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Turning Data Chaos into Clarity
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Turning Data Chaos into Clarity
22 followers · 4 following

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Achievement: Pair ExtraordinaireAchievement: YOLOAchievement: Pull SharkAchievement: Quickdraw

Achievements

Achievement: Pair ExtraordinaireAchievement: YOLOAchievement: Pull SharkAchievement: Quickdraw

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Mudassiruddin7/README.md

Typing SVG

Research: Mechanistic Interpretability · Federated Fairness · Edge ML · Agentic Systems · Quantum-Classical Optimization

github-snake

⚡ The spatial mapping layer was rewritten three times. The third failure became the architecture. Research

  • Building: Perception (real-time navigation on sub-$20 hardware, zero internet, 80ms latency)
  • Publishing: 8 papers in 2026 (axRiv, JMLR, ACM Digital Library, Discover AI)
  • Researching: sparse circuit extraction in billion-parameter LLMs without full-graph traversal
  • Open to: research collaboration on mechanistic interpretability, federated systems, agentic AI
  • Reach me: mohd.mudassiruddin7@gmail.com

Connect with me:

Languages and Tools:

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  1. YatraKavach YatraKavach Public

    SmartYatra is a revolutionary tourist safety and monitoring platform designed to protect and assist travelers throughout their journey.

    TypeScript

  2. TradeGenie TradeGenie Public

    TradeGenie is an educational, proof-of-concept trading platform that combines a sleek React-and-Vite web interface with a robust Python-based backend powered by FastAPI and Poetry.

    Python 1

  3. BioNova BioNova Public

    Predict gene edits. Monitor lab conditions. Secure your research.

    TypeScript 2

  4. MediCoach MediCoach Public

    This medical application leverages advanced machine learning models to predict results across a wide range of medical tests with high accuracy. By analyzing patient data, the app delivers real-time…

    Jupyter Notebook

  5. Identifying-Defects-In-Various-Fabric-Using-CNN Identifying-Defects-In-Various-Fabric-Using-CNN Public

    Detect fabric defects with precision using CNN in this project, ensuring product quality and reducing manufacturing errors.

    Jupyter Notebook 2

  6. Detection-Of-Retinal-Pigmentosa-In-Paediatric-Age-Patients-Using-CNN-With-Tkinter-Framework Detection-Of-Retinal-Pigmentosa-In-Paediatric-Age-Patients-Using-CNN-With-Tkinter-Framework Public

    Revolutionizing pediatric retinal disease diagnosis, our innovative approach employs Convolutional Neural Networks (CNN) integrated with Tkinter framework for swift and accurate detection of Retina…

    Jupyter Notebook 1