A Full Stack Software Developer, currently working on Advanced Learning Algorithms
building sustainable neural networks, deploying LLMs that scale and crafting
intelligent systems with knowledge-aware graph networks.
In my free time, you can catch me training in Chess
refining my Mathematics skills, or experimenting with new
Linux Distros just for fun.
I'm a full stack developer and AI enthusiast driven by curiosity and a deep love for math and learning. I work on advanced learning algorithms, graph neural networks, and decentralized platforms. Most comfortable building in Python, JavaScript, and Linux environments — especially when there's a terminal involved. Whether I'm debugging models, exploring abstract math, or trying out obscure Linux distros, I'm always chasing the "why" behind everything.
Focused on MPC (Maths, Physics, Chemistry) with parallel preparation for JEE Advanced. Spent significant time diving into advanced mathematics topics such as combinatorics, number theory, and inequalities — influenced by Olympiad-style problem solving. Developed critical thinking, abstraction, and proof-based reasoning alongside a strong foundation in calculus and physics.
Graduated with distinction and began exploring foundational concepts in logic, algebra, and discrete mathematics early on. Regularly participated in problem-solving communities and engaged in Olympiad-style training programs that nurtured a deep interest in math beyond the standard syllabus.
Building a decentralized social platform on the ICP blockchain, focused on privacy, identity integrity, and trust-based interaction loops. Worked with technologies like Rust, JavaScript, Internet Identity, and Canister smart contracts. Designed real-time data flows, off-chain message verification systems, and explored edge cases around digital identity decentralization.
Designed an experimental latent transformer that combines structured embedding models with graph traversal techniques. Focused on building scalable knowledge-aware systems capable of symbolic reasoning over noisy or incomplete data. Worked extensively with Python, PyTorch, NumPy, and custom embedding structures. Introduced entropy-driven pruning to improve signal clarity in graph representation learning.
Engaged in self-directed research exploring the implications and structure behind the Riemann Hypothesis. Studied deep analytic number theory, explored zeta zero distributions, and attempted various visualization techniques to interpret non-trivial zeros. This phase honed my proof intuition, abstract thinking, and mathematical maturity far beyond standard curriculum exposure.
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