CS student passionate about technology, problem-solving and learning, with experience in development, AI and research.
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Computer Science & Engineering • 2028
Bachelors of Technology in Computer Science and Engineering • Jul 2024 - Jul 2028
• Co-authored and published a peer-reviewed research paper, Decoding Breast Cancer Prognosis: An AI-Driven Analytical Approach to Genetic Markers and Prediction Metrics, at ICDAM 2025. • Selected as a Flipkart Girls Wanna Code 7.0 Scholar, ranking among the top 200 students across India from 27K+ registrations.
Webappmate • May 2026 - Jul 2026
• Owned the development of a client-approved food-truck platform spanning a React Native customer app and 2 TypeScript dashboards, from database design and API integration to feature delivery. • Integrated Google Maps APIs and Supabase/PostgreSQL to support nearby-truck discovery, real-time data synchronization, authentication, menu management, and administrative workflows. • Developed a Python automation backend with 22 validation rules and 3 REST APIs to compare operational spreadsheets and generate structured Excel exception reports. • Performed functional and regression testing for production webs
Indira Gandhi Delhi Technical University for Women • May 2025 - Aug 2025
Random Forest, SVM, Logistic Regression}) for genetic disease risk prediction using genomic and clinical data. Trained and validated models on public biomedical datasets to assess classification performance and generalization.
• Engineered a full-stack platform comprising 3 applications: a React Native customer app, a truck-owner dashboard, and a super-admin dashboard, delivering 10+ core features. • Integrated Google Maps API for location-based truck discovery and implemented 10+ workflows covering truck onboarding, menu management, availability, user administration, and analytics. • Designed a PostgreSQL-backed Supabase architecture with 7+ relational tables, real-time synchronization, authentication, and CRUD operations across trucks, users, menus, and orders.
• Developed a Python-based validation engine to reconcile two operational datasets using field mapping, normalization, and composite record-matching logic. • Implemented 22 exception rules to detect missing shifts, duplicate records, field mismatches, and formatting errors, generating structured Excel reports containing source values and actionable exception messages. • Built and tested 3 REST APIs for file upload, exception-report retrieval, and job deletion using Flask and Postman, enabling automated end-to-end processing of two input spreadsheets.
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