About

Abstract

I am a self-taught AI/ML engineer and independent NLP researcher bridging the gap between rigorous academic inquiry and practical, scalable systems. My primary focus lies in enhancing the faithfulness of Large Language Models in abstractive summarization.

Operating independently alongside my formal high school education, I have developed state-of-the-art evaluation pipelines, contributed to MVP architectures at startups, and engaged with leading minds as an AI Fellow.

Mathematics Coursework

MIT OpenCourseWare
  • 18.01 – Single Variable Calculus: Differential and integral calculus with rigorous problem-solving.
  • 18.06 – Linear Algebra: Matrix theory, eigen decomposition, orthogonality, and applications to deep learning.
  • 18.05 – Introduction to Probability and Statistics: Probabilistic modeling, Bayesian reasoning, and statistical inference.

Technical Skills

  • Languages: Python, C++, SQL
  • Libraries/Frameworks: NumPy, Pandas, Scikit-Learn, Matplotlib, Seaborn, Plotly, Transformers, Hugging Face, LLMs, TensorFlow, PyTorch
  • Tools & Platforms: Git/GitHub, Docker, REST APIs, Jupyter, VS Code, Firebase, MySQL, CUDA

Other Interests

  • Rubik's Cube (3x3): Average: 22.39s | Personal Record: 13.69s
  • Music: Alice in Chains, Metallica, Soundgarden, Nirvana, Pearl Jam, Pink Floyd, The Smiths, Fleetwood Mac, The Weeknd, Tame Impala, and others.
  • Geography & Trivia: Guessed 143/197 country flags in 18 minutes.