
## Overview

FLO models traffic as continuous vector fields (curb/median repulsion, goal attraction, inter-vehicle discomfort) combined with bicycle-model vehicles. Local interactions produce global structure — lanes and predictable flows — without explicit lane rules.

## Key Results (concise)

- **Lane Formation Index:** 1.000 (Following) — perfect self-organization  
- **Trajectory Entropy:** 0.174 (Chaotic) — low unpredictability; structure emerges from conflict  
- **Stoppage Frequency:** 97.8% (Merging) — realistic bottleneck behavior

These metrics show: emergence works, traffic is not random, and the model reproduces realistic congestion.

## Visualization

- Real-time plots and ffmpeg-backed animations (matplotlib + FuncAnimation)
- Field visualization (quiver), vehicle trajectories, and per-frame snapshots for presentations

## Implementation Details

- Core: Python (NumPy, SciPy)
- Simulation: custom continuum field + bicycle vehicle controller
- Visualization: Matplotlib (animation to MP4)
- Experiment scripts: scenario definitions, metric logging, and plot generation

## Future Work

- Implement Fast Sweeping (O(m)) to replace FMM for linear-time field solve  
- GPU acceleration for large-scale, real-time simulations  
- Validate against real-world trajectory datasets (e.g., NGSIM)  
- Add curved roads, intersections, traffic lights, and richer driver models

## Links

- Repository: 
- Animation: 

p.s this is my fyp, marked in highest in the entire cs batch in FYP-1 evaluation. :3

FLO is a continuum crowds inspired traffic model that demonstrates emergent lane formation and realistic congestion negotiation as seen in chaotic traffic.

FLO — Field-based Traffic Simulator | Danish Haroon

Emergence & Continuum Methods

FLO (Field-based Traffic Model)


## Overview

Worked on **IAugmentor**, a Python package for intelligent data augmentation with intraclass/interclass-aware strategies: implemented **ResNet-based embedding extraction**, **K-means clustering** for intraclass diversity analysis, and **Anchored Distribution Interpolation (ADI)** to balance datasets while preserving meaningful subgroup structure.

## Technical Implementation

- **Multi-level Augmentation Pipeline**: Designed and deployed multi-level augmentation pipeline with concurrent transformation execution (threading), cluster-aware sampling for underrepresented subgroups, and comprehensive pre/post-augmentation analysis.

- **Validation & Results**: Validated IAugmentor on **Oxford-102 Flowers** and **HAM10000** skin lesion datasets, achieving targeted class balance while maintaining intraclass diversity through intelligent cluster-based sampling, documented in automated comparison reports.

## Technologies Used

- Python
- ResNet
- K-means Clustering
- Computer Vision
- Threading/Concurrency
- Data Augmentation


A Python package for intelligent data augmentation with intraclass/interclass-aware strategies using ResNet embeddings, K-means clustering, and Anchored Distribution Interpolation.

IAugmentor - Intelligent Augmentation | Danish Haroon

AI, Computer Vision

IAugmentor - Intelligent Augmentation


## Overview

Built **Two-Tongue Tales**, a SaaS platform where users can generate bilingual stories with AI-created images. Developed entirely solo, handling everything from the frontend and backend to the database, AI integration, payments, and deployment. The app lets users create stories, manage a library, purchase credits, and track their content in real-time.

## Frontend & UX

Built with **Next.js 16** and **React 19**, creating a responsive interface that works across devices. Used **Radix UI** components for accessible, polished interactions and **Tailwind CSS** for styling. The app includes story creation workflows, user dashboards, a story library, and checkout flows.

## Backend & Database

Used **Drizzle ORM** with **Supabase** to manage user data, stories, and credits efficiently.

## AI & Image Generation

Integrated **Google Gemini API** to generate bilingual stories based on user input. Used **Cloudinary** to handle image generation and storage for story illustrations.

## Authentication & Payments

Implemented **NextAuth** + **Resend** + **Supabase** for secure user authentication with magiclinks. Connected **Stripe** to handle credit purchases and manage the subscription/credit system with webhook validation.

## Technologies Used

- **Frontend**: Next.js 16, React 19, TypeScript, Tailwind CSS, Radix UI
- **Backend**: Next.js API Routes
- **Database**: PostgreSQL, Drizzle ORM
- **AI**: Google Gemini API
- **Images**: Cloudinary
- **Auth**: NextAuth
- **Payments**: Stripe
- **Email**: Resend

## Key Features

- AI-generated bilingual stories
- Dynamic image generation
- Credit-based purchase system
- User authentication and profiles
- Story library and management
- Real-time story status tracking
- Mobile-friendly responsive design

Working on a full-stack SaaS platform that generates bilingual stories with AI-created images. Handles user authentication, payments, and credits. developed entirely solo from concept to production.

Two-Tongue Tales | Danish Haroon

AI-Powered Bilingual Story Generation Platform

Two-Tongue Tales


## Overview

Engineered a full-stack journal application with **FastAPI** for a high-performance backend and **Next.js** for a responsive frontend, implementing secure authentication, CRUD operations, and RESTful API integration.

## Infrastructure & Deployment

- **Containerization**: Containerized all components with **Docker** and deployed on **Kubernetes**, using Ingress for intelligent load balancing, ConfigMaps and Secrets for environment management, and horizontal scaling for reliability.

- **CI/CD Automation**: Automated infrastructure provisioning and CI/CD pipelines using **Terraform**, **Ansible**, **GitHub Actions**, and **ArgoCD**, deploying seamlessly to **AWS EC2** for scalable, cloud-native operations and continuous delivery.

## Technologies Used

- FastAPI
- Next.js
- Docker
- Kubernetes
- Terraform
- Ansible
- GitHub Actions
- ArgoCD
- AWS EC2


Engineered a full-stack journal application with FastAPI and Next.js, containerized with Docker and deployed on Kubernetes with automated CI/CD pipelines.

Projects

FLO (Field-based Traffic Model)

IAugmentor - Intelligent Augmentation

Two-Tongue Tales

Journal Management Platform