I am an AI Researcher & Systems Engineer specializing in neural-symbolic reasoning and high-performance machine learning systems.

My work bridges the gap between theoretical ML research and production-grade systems engineering. I have independently reproduced Google DeepMind’s AlphaProof mathematical reasoning approach and authored a novel gradient boosting algorithm that outperforms XGBoost on extreme class imbalance.

I thrive on building systems that are not just accurate but also blazing fast, scalable, and reliable.

Research Profile

  • Neural-Symbolic Reasoning: Combining Monte Carlo Tree Search (MCTS) with Transformer policy networks for automated mathematical reasoning.
  • High-Performance ML: Designing cache-aware, SIMD-optimized algorithms in Rust.
  • Concept Drift Adaptation: Developing novel techniques for learning under non-stationary data distributions.
  • Formal Verification: Building systems with formally verified proof traces to eliminate hallucinations.

The Discipline of Engineering

Outside of code, I am an Amateur MMA District Gold Medalist with a 5-1 record. Engineering and combat sports share the same DNA:

  • Pressure Testing: A system’s reliability is only proven when stressed to its limits.
  • Fundamental Mastery: Deep knowledge of data structures, operating systems, and mathematics over framework hype.
  • Relentless Improvement: Building elite systems requires daily discipline.

Other Achievements:

  • Amateur MMA District Gold Medalist, Mumbai 2022 (5-1 record)
  • TRCAC Chess Gold Medalist 2024