Komma Manjunath Reddy

Full-stack Web GIS developer with M.Tech Geoinformatics (CPI 9.0, MNNIT Allahabad) and B.Tech Computer Science & Data Science (CGPA 9.03). Delivered end-to-end geospatial web applications using FastAPI, PostgreSQL/PostGIS, GeoServer OGC services (WMS, WFS, WPS), and Leaflet.js. Engineered spatio-temporal ML pipelines over 1,484 sq km of NCT Delhi for Land Surface Temperature prediction and 2030 UHI scenario forecasting.

Targeting

Web GIS Developer and Software Engineer roles where spatial data engineering meets production software development.

Full-Stack Web GIS Developer - Personal Project

(2026-05)

• Sole developer of a full-stack Web GIS application ingesting 393,000+ OSM road segments and hospital POIs into PostGIS via spatial geometry indexing, served through FastAPI REST endpoints.
• Built 8-layer Leaflet.js frontend with choropleth maps, MarkerCluster, heatmap overlays, and 500 m buffer zones; deployed as a standalone HTML dashboard.
• Implemented hospital coverage analysis, road density statistics, and neighbourhood spatial joins using PostGIS spatial functions (ST_Buffer, ST_Distance, ST_Intersects, ST_Union).

Full-Stack WebGIS Platform Developer - MNNIT Allahabad

(2024-08 - 2025-05)

• Developed full-stack WebGIS platform using OpenLayers integrating WMS, WFS, and WPS services, enabling real-time browser-based spatial rendering without desktop GIS software.
• Published PostGIS layers via GeoServer on Apache Tomcat; exposed spatial queries as REST API endpoints for dynamic feature filtering and attribute-driven map rendering.

LULC Classification & Change Detection System Developer - MNNIT Allahabad

(2024-08 - 2025-05)

• Automated end-to-end preprocessing pipeline for a 23-year Landsat time series, classifying 6 land-cover classes using supervised ML; achieved 85.59% overall accuracy (Kappa validated).
• Validated outputs with confusion matrix and Kappa coefficient analysis across multi-temporal imagery, confirming classification reliability over the full study period.

Clinical Sepsis Prediction ML Classification Pipeline Developer

(2024-01 - 2024-05)

• Built binary classification pipeline integrating patient vitals, lab results, and demographics; trained SVM and XGBoost models for early sepsis detection.
• Applied SHAP-based feature importance analysis and XGBoost hyperparameter tuning to improve model interpretability in line with healthcare AI standards.

M.Tech - Geoinformatics - MNNIT Allahabad (2024-08 - 2026-05)

B.Tech - Computer Science & Engineering (Data Science) - SVEC, Tirupati (2020-08 - 2024-05)