Research and Engineering

I am a theoretical physics PhD researcher transitioning into Machine Learning Engineering. My work focuses on bridging high-energy physics with representation learning and autonomous reasoning. Currently, I am developing data pipelines and flight risk prediction models for an AI-driven travel domain.

Research & Engineering Focus

Theoretical Physics

Conducting PhD research in soft photon theorems in quantum field theory. My recent work includes co-authoring publication in high-energy physics (arXiv: 2601.00336).

Machine Learning

Focused on mechanistic interpretability, representation learning, and training large language models. I build robust local RAG engines and autonomous reasoning systems utilizing PyTorch and FastAPI.

Data Architecture

Developing travel pipeline that includes orchestrating Medallion data pipelines on Google Cloud Storage to process millions of flight records and integrated weather data for flight delay risk prediction models.

Technical Ecosystem

  • Languages: Python, C++, SQL, Bash
  • Machine Learning & AI: PyTorch, Qdrant, PyDantic
  • Data & Backend Engineering: FastAPI, Airflow, Docker, GCP, Medallion Architecture
  • Applied Mathematics: Numerical Analysis, Mathematica, Sympy