DRI / DIRECTORY / SAYANTAN (MONTY) MAJUMDAR
Sayantan (Monty) Majumdar Profile Photo
Dr. Sayantan (Monty) Majumdar
Assistant Research Professor, Hydrologic Sciences and Remote Sensing

About

Dr. Sayantan (Monty) Majumdar is an Assistant Research Professor of Hydrologic Sciences and Remote Sensing at the Desert Research Institute, Reno, Nevada. He is also serving as an Adjunct Faculty as part of the University of Nevada Reno (UNR) and DRI Graduate Program of Hydrologic Sciences.

Earlier, he was a Postdoctoral Fellow at the Department of Civil and Environmental Engineering, Colorado State University.Dr. Majumdar is a computational hydrologist with expertise in geospatial data science, machine learning, remote sensing, and scientific computing. He has a strong track record of producing high-impact, cross-disciplinary research at the intersection of remote sensing, machine learning, geoinformatics, and hydrology.

Dr. Majumdar has a Ph.D. degree in Geological Engineering from Missouri S&T, USA. His doctoral research was focused on groundwater withdrawal estimation using integrated remote sensing datasets (satellite, airborne, modeled) and machine learning (deep). He is currently working on multiple projects funded by NASA, USGS, U.S. Bureau of Reclamation (USBR), National Park Service (NPS), State of Nevada/U.S. Department of the Treasury, and National Institutes of Health (NIH).

Research Areas of Interest

  • Hydrologic Remote Sensing
  • Geospatial Data Science
  • Applied Machine Learning
  • Irrigation Water Use
  • InSAR
  • Land Subsidence
  • Open-source Geospatial Scientific Software Development

Related links

Linkedin:https://www.linkedin.com/in/sayantanmajumdar/

Twitter:https://twitter.com/hydromaj

GitHub:https://github.com/montimaj

Google Scholar:https://scholar.google.com/citations?user=iYlO-VcAAAAJ&hl=en

Keywords

hydrology, remote sensing, applied machine learning, irrigation water use, geospatial data science, scientific software development, InSAR, land subsidence

 

Publications
2024
Tolan, J., Yang, H., Nosarzewski, B., Couairon, G., Vo, H. V., Brandt, J., Spore, J., Majumdar, S., Haziza, D., Vamaraju, J., Moutakanni, T., Bojanowski, P., Johns, T., White, B., Tiecke, T., Couprie, C. (2024). Very high resolution canopy height maps from RGB imagery using self-supervised vision transformer and convolutional decoder trained on aerial lidar, Remote Sensing of Environment, 300, Article No. 113888, 10.1016/j.rse.2023.113888

2023
Hasan, M. F., Smith, R., Vajedian, S., Pommerenke, R., Majumdar, S. (2023). Global land subsidence mapping reveals widespread loss of aquifer storage capacity, Nature Communications, 14 (1), Article No. 6180. 10.1038/s41467-023-41933-z

Conference Proceedings
Majumdar, S., Ott, T., Huntington, J. L., Smith, R., Fang, B., Lakshmi, V. (2023). Toward Field Scale Groundwater Withdrawals in the Western U.S. using Remote Sensing and Climate Data. American Geophysical Union, AGU Fall Meeting: San Francisco, CA, December 11, 2023-December 15, 2023, 10.13140/RG.2.2.35583.18085
https://doi.org/10.22541/essoar.170688858.81127989/v1

Asfaw, D., Smith, R., Majumdar, S., Lakshmi, V., Fang, B., Grote, K., Butler, J. J., Wilson, B. B. (2023). Capturing the Spatio-Temporal Variability of Groundwater Pumping Using Remote Sensing Products and Machine Learning Techniques: An Assessment of Training Data Quality and Quantity Implications on Model Performance. American Geophysical Union, AGU Fall Meeting: San Francisco, CA, December 11, 2023-December 15, 2023