I work on Machine Learning, Computer Vision, and NLP systems, with a strong interest in
research-driven problem solving and end-to-end ML pipelines.

Turning data into understanding — and models into systems.

• Computer Vision

  • Anomaly Detection
  • Lightweight architectures & representation learning

• NLP & RAG Systems

  • PDF / CSV based Retrieval-Augmented Generation
  • FAISS, Pinecone, chunking, embeddings

• ML Systems

  • Pipeline design, evaluation, and ablation thinking
  • Bridging research ideas → deployable systems

• Self-Evolving AI Systems

  • Designed agents that iteratively improve outputs using feedback loops
  • Focus on autonomy, adaptation, and long-term performance improvement

• End-to-End Predictive Systems

  • Built and deployed an F1 Race Outcome Predictor
  • Covered the full pipeline: data ingestion → feature engineering → model training → deployment → inference

• Responsible & Explainable AI

  • Integrated explainability mechanisms into self-evolving AI systems
  • Used lightweight adaptation strategies (e.g., PEFT-style updates) to enable safe, controlled learning
  • Focused on transparency, stability, and preventing uncontrolled model drift

• 🔹 Intelligent CSV Assistant (LLM-Powered)
  Chat with CSV files using LLMs — supports column explanation, NaN detection, and data insights.

• 🔹 RAG Chatbots (PDF / Website Data)
  Built multiple RAG systems using FAISS & Pinecone for academic content and real company data.

• 🔹 AI-based Clinic Triage System
  NLP-based triage classification & symptom extraction using SpaCy with a Flask backend.

• 🔹 F1 Race Outcome Predictor – Web App
  End-to-end ML system for predicting F1 race outcomes, covering data ingestion, feature engineering, model training, deployment, and live inference via a web interface.

• 🔹 Django Blog Application
  Full-stack Django app with MySQL backend and dynamic HTML frontend.

• 🥇 AIR 5 – Introduction to Large Language Models
• 🥈 AIR 16 – Responsible AI
• 🎓 B.Tech AI & DS (3rd Year)
• 📊 CGPA: 8.9 / 10
• 💼 Internship experience in Data Analytics, Data Scienist & AI-ML Systems

• Research internships (India & abroad)
• Deeper work in Computer Vision & Multimodal Learning
• Building robust ML systems that scale beyond experiments
• Publishing or contributing to research-grade projects
• Preparing for AI-ML-DS related job roles

• I treat commit messages like tiny research notes.
• I enjoy reading papers more than model zoo repos.
• My browser tabs have ablations.
• I once debugged a bug caused by a comment. 😵
• I trust learning curves more than accuracy scores.
• I’ve broken models on purpose just to understand why they worked.
• I read architecture diagrams before reading conclusions.
• I care more about failure cases than perfect results.
• I believe a good baseline can be more impressive than a complex model.

If you’ve read this far, you might as well ⭐ a repo.