Education

• M.S. in Geography – GIS Specialization, South Dakota State University , May 2026
• B.E. in Geomatics Engineering, Institute of Engineering, Tribhuvan University, 2020

Work experience

Graduate Research Assistant — Remote Sensing & Digital Agriculture

I build production-grade Earth observation and computer vision systems for high-throughput wheat phenotyping, turning UAV, close-range, and satellite imagery into validated traits used directly in breeding workflows.

• Built and deployed a cloud-native phenotyping platform (wheatai.net) on GCP, converting multi-source imagery into quantitative phenotypes; utility patent under consideration.
• Developed satellite-based wheat tiller density mapping using HLS via the GEE Python API, benchmarked against field measurements for the USDA NCR-SARE Wheat-TDM project.
• Trained vision models in PyTorch (YOLO-OBB, RT-DETR, SAM) robust to variable illumination and canopy variability.
• Designed high-throughput RGB/multispectral pipelines with QA/QC; two manuscripts under review.
• Engineered Dockerized PyTorch inference on GCP for asynchronous ingestion and scalable delivery.
• Led validation with breeders, translating outputs into operational decisions and funding proposals.

Tech stack: PyTorch · YOLO-OBB · RT-DETR · SAM · GEE · HLS · GCP · Docker · UAV phenotyping

Research Associate — Remote Sensing Analyst

Led large-scale Earth observation analytics and operational remote sensing workflows supporting vegetation monitoring, disaster response, and land-productivity assessment across multiple countries.

• Led country-scale EO analytics using Landsat and Sentinel-1/2 to quantify vegetation dynamics and surface water trends.
• Built production SAR and optical processing pipelines, reducing processing latency from ~1 week to under 24 hours.
• Authored reproducible Python-based multi-temporal optical and SAR workflows with QA/QC for rapid disaster-response deployment.
• Designed and maintained PostgreSQL/PostGIS geospatial data infrastructure and ETL pipelines with clean metadata and traceability.
• Delivered Google Earth Engine training for FAO-affiliated technical staff to operationalize vegetation and land-productivity monitoring.
• Executed GEE-based vegetation and groundwater analytics for FAO (Afghanistan) and SAR post-disaster reconstruction monitoring for ADB.

Internships

GIS Developer Intern

Supported operational disaster-response and environmental monitoring systems through applied machine learning and scalable geospatial workflows.

• Built and deployed a U-Net–based SAR flood-mapping workflow, delivering validated flood-extent layers into operational GIS platforms.
• Integrated model outputs into production disaster-response systems supporting Sentinel-Asia initiatives.
• Developed automated geospatial ETL and validation routines ensuring CRS consistency and reproducible outputs.

Geospatial Intern (Remote)

Applied deep learning and multi-sensor satellite analytics for environmental monitoring and investigative reporting.

• Developed U-Net segmentation workflows in Google Earth Engine using Sentinel-2 imagery for road detection and surface-change analysis.
• Conducted SAR-based detection of illegal oil dumping using rapid screening pipelines in GEE.
• Performed PlanetScope-based change analysis for environmental monitoring, producing decision-ready maps and concise technical reports.

Skills

• Skill 1
• Skill 2
  • Sub-skill 2.1
  • Sub-skill 2.2
  • Sub-skill 2.3
• Skill 3

Publications

Paper Title Number 5, with math \(E=mc^2\)

Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).

Paper Title Number 4

Your Name, You. (2024). "Paper Title Number 3." GitHub Journal of Bugs. 1(3).

Paper Title Number 3

Your Name, You. (2015). "Paper Title Number 3." Journal 1. 1(3).

Paper Title Number 2

Your Name, You. (2010). "Paper Title Number 2." Journal 1. 1(2).

Paper Title Number 1

Your Name, You. (2009). "Paper Title Number 1." Journal 1. 1(1).

Talks

• Thapa, S., Singh, M., Ghimire, H., Koupal, D., Kaushal, S., Halder, J., Maimaitijiang, M., Sehgal, S.K.* (2025). Advancing Wheat Disease Phenotyping through YOLOv11 and YOLOv12: Automated Detection of FHB Severity and Damaged Kernels. — 2025 CANVAS, November 9-12, 2025 , Salt Lake City, UT
• Kaushal, S., Sehgal, S.K.*, Maimaitijiang, M., Billah, M.M., Singh, M., Koupal, D., Gill, H., Thapa, S., Halder, J., Ghimire, H., Subedi, S. (2025). Integrating Genomics and High-Throughput Phenotyping with Machine Learning for Predictive Breeding in Winter Wheat. — 2025 CANVAS, November 9-12, 2025 , Salt Lake City, UT
• Maimaitijiang, M., Sehgal, S.K., Kaushal, S., Billah, M.M., Janjua, U.U.R., Subedi, S., Ghimire, H., Thapa, S., Halder, J. (2025). From Image to Insight: AI-Driven Wheat Monitoring and Yield Prediction with Multi-Scale Sensing. — 2025 CANVAS, November 9-12, 2025 , Salt Lake City, UT
• Ghimire, H., Maimaitijiang, M*., Kaushal, S., Koupal, D., Poudel, K., Thapa, S., Singh, M., Subedi, S., Janjua, U. U. R., & Sehgal, S. K*. (2025). Deep Learning-Assisted Wheat Yield Estimation Through Spike and Spikelet Counting from High-Resolution Imagery. — 56th Annual SDSU Geography Convention, April 3-4 , Brookings, SD, USA
• Ghimire, H., Maimaitijiang, M*., Kaushal, S., Koupal, D., Poudel, K., Subedi, S., Janjua, U.U. R., & Sehgal, S. K*. (2025). Deep learning-based detection and counting of wheat spikes and spikelets using high-resolution field imagery for improved yield estimation. — 2025 McFadden Symposium, March 3 , Nebraska, USA

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Service and leadership ======

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