I am a multidisciplinary data enthusiast, particularly passionate about geospatial analysis, aiming to research and develop data-driven solutions that enhance decision-making and create real-world impact across various domains.<\/p>\n
abdullahjunejo@hotmail.com
\nhttps:\/\/www.linkedin.com\/in\/abdullah-junejo<\/p>\n
+92 334 386 4685<\/p>\n
Dhanani School of Science and Engineering
\nBS Computer Science<\/p>\n
2025
\nGeospatial Analysis, Graph Database, HUMUN VI Director Photography, Python, SQL<\/p>\n
Habib University MUN VI Director Photography, HUSG 2024-25 Communications Cabinet Chair, IFTP 2023 Runner Up, Student Life Ambassador 2023, Orientation Leader 2023<\/p>\n
Weather Walay – Agaahi
\nWeatherWalay is an AI-powered weather prediction system for hyper-local nowcasts and forecasts in Pakistan. It integrates Kriging, CNNs, LSTMs, and Transformer models to enhance accuracy. The system provides real-time, role-based advisories for freight transporters, travelers, and event managers via a mobile app, offering route-specific weather alerts and an advanced mode with detailed maps and model selection. The project addresses Pakistan’s unpredictable weather by improving localized forecasting, benefiting transport, logistics, event planning, and climate resilience by training models on Hyper Local Weather Stations Data. Key findings include increased forecast accuracy, customized insights, and real-time alerts. Future applications include climate risk assessment and smart agriculture.<\/p>\n","protected":false},"excerpt":{"rendered":"
I am a multidisciplinary data enthusiast, particularly passionate about geospatial analysis, aiming to research and develop data-driven solutions that enhance decision-making and create real-world impact across various domains. abdullahjunejo@hotmail.com https:\/\/www.linkedin.com\/in\/abdullah-junejo +92 334 386 4685 Dhanani School of Science and Engineering BS Computer Science 2025 Geospatial Analysis, Graph Database, HUMUN VI Director Photography, Python, SQL Habib […]<\/p>\n","protected":false},"author":9,"featured_media":6149,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"single-graduate-01.php","format":"standard","meta":{"_acf_changed":false,"footnotes":""},"program":[29],"school":[42],"class":[41],"minor":[52],"class_list":["post-6238","graduate","type-graduate","status-publish","format-standard","has-post-thumbnail","hentry","program-bscs","school-dhanani-school-of-science-and-engineering","class-class-of-2025","minor-not-applicable"],"acf":{"aspiration_statement":"I am a multidisciplinary data enthusiast, particularly passionate about geospatial analysis, aiming to research and develop data-driven solutions that enhance decision-making and create real-world impact across various domains.","video_profile_link":"","habib_email":"","email":"abdullahjunejo@hotmail.com","linkedin_link":"https:\/\/www.linkedin.com\/in\/abdullah-junejo","portfolio_Title":"","portfolio_link":"","personal_cell_1":"+92 334 386 4685","personal_cell_2":"","core_skills":[{"skill":"Geospatial Analysis, Graph Database, HUMUN VI Director Photography, Python, SQL"}],"academic_awards":null,"leadership-experiences":[{"leadership":"Habib University MUN VI Director Photography"},{"leadership":"HUSG 2024-25 Communications Cabinet Chair"},{"leadership":"IFTP 2023 Runner Up"},{"leadership":"Student Life Ambassador 2023"},{"leadership":"Orientation Leader 2023"}],"work-experience":null,"publications-projects":null,"project_title":"Weather Walay - Agaahi","fyp_desc":"WeatherWalay is an AI-powered weather prediction system for hyper-local nowcasts and forecasts in Pakistan. It integrates Kriging, CNNs, LSTMs, and Transformer models to enhance accuracy. The system provides real-time, role-based advisories for freight transporters, travelers, and event managers via a mobile app, offering route-specific weather alerts and an advanced mode with detailed maps and model selection. The project addresses Pakistan's unpredictable weather by improving localized forecasting, benefiting transport, logistics, event planning, and climate resilience by training models on Hyper Local Weather Stations Data. Key findings include increased forecast accuracy, customized insights, and real-time alerts. Future applications include climate risk assessment and smart agriculture.","project_pictures":null,"project-video":""},"yoast_head":"\n