Sairam S

My work is driven by a core philosophy: true progress in AI requires moving beyond “black box” models and building systems from first principles. I focus on meticulous engineering and the scientific principle of reproducibility to gain a deep, functional understanding of the mechanics that govern machine learning.

My projects are demonstrations of this philosophy in action:

• Reproducibility and Analysis: I performed a complete, clean-room replication of the Sharpness-Aware Minimization (SAM) optimizer (ICLR 2021). By building the algorithm solely from the paper’s description, I successfully reproduced its CIFAR-10 results, validating its performance and the importance of scientific rigor.
  • View the Project Analysis & Code →
• First-Principles Engineering: To master the mechanics of deep learning, I engineered a complete, reverse-mode automatic differentiation engine in Python from the ground up. I then built and trained a neural network using only this custom framework, demonstrating a ground-up understanding of gradient-based learning.
  • Explore the Autodiff Engine →