Quantum Computing & AI Researcher
Quantum Error Correction · Autonomous AI Agents · Machine Learning from First Principles
I am a researcher and MS student working at the intersection of quantum computing, autonomous AI systems, and mathematical machine learning. My work focuses on building fault-tolerant quantum algorithms, designing autonomous multi-agent pipelines for scientific discovery, and implementing ML optimization algorithms from first-principles mathematics.
I have published in IEEE and international conferences, with research cited across the machine learning and healthcare informatics communities.
| Year | Title | Venue | Citations |
|---|---|---|---|
| 2023 | High Performance Cervical Cancer Detection Using Gradient Boosting Algorithm and Bayesian Optimization | IEEE ITT |  |
| 2023 | Crop Doctor: A Comprehensive Crop Management System for Precision Agriculture | River Publishers |  |
h-index: 1 · Total Citations: 11 · Source: Semantic Scholar
Designing stabilizer circuits and fault-tolerant noise models for surface codes. Building high-speed minimum-weight perfect matching (MWPM) graph decoders using Stim, PyMatching, and Qiskit. Exploring variational quantum eigensolver (VQE) pipelines for near-term quantum hardware.
Building multi-agent AI frameworks for computational drug discovery — including molecular property prediction, target binding analysis, and autonomous pipeline orchestration using agentic architectures.
Implementing mathematical optimization algorithms from ground-truth derivations: gradient boosting with deviance loss functions, LASSO via homotopy path optimization, and sparse recovery methods — all built without high-level ML library abstractions.
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Autonomous QEC syndrome decoder implementing surface code circuits with Stim noise simulation, PyMatching MWPM decoding, and Qiskit integration. Includes threshold analysis across physical error rates and interactive Streamlit visualization.
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Applied variational quantum eigensolver implementations and syndrome decoding pipelines for quantum error correction research.
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Autonomous AI agent suite for hybrid drug discovery workflows — molecular property prediction, target binding analysis, and multi-agent orchestration for computational chemistry pipelines.
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Complete derivation and implementation of gradient boosting machines with deviance loss optimization, built entirely from mathematical first principles without scikit-learn.
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LASSO regularization via the homotopy continuation path algorithm — implementing the full regularization path from the KKT conditions upward.
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High-performance web crawler with document indexing and search ranking engine. Implements crawling, parsing, inverted index construction, and TF-IDF based retrieval.
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Quantum Computing
Machine Learning & Data Science
AI & Agents
Tools & Infrastructure
Open to research collaborations in quantum computing, quantum machine learning, and AI for science.