THANMAYEE YELURI

Engaged in bioinformatics projects focused on DNA and RNA sequencing analysis, utilizing R for data preprocessing, statistical modeling, and visualization. Developed and applied custom scripts to handle large sequencing datasets, including tasks such as quality control, alignment, and variant calling. Leveraged packages like Bioconductor for differential expression analysis, and ggplot2 for data visualization. Collaborated with interdisciplinary teams to interpret genetic data, identify biomarkers, and contribute to research papers and presentations. Ensured reproducibility and scalability of analyses by implementing best practices in R programming, version control, and documentation.

• Utilized advanced statistical software to analyze large datasets, uncovering key insights for informed decisionmaking.
• Participated in regular meetings with project supervisors, providing progress updates and discussing potential challenges or areas for improvement.
• Assisted in manuscript preparation, contributing to the publication of influential articles in peer-reviewed journals.
• Managed multiple research projects simultaneously, ensuring timely completion and adherence to high-quality standards.
• Conducted research using sUAS and UAVs to monitor crop health, deriving insights from highresolution imagery for precision agriculture applications.
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  Applied sophisticated data analysis techniques to establish correlations between ground truth crop water stress measurements and NDVI from aerial imagery, emphasizing factors like BRDF calibration for enhanced accuracy.
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  Played a pivotal role in advancing precision agriculture, showcasing the potential of sUAS and UAVs for informed decisionmaking in crop management.

Agricultural Robot for Precision Farming
This project involves developing an autonomous agricultural robot coded in Python and C++ to perform tasks like weeding, hoeing, and soil analysis. The robot uses RTK GPS for precise navigation and integrates spectroscopic sensors for soil property analysis. Designed for environmentally friendly farming, it features a "follow-me" mode and high-precision soil analysis capabilities.

This project involves upgrading an air quality monitoring system from Arduino to Raspberry Pi 4 for enhanced data processing and wireless connectivity. It integrates multiple sensors (NH3, NO2, CO, etc.) to collect real-time data, displayed on a web-based dashboard using Python and Flask. Remote access and cloud storage are implemented for continuous monitoring and analysis.

• Conducted thorough data validation and cleanup, ensuring accuracy and reliability of datasets used for analysis.
• Optimized SQL queries for faster processing times, increasing overall productivity within the team.
• Enhanced data visualization capabilities for better decision-making with the use of advanced BI tools.
• Completed data cleaning and data validation of existing spreadsheets to promote robust data management platform, resulting in accurate data analysis and entry.