Rythama Chevendra

- Engineered a convolutional neural network (CNN) to track proton movement by leveraging advanced regression model techniques. - Significantly improving data precision and contributing to breakthroughs in quantum computing research. - Collaborated closely with physics Ph.D. students to integrate sophisticated machine learning models for quantum-level particle tracking, driving research efficiency and setting the stage for future innovations in quantum technology.

- Co-led the architectural redesign of a conversational AI chatbot to adopt the Agentic AI framework through Model Context Protocol (MCP) integration, enabling autonomous agent workflows and improved contextual reasoning. - Implemented dynamic HTTP routing across Spring Boot and Flask microservices to orchestrate multi-provider AI workflows with Snowflake and Azure, improving system scalability and modularity.

- Led data aggregation and analysis within a SCRUM team, leveraging PySpark to transform financial datasets into actionable insights that guided strategic decision-making. - Responsible for processing and transforming data using PySpark, with expertise in handling .parquet files. - Improved development (CI/CD) workflows by applying Agile methodologies, accelerating delivery timelines and boosting long-term data processing efficiency.

- Led the development of an AI-driven academic planning tool by integrating a LangChain model with AWS cloud services, resulting in a dynamic chatbot that simplified course and extracurricular scheduling for students. - Implemented a LangChain model to integrate an LLM feature using AWS. - Employed Selenium and AWS Boto3 for real-time web scraping, ensuring up-to-date course and club data and delivering a solution that continues to enhance academic planning accuracy and accessibility. - Leveraged AWS for cloud services and integrated LangChain with the front-end.

- Engineered the final neural network layer for a Covid-19 radiology scan classifier, utilizing deep learning techniques to boost diagnostic accuracy and support faster clinical decision-making. - Performed ETL operations on large-scale datasets with Pandas and NumPy from public sources like MIDRC, establishing a reliable data pipeline to support model training and evaluation. - Created insightful UMAP visualizations to analyze model predictions across demographics, revealing trends that inform public health strategies and drive long-term improvements in diagnostic protocols.

- Led a ML model deployment feasibility project with AWS IoT Greengrass to seamlessly connect advanced algorithms between the College and local hospitals, enhancing data management and elevating healthcare delivery for lasting operational impact. - Achieved AWS Cloud Practitioner Certification, demonstrating deep proficiency in cloud computing that laid the groundwork to utilize cloud based solutions for ML models deployment. - Engaged in a pioneering project utilizing AWS IoT Greengrass, which facilitated seamless end-to-end connections for advanced algorithms. This project bridged the gap between The American College of Radiology and local hospitals, enhancing healthcare delivery through improved data management and algorithm deployment.