Engineering ideas into reliable software.
I'm an aspiring Software Engineer who enjoys turning real-world problems into practical, reliable software.
I build AI-powered applications, automated pipelines, and backend systems, while exploring how intelligent tools can make complex engineering workflows simpler and more efficient.
I learn by building, competing in hackathons, contributing to open source, and continuously taking on problems that push me to become a better engineer.
Selected projects showcasing AI systems, intelligent automation, machine learning pipelines, and practical software engineering.
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AI-assisted manufacturing cost estimation system that transforms STEP CAD geometry into an explainable production quote. The pipeline combines geometry extraction, process routing, stock estimation, machine-learning prediction, cost analysis, confidence estimation, and explainability. Stack
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AI-powered crisis management backend that transforms chaotic voice-based incident reports into structured, actionable workflows. Uses an LLM to extract actions and urgency, then automates task creation, team notifications, and client communication through external APIs. Stack
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Long-context narrative reasoning system that determines whether a hypothetical character backstory is consistent with a complete novel. Combines persistent state, sparse updates, incremental belief formation, semantic embeddings, evidence retrieval, and hybrid neural-rule-based classification. Stack
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Hybrid machine-learning pipeline for autoregressive flood prediction across coupled urban drainage and surface networks. Combines LightGBM with ARX Ridge models, graph-based features, temporal forecasting, rainfall features, and physics-informed stability constraints. Stack
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Building, experimenting, and contributing across software and AI projects.
