Shankho Subhra Pal | Computer Science | Cross-Disciplinary Innovation Award

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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]

References

  1. Elsevier. (n.d.). Scopus author details: Shankho Subhra Pal, Author ID 57212574678. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57212574678
  2. 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
  3. ACM. (2025). Revisiting Multi-Agent GAN for Multimodal Time Series Generation in Human Sensing and mHealth Applications.
    https://doi.org/10.1145/3714394.3756189
  4. Pattern Recognition Letters. (2024). Finding Hierarchy of Clusters.
    https://doi.org/10.1016/j.patrec.2023.12.009
  5. ACM. (2023). Fine-grain Cluster Estimation of Land Cover Classes using Landsat 8 Multispectral Images.
    https://doi.org/10.1145/3627631.3627643
  6. IEEE. (2023). Time Series Prediction of Multi-Spectral Satellite Images and Its Application for Cloud Removal.
    https://doi.org/10.1109/INGARSS59135.2023.10490400

Hsin Yuan Chen | Engineering | Best Scholar Award

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Prof. Hsin Yuan Chen | Engineering | Best Scholar Award

Professor at Zhejiang University | China

Dr. Hsin Yuan Chen is a leading scholar and technologist, currently serving as a Changjiang Scholar Professor and Director at Zhejiang University’s Institute of Wenzhou, Center of Digital Technology Entrepreneurship and Innovation. With an extensive academic and industrial background, she has made significant contributions in smart agriculture, AI, robotics, and digital transformation. Dr. Chen’s interdisciplinary expertise bridges engineering, healthcare, and artificial intelligence, and her work has impacted education, industry collaboration, and technological advancement across Asia. Her recognition includes international fellowships, keynote speaker roles, and leadership in major research centers, positioning her as a dynamic force in intelligent systems and innovation.

Profile:

Google Scholar

Education:

Dr. Hsin Yuan Chen earned her Bachelor’s and Ph.D. degrees in Aerospace Engineering from National Cheng Kung University, Taiwan, completing her doctorate in 2000. She complemented her formal education with a visiting professorship at Washington University in St. Louis, USA, which deepened her global academic perspective. Her educational journey has been distinguished by a strong foundation in systems control, aerospace, and robotics, which later evolved to encompass AI, digital agriculture, and interdisciplinary technology management. This robust academic training underpins her approach to integrating theoretical insights with practical innovations in smart technologies and data-driven platforms.

Experience:

Dr. Hsin Yuan Chen’s professional journey spans over two decades of academic, governmental, and industrial roles. She served as Professor and Dean at Fujian Normal University, CTO at GEOSAT Technology and Mobiletron Electronics, and Assistant Professor at multiple Taiwanese institutions. Additionally, she held advisory roles in patent offices and high-tech companies, contributing to projects on AI positioning systems, smart agriculture, and unmanned vehicles. Her international engagements include collaborations with institutions such as McGill University and Washington University. These diverse experiences enrich her ability to lead transdisciplinary teams and execute complex, innovation-focused initiatives across multiple sectors.

Research Interest:

Dr. Hsin Yuan Chen’s research focuses on the convergence of artificial intelligence, smart agriculture, IoT, blockchain, and autonomous systems. Her projects have addressed real-world challenges in digital transformation, healthcare innovation, and sustainable agriculture. A particular interest lies in integrating explainable AI with blockchain to enhance decision-making in agricultural technology. She is also actively involved in robotics, wireless positioning systems, and medical platforms leveraging sensor technology. Her passion for developing inclusive, intelligent systems is reflected in her projects like AI Doctors for crops and Paro Robots for health monitoring, aiming to merge emotion detection with deep learning-based automation.

Awards and Honors:

Dr. Hsin Yuan Chen has received prestigious accolades including the ScienceFather International Outstanding Scientist Award (2024), IET Fellowship (2023), and ASEAN Fellowship (2022). She was recognized with national teaching excellence awards, innovation medals in higher education, and championship titles in robotics competitions. Her pioneering work has also earned distinctions in cloud technology and virtual cultural heritage. As a member of high-level talent programs in Zhejiang and Fujian Provinces, and a recipient of multiple creativity group medals, Dr. Chen’s impact extends across education, technology, and international science forums. Her awards reflect both scholarly excellence and societal contributions.

