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Cover image for Project Name: Pakistan Street Food
Project Name: Pakistan Street Food Guide Project Overview Designed and developed a multi-page, custom website celebrating the rich culinary heritage and vibrant street food culture of Pakistan. The "Pakistan Street Food Guide" serves as a digital exploration of traditional recipes, local favorites, and the stories behind regional street food vendors. Key Features & Deliverables Custom Multi-Page Architecture: Structured a seamless navigation experience across dedicated sections, including the homepage, a visual Food Gallery, and a Contact portal. Lightweight Front-End Engineering: Built entirely from scratch using clean HTML5, CSS3, and vanilla JavaScript. By avoiding heavy external frameworks, the site maintains rapid load times and a highly optimized codebase. Semantic UI/UX Design: Developed an intuitive, image-focused layout designed to highlight cultural storytelling and visual content effectively across different screen sizes. #html #CSS#Frontend #javascript#webdevelopment
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Cover image for Overview This project provides an interactive
Overview This project provides an interactive platform for financial data analysis and predictive modeling. Built with Python and Streamlit, it enables users to visualize financial trends and explore machine learning-driven insights through an intuitive, web-based interface. Key Features Data Pipeline: Automated ingestion and cleaning of financial datasets. Feature Engineering: Implementation of financial indicators and technical features to capture market dynamics. Predictive Modeling: A streamlined training and evaluation workflow using robust machine learning algorithms. Interactive Visualization: Real-time dashboards allowing users to experiment with different parameters and model configurations. Technical Workflow Data Loading: Robust ingestion of historical financial data. Preprocessing: Handling missing values, noise reduction, and data normalization. Feature Engineering: Extraction of meaningful market features (e.g., technical indicators, volatility metrics). Model Training & Evaluation: A modular approach to training, testing, and validating model performance using industry-standard metrics. Experience It Live Explore the application and interact with the model here: 👉 Financial ML Dashboard (https://vtt4xouy7ifekdm7s5c4zj.streamlit.app/)
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Cover image for Project Title: Privacy-Preserving Sepsis Prediction
Project Title: Privacy-Preserving Sepsis Prediction Model Role: AI Research Intern The Challenge: Integrating complex Recurrent Neural Network (RNN) architectures within a privacy-preserving Multi-Party Computation (MPC) environment. Addressing the "fixed-point arithmetic" limitations inherent in cryptographic frameworks when handling 48-hour sequential medical data. My Approach: Mentored by a Yale University researcher to bridge the gap between AI and secure cryptographic protocols. Independently engineered custom normalization and clipping techniques to resolve data overflow issues caused by sequential computation loops. Successfully optimized the model for the MIMIC-III dataset, ensuring high-quality, functional code performance. The Outcome: Successfully contributed the finalized research implementation to the official open-source repository. Demonstrated technical maturity in handling complex engineering bottlenecks while adhering to formal research methodologies. Here is my repo link: https://github.com/sum710/sequre
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Cover image for Title: Civitas: Digitizing Community Savings
Title: Civitas: Digitizing Community Savings & Financial Trust with AI Description: Traditional community-based saving groups (committees/ROSCA) have always been built on trust, but they often struggle with manual record-keeping, transparency issues, and security risks. I am excited to share Civitas, a full-stack FinTech solution designed to modernize the committee system. Civitas transforms how communities save and contribute money by blending automated financial management with AI-driven security. Key Features: Digital ROSCA Management: A transparent platform where users can join, track, and manage their committee contributions automatically. AI-Powered Trust Engine: A scoring logic that tracks payment consistency and user reliability, ensuring a safe ecosystem. Payout Security: Payouts are protected with mandatory Two-Factor Authentication (2FA) and Google OAuth integration. Modern Tech Stack: Built with React 19, Supabase Cloud, and AI financial advisors. Why Civitas? In the world of FinTech, trust is everything. Civitas replaces paper-based tracking with a permanent, automated digital ledger, reducing human error and building a stronger, more reliable financial community. Explore the live project here: https://civitas-backend.vercel.app/ I’d love to hear your thoughts on how digital transformation is changing the landscape of community-based finance! #FinTech #WebDevelopment #FullStack #AI #ProductDesign #Civitas #FinancialInclusion
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