustering methodologies, multimodal sensing, and artificial intelligence applications. His published works demonstrate the application of advanced computational techniques to real-world environmental and sensing challenges, contributing to contemporary developments in computer science and data-driven decision-making.[1]
Abstract
This article reviews the academic profile and research accomplishments of Shankho Subhra Pal. His work focuses on machine learning, remote sensing, image prediction, clustering analysis, and multimodal sensing systems. Through interdisciplinary integration of artificial intelligence and geospatial technologies, his studies address challenges in cloud removal, land-cover analysis, human sensing, and synthetic data generation. These contributions illustrate emerging intersections between computer science and applied environmental analytics.[2]
Keywords
Artificial Intelligence, Machine Learning, Remote Sensing, Satellite Imagery, Time-Series Prediction, Clustering Analysis, Multimodal Data, Human Sensing, Computer Science.
Introduction
Contemporary research increasingly depends on cross-disciplinary approaches capable of integrating computational methodologies with practical applications. Pal’s research portfolio reflects this trend through the application of machine learning and pattern recognition techniques to environmental monitoring, sensing systems, and geospatial intelligence. His work contributes to methodological development while supporting applied research objectives.[3]
Research Profile
According to available scholarly records, the researcher has authored ten indexed publications and received twenty citations, resulting in an h-index of two. His primary specialization lies within Computer Science, with notable engagement in artificial intelligence, pattern recognition, remote sensing, and predictive analytics.[1]
Research Contributions
- Development of self-supervised learning frameworks for multispectral image prediction and cloud-removal applications.
- Research on multimodal time-series generation using Multi-Agent GAN architectures for sensing and mHealth environments.
- Advancement of hierarchical clustering methodologies for pattern recognition and data organization.
- Fine-grained estimation of land-cover classes using Landsat 8 multispectral imagery.
Publications
- Time Series Prediction of Multi-Spectral Images Using Self-Supervised Learning and Its Applications in Cloud Removal and Land Use Analysis.
- Revisiting Multi-Agent GAN for Multimodal Time Series Generation in Human Sensing and mHealth Applications.
- Finding Hierarchy of Clusters.
- Fine-grain Cluster Estimation of Land Cover Classes Using Landsat 8 Multispectral Images.
Research Impact
The research portfolio demonstrates an emphasis on practical artificial intelligence applications with relevance to environmental analytics, sensing technologies, and predictive modeling. By combining computer science methodologies with geospatial and healthcare-oriented datasets, the work contributes to broader interdisciplinary innovation and supports reproducible computational research.[4]
Award Suitability
The Cross-Disciplinary Innovation Award emphasizes integration across academic domains and the translation of advanced research into practical applications. Pal’s body of work aligns with these objectives through the convergence of artificial intelligence, remote sensing, pattern recognition, and multimodal data analytics. His contributions provide evidence of interdisciplinary engagement and methodological innovation consistent with the objectives of the International Phenomenological Research Awards.[5]
Conclusion
Shankho Subhra Pal has established a research profile centered on machine learning, remote sensing, and computational intelligence. His publications illustrate interdisciplinary problem-solving and the application of advanced analytical techniques across multiple domains. These characteristics support consideration for recognition under a cross-disciplinary innovation framework.[6]
External Links
References
- Elsevier. (n.d.). Scopus author details: Shankho Subhra Pal, Author ID 57212574678. Scopus.
https://www.scopus.com/authid/detail.uri?authorId=57212574678
- Engineering Applications of Artificial Intelligence. (2026). Time Series Prediction of Multi-Spectral Images Using Self-Supervised Learning.
https://doi.org/10.1016/j.engappai.2026.115442
- ACM. (2025). Revisiting Multi-Agent GAN for Multimodal Time Series Generation in Human Sensing and mHealth Applications.
https://doi.org/10.1145/3714394.3756189
- Pattern Recognition Letters. (2024). Finding Hierarchy of Clusters.
https://doi.org/10.1016/j.patrec.2023.12.009
- ACM. (2023). Fine-grain Cluster Estimation of Land Cover Classes using Landsat 8 Multispectral Images.
https://doi.org/10.1145/3627631.3627643
- IEEE. (2023). Time Series Prediction of Multi-Spectral Satellite Images and Its Application for Cloud Removal.
https://doi.org/10.1109/INGARSS59135.2023.10490400