Publications:

Title: Exploring the sensitivity of next generation gravitational wave detectors

Citations: 1533

Year of Publication: 2017

Title: Cosmology intertwined: A review of the particle physics, astrophysics, and cosmology associated with the cosmological tensions and anomalies

Citations: 1322

Year of Publication: 2022

Title: Carbon nanotube computer

Citations: 1228

Year of Publication: 2013

Title: Three dimensional reconstruction of a solid-oxide fuel-cell anode

Citations: 1019

Year of Publication: 2006

Title: GPR55 is a cannabinoid receptor that increases intracellular calcium and inhibits M current

Citations: 895

Year of Publication: 2008

Title: Plasmonic nanolaser using epitaxially grown silver film

Citations: 878

Year of Publication: 2012

Title: Translation and back‐translation in qualitative nursing research: methodological review

Citations: 874

Year of Publication: 2010

Title: Mapping the Evolution: A Bibliometric Analysis of Employee Engagement and Performance in the Age of AI-Based Solutions
Year of Publication: 2025

Title: Advancements in Handwritten Devanagari Character Recognition: A Study on Transfer Learning and VGG16 Algorithm
Citations: 3
Year of Publication: 2024

Title: Intellectual Structure of Explainable Artificial Intelligence: A Bibliometric Reference to Research Constituents
Year of Publication: 2024

Title: Integrating Explainable Artificial Intelligence and Blockchain to Smart Agriculture: Research Prospects for Decision Making and Improved Security
Citations: 39
Year of Publication: 2023

Conclusion:

Dr. Hsin Yuan Chen exemplifies excellence in research, leadership, and innovation, making her a strong candidate for the Best Researcher Award. Her prolific output in scientific publications, transformative projects in smart agriculture and digital health, and her commitment to knowledge transfer through academia-industry collaborations illustrate her deep impact. Dr. Chen’s fusion of AI with real-world applications—particularly in sustainable systems and intelligent automation—positions her at the forefront of global innovation. Her recognition across international platforms affirms her thought leadership and the lasting value of her contributions to science, technology, and education.

Xingjia Li | Robotics | Best Researcher Award

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Dr. Xingjia Li | Robotics | Best Researcher Award

Senior Engineer at Shanghai Liangxin Electrical Co., Ltd., China.

Dr. Xingjia Li 🎓 is a dynamic early-career researcher specializing in robotics 🤖 and electrical systems ⚡. He earned his Ph.D. in Mechanical Engineering from Jiangsu University, China 🇨🇳, in 2023. Currently, he is a Postdoctoral Associate at Shanghai Liangxin Electrical Co., Ltd. 🏢, where he explores advanced sensor data processing integrated with machine learning 🤖📊. His work is focused on creating intelligent and secure systems for human-centered applications 🧠🔒. Passionate about innovation and industrial collaboration, Dr. Li aims to bridge the gap between research and real-world impact 🌐💼.

Professional Profile:

ORCID

Suitability for Best Researcher Award – Dr. Xingjia Li

Dr. Xingjia Li is a highly promising early-career researcher whose work stands at the intersection of robotics, electrical systems, and machine learning. While he is still in the early stages of his academic journey, his innovative research direction, strong industrial collaboration, and interdisciplinary approach make him a strong contender for the Best Researcher Award in the early-career or emerging researcher category. His focus on human-centered intelligent systems showcases a commitment to solving real-world problems using advanced technologies, a quality that aligns perfectly with the spirit of this award.

🔹 Education & Experience

🎓 Ph.D. in Mechanical Engineering – Jiangsu University, Zhenjiang, China (2023)
🧪 Postdoctoral Associate – Shanghai Liangxin Electrical Co., Ltd., Postdoctoral Workstation, Shanghai, China (2023–Present)
🔬 Research Interests – Robotics 🤖, Electrical Systems ⚡, Sensor Data Processing with Machine Learning 📈
🔗 Application Areas – Human-centered Systems 🧍‍♂️🛡️, Automation 🤝, Smart Devices 📲

🔹 Professional Development

Dr. Li is committed to continuous professional growth through industrial collaboration and advanced research 🤝🧪. At Shanghai Liangxin Electrical Co., Ltd., he participates in practical projects focused on secure and intelligent automation systems 🔒🤖. He actively engages in interdisciplinary learning, integrating machine learning, AI, and electrical systems to enhance innovation 📚💡. His exposure to both academic and industrial environments enables him to develop real-world applications that solve current technological challenges 🌍🛠️. By staying updated through research networks, technical seminars, and collaboration, Dr. Li positions himself as a forward-thinking researcher 📖🌐.

