Amna Riaz@amnariaz22
3
Events attended
3
Submissions made
1 year of experience
About me
AI researcher and builder with a Master's in Artificial Intelligence. I work on intelligent systems that combine machine learning, knowledge graphs, and retrieval-augmented generation to solve real-world problems. My background spans both research and operations, which means I care about AI that's not just technically sound, but actually useful. Currently exploring agentic AI, NLP, and how smart systems can support complex decision-making.
🤝 Top Collaborators
🤓 Latest Submissions
PaperBand AI
PaperBand AI is a multi-agent research paper review system built for academics, students, and conference organizers who need fast, structured feedback on academic work. Instead of waiting weeks for peer review, users simply upload any research PDF and three specialized AI agents go to work collaboratively. Agent 1 (Summarizer) reads the full paper and extracts the title, authors, research problem, methodology, key results, and conclusion into a clean structured format. Agent 2 (Critic) receives that summary through a shared Band Room message bus and performs a rigorous peer-review-style analysis — identifying strengths, weaknesses, missing experiments, and limitations. Agent 3 (Recommender) reads both the summary and critique, then scores the paper from 1 to 10 and issues a formal publication decision: Accept, Accept with Minor Revisions, Major Revisions Required, or Reject — with a written justification and future research suggestions. All three agents communicate through a custom BandRoom, a shared in-process message bus that simulates real agent collaboration. The entire pipeline runs on Groq's free LLaMA 3.3 70B model, making it blazing fast and completely free to use. The frontend is built in Streamlit with a custom dark academic UI, and the project is structured as a clean modular Python codebase with separate agent classes, a PDF reader utility, and secure API key management via dotenv. PaperBand AI was built in one day as a hackathon project by Team IDEA — a five-person team specializing in Data and Agentic AI.
19 Jun 2026
VanGuard - Open Web Theart Intelligence
VANGUARD is an AI-powered open web threat intelligence platform built to fill the blind spots that traditional security tools miss. While SIEMs and firewalls protect the perimeter, they can't see credential dumps on paste sites, silent regulatory updates, or vendor breaches spreading across the open web. VANGUARD solves this by running three parallel detection modules — a Threat Surface Monitor, a Regulatory Change Tracker, and a Vendor Risk Radar — that continuously scan the open web for signals relevant to your organization. All findings are fed into Groq's LLaMA-3.3-70B model, which classifies each alert by severity, extracts indicators of compromise, and generates actionable recommendations in under 30 seconds. Built with React and Vite on the frontend and deployed on Netlify, VANGUARD delivers a cinematic, real-time threat dashboard that turns raw open-web noise into a structured, severity-ranked intelligence report — giving security teams the early warning system they've always needed, powered entirely by AI.
31 May 2026
TradeAgents
AI Trading Agents An automated AI-powered trading system designed to analyze market data, generate trading signals, and execute trades using configurable strategies. 🚀 Features 📊 AI-based trading decision making 🔌 Integration with Kraken API 🧪 Paper trading mode for safe testing ⚙️ Configurable trading parameters 📈 Modular and extensible architecture 🗂️ Project Structure AI-Trading-Agents/ │── ai_agent.py # Core AI trading logic │── app.py # Main application entry point │── config.py # Configuration settings │── kraken_client.py # Kraken API integration │── paper_trader.py # Paper trading simulation │── requirements.txt # Dependencies │── .env # Environment variables
12 Apr 2026
