Runzi Mu

• Conducted detailed research and evaluation tasks in a web-based environment
• Verified and compared AI generated data, ensuring relevance and accuracy of information
• Adhered to guidelines for each task, maintaining consistency and accuracy
• Demonstrated flexibility by working on a freelance basis with a flexible schedule
• Engaged in diverse task types, including Apple Maps, Apple Podcasts, Apple Siri, News, Audio tasks, and other Relevance assessments

Developed a Home Range analysis Project to improve Texas Whitetail Deer Conservation Strategies by utilizing advanced statistical techniques and GIS methodologies.

• Managed data acquisition, imported, converted, integrated, tested and implemented a complex dataset of over 4,000 rows, significantly improving data accuracy for precise mapping of whitetail deer home ranges
• Utilized advanced statistical techniques such as Kernel Density computation to achieve an impressive 99.5% prediction accuracy in determining whitetail deer movement patterns, offering valuable insights for conservation strategies in Northern Mexico
• Employed Esri software and Geographic Information Systems (GIS) methodologies to identify and address outlier data points, ensuring the reliability and integrity of the analysis for informed conservation strategies
• Collaborated within a multidisciplinary team, department Professor and stakeholders, including wildlife ecologists and GIS specialists, enhancing ecological insights and promoting interdisciplinary excellence
• Link to the Project: http://bit.ly/3LuQi6u