🔹 Research Focus Category 

Dr. Xingjia Li’s research falls under AI-driven robotics and smart electrical systems 🤖⚡. He specializes in applying machine learning techniques to process sensor data 📊🧠, enabling systems to become more intelligent, secure, and adaptive to human needs. His focus includes cyber-physical systems, human-machine interfaces, and automation technologies 💻🔧. These technologies have broad applications in healthcare, industrial automation, and smart homes 🏥🏭🏠. Dr. Li’s interdisciplinary approach combines mechanical engineering, computer science, and electrical design to create next-generation human-centric innovations 🌐🤝.

🔹 Awards & Honors

As a recent Ph.D. graduate and emerging researcher, Dr. Xingjia Li 🎓 is in the early stages of building his academic and professional recognition profile. While there are currently no publicly documented awards or honors 🏅, his active involvement in cutting-edge research projects and collaboration with industry through Shanghai Liangxin Electrical Co., Ltd. 🏢 positions him well for future accolades. With continued publication of impactful research, participation in international conferences 🌐, and contributions to innovation in robotics and machine learning 🤖📊, Dr. Li is poised to earn distinctions such as best paper awards, patents, or young researcher honors in the near future 🌟📈.

Publication Top Notes

1. Optimization of Piezoelectric Energy Harvester Using Equilibrium Optimizer Algorithm

  • Conference: 16th Symposium on Piezoelectricity, Acoustic Waves, and Device Applications (SPAWDA)

  • Date: October 11, 2022

  • DOI: 10.1109/spawda56268.2022.10046019

  • Contributors: Jian Sun, X. J. Li, J. N. Gu, M. L. Pu, H. Chen

  • Summary: This paper presents a novel approach to enhance the efficiency of piezoelectric energy harvesters by applying the Equilibrium Optimizer algorithm, a nature-inspired metaheuristic, for optimal parameter tuning. The method improves energy output and system stability.

2. Tuning ANFIS Using a Simplified Sparrow Search Algorithm

  • Journal: Advances in Transdisciplinary Engineering

  • Date: February 10, 2022

  • DOI: 10.3233/ATDE220091

  • Contributor: Xingjia Li

  • Summary: This study applies a simplified version of the Sparrow Search Algorithm to optimize the parameters of the Adaptive Neuro-Fuzzy Inference System (ANFIS), enhancing its performance in complex engineering problems.

3. A Numerical Approach for Flexoelectric Energy Harvester Modeling Using COMSOL Multiphysics

  • Conference: 15th Symposium on Piezoelectricity, Acoustic Waves and Device Applications (SPAWDA)

  • Date: June 4, 2021

  • DOI: 10.1109/spawda51471.2021.9445427

  • Contributor: Xingjia Li

  • Summary: This paper proposes a numerical model of a flexoelectric energy harvester using COMSOL Multiphysics, addressing the coupling of mechanical and electrical domains to predict device performance accurately.

4. A Fusion Parameter Method for Classifying Freshness of Fish Based on Electrochemical Impedance Spectroscopy

  • Journal: Journal of Food Quality

  • Date: March 10, 2021

  • DOI: 10.1155/2021/6664291

  • Contributors: Jian Sun, Yuhao Liu, Gangshan Wu, Yecheng Zhang, Rongbiao Zhang, X. J. Li, Daniel Cozzolino

  • Summary: The research introduces a fusion parameter technique combining electrochemical impedance spectroscopy data to accurately classify fish freshness, demonstrating potential for food quality control applications.

5. Research on the Actuation Performance of 2D-Orthotropic Piezoelectric Composite Materials Linear Phased Array Transducer

  • Journal: Journal of Nanoscience and Nanotechnology

  • Date: August 1, 2019

  • DOI: 10.1166/jnn.2019.16814

  • Contributor: Xingjia Li

  • Summary: This article investigates the actuation performance of a 2D-orthotropic piezoelectric composite used in linear phased array transducers, highlighting the material’s anisotropic effects on acoustic wave propagation.

6. Design and Optimization for Double-Sided Interdigital Transducer with Piezoelectric Substrate

  • Conference: 13th Symposium on Piezoelectricity, Acoustic Waves and Device Applications (SPAWDA)

  • Date: January 11, 2019

  • Contributor: Xingjia Li

  • Summary: The paper focuses on the design and optimization of double-sided interdigital transducers on piezoelectric substrates to improve device efficiency and sensitivity for acoustic applications.

.

Conclusion

While Dr. Li is at the beginning of his research career, his exceptional potential, innovation-driven mindset, and strong research focus make him a suitable candidate for the Best Researcher Award (Emerging Researcher Category). His contributions already demonstrate the capacity to shape the future of robotics and intelligent systems. With continued research output and growing industrial impact, Dr. Li is on a clear path to becoming a leading figure in his field.