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  • IIIT Naya Raipur

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karthik-1604/README.md
Typing SVG

🧑‍💻 About Me

karthik = {
    "role"        : "ML Engineer · Data Scientist · Researcher",
    "currently"   : "@ IIIT Naya Raipur — final year CSE",
    "published"   : "5× IEEE Author across NLP, CV, sensors & GenAI ",
    "interests"   : [
                      "RAG & LLM Systems",
                      "Time Series & Predictive Maintenance", 
                      "Computer Vision",
                      "NLP & Multimodal AI",
                      "Whatever hard problem lands next 🤷"
                    ],
    "currently_building" : "Industrial AI + Aerospace GenAI pipelines",
    "ask_me_about"       : "ML research, IEEE publishing, or breaking LLMs",
    "fun_fact"           : "Ranked 354 / 75,000+ in Amazon ML Challenge "
}

🚀 Featured Projects

🛩️ AeroManual-RAG ⚙️ Predictive Maintenance
Advanced RAG pipeline over 2,223 pages of FAA aerospace manuals End-to-end RUL prediction for NASA turbofan engines
Hybrid BM25 + FAISS · RRF Fusion · BGE Reranking LSTM · TFT · Autoencoder Anomaly Detection
RAGAS Answer Relevancy: 0.875 ✅ LSTM RMSE: 15.42 · R² = 0.862 ✅
16/16 pytest · FastAPI · Streamlit SHAP Explainability · FastAPI Risk API
Repo Repo

🛠️ Tech Stack

Languages

Python SQL C++

ML / DL

PyTorch scikit-learn XGBoost HuggingFace NumPy Pandas

GenAI / RAG

LangChain FAISS OpenAI

Deploy / MLOps

FastAPI Streamlit Docker Git

📊 GitHub Stats

📄 IEEE Publications

"If it's not published, did it even happen?"

  • 📌 Machine Condition Monitoring Using TENG-Based Vibration Sensor — KST 2026 · DOI
  • 📌 Multimodal Content Moderation (MemeShield-1K) — IEEE Transactions (submitted)
  • 📌 +4 more IEEE conference papers — Google Scholar →

🏆 Highlights

🥇 Amazon ML Challenge — 354th / 75,000+  |  📊 NPTEL Top 1% in LLMs  |  📝 JEE Top 2% among 1M+

Popular repositories Loading

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    End-to-end Basel II compliant credit risk modeling system with PD/LGD/EAD models, SHAP explainability, PyTorch neural network and PSI monitoring

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    Financial sentiment analysis API with fine-tuned FinBERT achieving 88.57% accuracy on Financial PhraseBank dataset

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  6. Animal-classifier Animal-classifier Public

    Production-ready animal image classifier using EfficientNet-B3 and PyTorch. Trained on 26k images across 10 classes with 98.80% accuracy. Features EDA-driven training, class imbalance handling, ONN…

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