Designed ML-driven pharmaceutical claim adjudication infrastructure processing millions of claims using Python AWS Lambdas with scalable LLM workflows
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Developed predictive analytics and LLM-based algorithms for prior authorization. Analyzing multi-modal documents with vision models to accelerate approval timelines
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Built RAG pipeline combining text extraction tools and generate structured LLM outputs with evidence
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Architected AI-powered prior authorization system using LLM to automate analysis of claims data and clinical documents
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Integrated real-time data pipelines with continuously optimized ML/LLM models, enabling proactive issue detection and dynamic compliance with evolving healthcare regulations
Developed and teach a comprehensive engineering course that introduces students to advanced modern computing tools and methods, with a strong emphasis on utilizing Python for numerical analysis, data visualization, and optimization techniques, integrating these skills into real-world engineering problems
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Designed the curriculum to incorporate machine learning concepts, enabling students to apply Python for predictive modeling, data-driven decision-making, and optimization of complex engineering systems
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Implemented hands-on projects that challenge students to develop algorithms for data analysis, visualization, and optimization, fostering a deep understanding of computational methods
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Introduced interdisciplinary approaches, blending engineering principles with modern data science practices, to equip students with the necessary skills to address emerging challenges in the field of engineering through computational solutions
Designed and implemented machine learning models to optimize trading strategies by analyzing vast financial datasets, identifying patterns, and predicting market trends with high accuracy, leading to significant improvements in trading performance
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Developed custom feature engineering techniques, extracting key indicators and market signals that enhance the predictive power of trading algorithms, reducing risk and increasing profitability
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Integrated reinforcement learning algorithms into trading systems, enabling adaptive strategies that continuously learn and evolve based on market behavior, resulting in more dynamic and responsive trading decisions
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Built a scalable backtesting framework, utilizing historical market data to rigorously test and validate trading models, ensuring their robustness and reliability under various market conditions
Led a multidisciplinary team of engineers in the development and implementation of process automation, leveraging Python for advanced data modeling and visualization, thereby optimizing project workflows and enhancing decision-making accuracy
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Pioneered integration of machine learning algorithms into data analysis processes, enabling predictive modeling and trend analysis to inform project management strategies
Led the design and development of a self-service monitoring and reporting system, utilizing real-time data processing to automate reporting tasks, which significantly increased productivity and reduced operational costs by 25%
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Engineered advanced data processing pipelines to handle real-time monitoring data, enabling immediate analysis and visualization, which improved decision-making speed and accuracy
