Shital Saha | Best Researcher Award | Statistical Methods

Shital Saha | Best Researcher Award Winner 2023  🏆

Mr Shital Saha : Statistical Methods

Congratulations to Mr. Shital Saha, recipient of the Statistical Methods Best Researcher Award at the International Research Data Analysis Excellence Awards! Your dedication to advancing statistical methods is truly commendable, and this recognition is a testament to your outstanding contributions. Well done!

Professional profile:

Early Academic Pursuits:

I embarked on my academic journey in Patiram High School, West Bengal, excelling in subjects like Bengali, English, Mathematics, Physics, Chemistry, History, and Geography. This laid the foundation for my future educational endeavors.

In 2013, I pursued a Bachelor's degree from Balurghat College, specializing in Mathematics (Hons.), Physics, and Chemistry. Subsequently, I obtained a Master's degree in Mathematics from the University of Gour Banga in 2015, showcasing my commitment to the field.

Professional Endeavors:

Driven by a passion for education, I earned a B.Ed. degree in 2017 from the University of Gour Banga, specializing in Mathematics. This equipped me with the skills needed to contribute meaningfully to the field of education.

Currently, I am pursuing a Ph.D. in Statistics with a focus on Statistical Information Theory at the National Institute of Technology Rourkela, under the guidance of Prof. Suchandan Kayal.

Contributions and Research Focus:

My research, centered on Statistical Information Theory, explores diverse aspects. Notable publications include works on extended fractional cumulative past, paired φ-entropy measures, and weighted varentropy. Furthermore, my ongoing studies delve into informational characteristics of cubic transmuted distributions and general weighted information generating functions.

As a referee for Contemporary Mathematics, I actively engage in the scholarly community, contributing to the peer-review process.

Accolades and Recognition:

My academic achievements include qualifying JAM-2013 (AIR-658), CSIR-UGC NET (JRF) Dec. 2019 (AIR-221), and WBSET-2020. Additionally, I successfully cleared CTET (Paper I & II) in December 2019.

Impact and Influence:

My research, as evidenced by published and under-review papers, contributes valuable insights to the fields of physics and applied mathematics. Attendance at workshops and seminars underscores my commitment to staying abreast of emerging trends in statistical research.

Legacy and Future Contributions:

I am dedicated to leaving a lasting impact through my contributions to academia. My ongoing Ph.D. research and engagement as a referee indicate a commitment to the advancement of statistical knowledge.

In the future, I aspire to further enrich the field through continued research, teaching, and collaboration with peers and scholars globally. My legacy aims to inspire future generations in the pursuit of knowledge and excellence.

Notable publication :

Citations:

A total of  83 citations for his publications, demonstrating the impact and recognition of his research within the academic community.

International Research Data Analysis Excellence Awards | Award Winners 2023

Raj kumar Singh

Congratulations, Raj kuma Singh |  Best Researcher  Award  Winner 2023 🏆

Raj kumar Singh : Satellite remote sensing, Machine learning

Raj Kumar Singh's groundbreaking work in the realms of satellite remote sensing and machine learning has earned him the esteemed Young Scientist Award at the International Research Data Analysis Excellence Awards. His innovative approach to leveraging satellite data and cutting-edge machine learning algorithms has not only advanced scientific understanding but has also showcased his commitment to pushing the boundaries of research.This accolade serves as a testament to Singh's dedication, talent, and exceptional capabilities in utilizing technology to unravel complex insights from vast datasets.

Professional profile:

Early Academic Pursuits:

Raj Kumar Singh began his academic journey with a Master of Science (M.Sc.) in Remote Sensing and GIS from Jiwaji University, Gwalior, India, in 2009. His thesis focused on hyperspectral remote sensing-based studies for the extraction of vegetation quality parameters using Hyperion data. This laid the foundation for his future specialization in geoinformatics.

He continued his education with a Master of Technology (M.Tech.) in Remote Sensing and GIS with a specialization in Atmospheric Science from the Indian Institute of Remote Sensing (IIRS-ISRO), Dehradun, India, in 2015. His thesis, titled "Spatial-temporal variability of Aerosol Optical properties and their impact on Aerosol Radiative forcing over Indo-Gangetic plain," showcased his commitment to understanding atmospheric dynamics through remote sensing.

Raj Kumar Singh completed his academic journey with a Doctor of Philosophy (Ph.D.) in Geoinformatics and Agroforestry from the Indian Institute of Technology Kharagpur (IIT-KGP) in 2023. His doctoral thesis, "Agroforestry Interventions for Improved Crop Management Using Geospatial Technology," demonstrated his expertise in applying geospatial techniques to agriculture.

