B.Tech Computer Science & Engineering student with an Entrepreneurship minor at Ahmedabad University. Focused on translating stochastic mathematics, vision-language architectures, and deep neural models into high-precision computational systems.
I approach software and machine learning with an architectural philosophy: code is not just functionality, but a rigorous formulation of probability, logic, and systems resilience.
At Ahmedabad University, my studies bridge deep computational science with the practical execution of entrepreneurship. My technical inquiry centers around two complementary disciplines: training multimodal vision-language models capable of parsing symbolic notations into executable software, and designing stochastic quantitative models that evaluate fat-tailed financial risk under market volatility.
Vision-Language Models (VLMs), multi-agent orchestration, unsupervised autoencoders for anomaly detection, and compiler-level AST translations.
GARCH(1,1) conditional volatility clustering, high-volume Monte Carlo path simulations, and Parametric / Historical Value at Risk (VaR) estimation for FX assets.
Engineered systems solving critical challenges in infrastructure security, multimodal symbolic compilation, and probabilistic risk modeling.
High-end Japanese anime scale figure collecting represents a multi-billion dollar alternative asset class crippled by counterfeit saturation and peer-to-peer transaction risks. OtakuBazaar introduces an institutional-grade infrastructure bridging cryptographic escrow security, physical vault custody, and instant liquidity.
AI-powered cyber-resilient infrastructure threat detection engine combining unsupervised deep autoencoders, conditional rule evaluation, and correlation graphs to identify zero-day intrusions with sub-18ms latency.
End-to-end vision-language pipeline that parses handwritten and printed mathematical equations from images, compiles them into Abstract Syntax Trees, and emits validated, executable Python simulations.
Stochastic financial risk framework for Foreign Exchange (FX) currency pairs. Couples maximum likelihood GARCH(1,1) volatility clustering with vectorized Monte Carlo trajectory generation to forecast 95% and 99% Value at Risk.
Active roles spanning technical community ambassadorship, competitive engineering sprints, and developer mentorship.
Categorized breakdown of programming languages, mathematical risk paradigms, neural frameworks, and developer tooling.
Working papers and intellectual notes on applied stochastic models, compiler engineering, and deep neural vision systems.
I am open to quantitative finance research opportunities, machine learning internships, and collaborative ventures with boutique engineering teams.
Ahmedabad, Gujarat, India
Ahmedabad University Campus