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Email
rajendra@gri.msstate.edu

Office
HPC

11/C, 2 Research Blvd.
Mississippi State University
MS 39759, USA


Rajendra Mohan Panda  
Rajendra Mohan
Panda
Geosystems Research Institute
Postdoctoral Associate

View Curriculum Vitae

Email
rajendra@gri.msstate.edu

Office
HPC

11/C, 2 Research Blvd.
Mississippi State University
MS 39759, USA
Biography
Dr. Rajendra Mohan Panda is a postdoctoral researcher at Mississippi State University with more than a decade of research experience in the fields of agriculture, ecology, forestry, remote sensing and soil science. His broad research interests is interdisciplinary including gradient pattern analyses, climate change predictions, diversity and distribution studies, invasion risk assessments, species distribution modeling, and spatial data analyses and interpretation. He has proficiency in large-data analytics, machine learning applications, modeling, interactive mapping and visualization, R-Shiny App development.

He has more than twenty peer-reviewed publications, and contributions to six GitHub repositories, two Shiny Apps and two news articles those can be accessed at:

Google Scholar
Research Gate
GitHub Repositories
R Shiny Apps
News Article1
News Article2

Education:

Ph.D., Indian Institute of Technology Kharagpur, School of Water Resources, 2018, Thesis: Environmental determinants of plant richness in Indian Himalaya.

Research Positions:

  • Postdoctoral Associate, Mississippi State University, Geosystems Research Institute, 2021-present

  • Postdoctoral Associate, University of Georgia, Dept of Crop and Soil Sciences, 2020-2021

  • Research Associate, Indian Institute of Technology Kharagpur, Centre for Oceans, Rivers, Atmosphere and Land Sciences (CORAL), 2019–2020

  • Sr. Project Officer, Indian Institute of Technology Kharagpur, Centre for Oceans, Rivers, Atmosphere and Land Sciences (CORAL), 2018– 2019

  • Graduate Research and Teaching Assistant, Indian Institute of Technology Kharagpur, School of Water Resources, 2017–2018

  • Graduate Research Fellow, Indian Institute of Technology Kharagpur, School of Water Resources, 2014–2017

  • Sr. Research Fellow, Indian Institute of Technology Kharagpur, School of Water Resources, 2012–2014

Research Experience:

Soil Sciences - Estimation, classification, and mapping soil moisture and soil temperature; R-package for interactive mapping and visualization of soil temperature

Plant Sciences - Diversity and distribution studies; species-environment interactions; gradient pattern analysis; spatial analyses; climate change predictions; invasion risk assessments; species distribution modeling; global dynamic vegetation modeling; ecological monitoring and assessment

Animal Sciences - Foraging behavior; movement ecology, population dynamics, human interactions, conservation and management of blackbucks (Antilope cervicapra); foraging behavior of Hanuman langur (Presbytes entellus); diversity and distribution studies, spatial modeling of bats (chiropterans)

Technical Skills:

  • Large-Data Analytics

  • Machine Learning Applications

  • Species Distribution Modeling

  • Spatial Data Analyses and Interpretation

  • Development of R-Shiny Apps

  • Interactive Mapping and Visualization


Software Skills:

  • R

  • ArcGIS

  • QGIS

  • MATLAB

  • Python(basics)

Research Interest

  • Interdisciplinary Research

  • Plant-animal interactions

  • Human impacts on ecosystems

  • Precision agriculture

  • Machine Learning Applications

  • Spatial Data Analyses

  • Climate Change Modeling

  • R-Shiny Web Applications

  • High-performance Computing

Hobbies
Soft music; Short stories; Comedy movies
Selected Publications Total Publications:  2 
Panda, R. M., Prince Czarnecki, J. M., Hu, J., & Keri, J. (2022). Data Mining Soil Testing Laboratory Datasets to Characterize Trends in Soil Fertility. ASA, CSSA, SSSA International Annual Meeting. Baltimore, MD. [Document Site]

Ramamoorthy, P., Samiappan, S., Wubben, M. J., Brooks, J. P., Panda, R. M., Reddy, R., & Bheemanahalli, R. (2022). Hyperspectral Reflectance and Machine Learning Approaches for the Detection of Drought and Root Knot Nematode Infestation in Cotton. Remote Sensing. 14, 4021. DOI:https://doi.org/10.3390/rs14164021. [Abstract]