Professional Endeavors:

Raj Kumar Singh has accumulated 14 years of professional experience, primarily focusing on satellite remote sensing and its applications in agriculture and agroforestry. He has worked with various national and international organizations, including ISRO, JNU, ICARDA, and currently leads geospatial activities at World Agroforestry (CIFOR-ICRAF) in New Delhi.

His roles include being a Geoinformatics Scientist at World Agroforestry, ICARF, where he leads and plans geoinformatics activities, contributes to technical exchanges, project reports, and proposals, and develops advanced remote sensing and geoinformatics applications for agroforestry. He has also led the development of a mobile app for site-specific agroforestry suitability.

Raj Kumar Singh served as a Remote Sensing Specialist at the International Centre for Agricultural Research in the Dry Areas (ICARDA) in New Delhi, where he characterized rice fallows, assessed long-term trends of crop fallows, and developed pulse crop suitability maps using advanced decision-making techniques.

He has also worked as a Research Fellow at Jawaharlal Nehru University (JNU) in New Delhi, focusing on geomorphological and geological mapping, and as a Junior Research Fellow at the Regional Remote Sensing Centre (RRSC)-East, Indian Space Research Organization (ISRO), Kolkata, where he mapped tea growing areas and conducted detailed site suitability analysis for tea plantations.

Additionally, Raj Kumar Singh served as a GIS Engineer at Magus Infocom Pvt. Ltd. in Noida, Delhi-NCR, contributing to spatial database creation for urban infrastructure using satellite and ground-based data.

Contributions and Research Focus:

Raj Kumar Singh's research encompasses biomass estimation, species distribution, and ecological modeling using geospatial data analytics, machine learning, and image processing software. He has hands-on experience with hyperspectral and SAR data, leading teams in the development of tools and applications for agroforestry suitability and carbon estimation.

His expertise extends to cloud-based platforms such as Google Earth Engine (GEE) and programming languages like Python and R. He has actively contributed to the development of geospatial decision layers, mobile applications, and dashboards for real-time monitoring and analysis.

Raj Kumar Singh has been actively involved in projects with organizations like USAID, NRSC, ISRO, ICAR, Tea Board, and Survey of India, showcasing his versatility in addressing diverse geospatial challenges.

Accolades and Recognition:

Raj Kumar Singh has received recognition for his work through memberships in professional bodies such as the Indian Society of Remote Sensing (ISRS) and the Indian Society of Agroforestry. He serves as a reviewer for reputable journals in the field, including the Journal of Environment and Management, Environmental Monitoring and Assessment, Remote Sensing, and Tropical Ecology.

His commitment to advancing knowledge is evident through his participation in numerous professional training programs, including those focused on Google Earth Engine, Web GIS Technology, Earth Observation for Carbon Cycle Studies, and Machine Learning.

Impact and Influence:

Raj Kumar Singh's work has had a significant impact on the field of geoinformatics, particularly in the application of remote sensing to agriculture and agroforestry. His research outputs, including over 15 peer-reviewed publications and presentations at national and international conferences, contribute to the body of knowledge in the domain.

As a leader at World Agroforestry, he has influenced the development of innovative tools, platforms, and applications for site-specific agroforestry interventions and biomass estimation. His work on mobile apps and decision support tools has facilitated practical applications of geospatial technology in the field.

Legacy and Future Contributions:

Raj Kumar Singh's legacy lies in his comprehensive contributions to the integration of geoinformatics into agricultural and agroforestry practices. His focus on sustainable solutions, capacity building through training, and active participation in collaborative projects positions him as a thought leader in the geospatial community.

Looking forward, Raj Kumar Singh is likely to continue shaping the future of geoinformatics by leveraging emerging technologies, such as machine learning and cloud-based platforms. His commitment to research, training, and the practical application of geospatial solutions ensures a lasting impact on the field.

Notable publication:

Decentralized proximal gradient algorithms with linear convergence rates

A linearly convergent proximal gradient algorithm for decentralized optimization

Linear convergence of primal–dual gradient methods and their performance in distributed optimization

On the influence of bias-correction on distributed stochastic optimization

Distributed coupled multiagent stochastic optimization

A proximal diffusion strategy for multiagent optimization with sparse affine constraints

A unified and refined convergence analysis for non-convex decentralized learning

Removing data heterogeneity influence enhances network topology dependence of decentralized sgd

Citation:

A total of 447  citations for his publications, demonstrating the impact and recognition of his research within the academic community.

Citations              447

h-index  10           10

i10-index             10