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.
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Computer Vision
- Anomaly Detection
- Lightweight architectures & representation learning
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NLP & RAG Systems
- PDF / CSV based Retrieval-Augmented Generation
- FAISS, Pinecone, chunking, embeddings
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ML Systems
- Pipeline design, evaluation, and ablation thinking
- Bridging research ideas → deployable systems
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Self-Evolving AI Systems
- Designed agents that iteratively improve outputs using feedback loops
- Focus on autonomy, adaptation, and long-term performance improvement
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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
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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
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🔹 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.