Jing Guo

Research Interests:

•  Graph learning
•  Nonparametric regression and functional data analysis
•  Relative survival for cancer research

CURE-E Course: Analytic Geometry and Calculus (MATH 120), Fall 2024 

Project title: The Relationship Between Windspeed and Dynamics of Wildfire Spreading 

Wildfires pose a significant threat to ecosystems, human life, and property. Understanding the factors that contribute to the spread of wildfires is crucial for effective management and mitigation strategies. This project aims to investigate the relationship between wind speed and the dynamics of wildfire spreading. By analyzing historical wildfire data in conjunction with meteorological records, students will identify patterns that can inform predictive models and enhance wildfire response efforts.

This project aligns with the student learning outcomes of the course by engaging students in real-world applications of mathematics and technology, fostering critical thinking, and promoting an entrepreneurial mindset.

Cultivating an Entrepreneurial Mindset Outcome Support: The project encourages students to think critically about the implications of their findings in the context of wildfire management and environmental conservation. They will be prompted to consider how their research could lead to innovative solutions for wildfire prediction and response strategies. Students will develop an entrepreneurial mindset by identifying opportunities for application and improvement in existing systems. They may explore potential partnerships with local fire departments or environmental organizations, fostering a sense of initiative and collaboration. This experience will empower them to think creatively about how to apply their mathematical knowledge to address societal challenges.

════════════════════════════════════

CURE-E Course: Advanced Topics in Data Science (MATH/CSCI 485), Spring 2024

Project Title: Data science modeling of the Surveillance, Epidemiology, and End Results (SEER) data to explore predictive factors for cancer survival

The proposed CURE-E project encourages students to explore predictive factors for cancer survival using a real-world dataset Surveillance, Epidemiology, and End Results (SEER). Upon successful completion of the CURE-E project, students will be able to provide the following new knowledge to our discipline: