Zhane' McCleod

My name is Zhane (Jah-Nay) McCleod and I am a graduating senior at the University of Florida. I am currently majoring in Aerospace Engineering with a minor in Engineering Innovation. Some of my strong points include engineering innovation, engineering entrepreneurship, structural analysis, stability and controls and finite element analysis. 
I have recently worked with the Material Science and Engineering (MSE) Department at North Carolina State University. My tasks heavily depended on Python programming and material analysis. I've had to learn how to import mass data into Python to perform machine learning for data analyzation. I have noticed the growing need for programming in engineering, as most engineers are required to take at least one programming course. I've also worked with the MSE Department at the University of Florida as a support to the faculty in the department.
I am a peer advisor in the Mechanical and Aerospace Engineering Department at the University of Florida. As an advisor, I help students plan for their future. I also have involvement outside of the engineering department. I am a member of a campus ministry called BOLD Campus Ministry at UF. My goal in this campus ministry is to spread the message of Jesus Christ on campus. With hopes of helping people find peace and discover their true purpose in life.

Technical EditorTechnical Editor:

University of Florida 

I work on projects for research proposals, award funding, proposal referencing, and nuclear research. I also work closely with my supervisor. As the only student technical editor in the department, I am 
assigned at least 3 new tasks each week. Due to my supervisor’s busy schedule, I am often given tasks where I have to work alone to figure out a solution based on other resource

Summer InternSummer Intern:

North Carolina State University

The Research Education for Undergraduates (REU) Program allowed for a close working dynamic with a mentor and a principal investigator to work on a 10-weeklong project regarding material science/engineering and data science. 

I participated in a 2-week python boot camp to learn about data science in relation to coding. The most essential part of learning Python was to obtain the ability to import databases from various sources into Python. This was done to develop machine learning algorithms that could differentiate the data in the databases. 

I participated in three hour-long symposiums to present research to a crowd of engineers. During these symposiums, professors, researchers, and students asked questions regarding the summer research project. The research that was done was on the unsustainable ways that Phosphorus (P) is used today, and the ways in which P can safely be removed from wastewater for reuse. Linear Regression, K-Nearest Neighbors, Decision Trees, and Random Forests were the four examined machine learning methods. A challenge that needs to be overcome in the future is the challenge of sparse data. For machine learning algorithms, samples with missing data can’t be used to train the model. It was concluded that machine learning methods cannot be used to accurately predict unknown candidate P-recovery materials because of the amount of inconsistent parameters across the literature. Therefore, broader and more consistent datasets are desired. Despite all of this, the correlations between the materials can still be examined.The Research Education for Undergraduates (REU) Program allowed for a close working dynamic with a mentor and a principal investigator to work on a 10-weeklong project regarding material science/engineering and data science. I participated in a 2-week python boot camp to learn about data science in relation to coding. The most essential part of learning Python was to obtain the ability to import databases from various sources into Python. This was done to develop machine learning algorithms that could differentiate the data in the databases. I participated in three hour-long symposiums to present research to a crowd of engineers. During these symposiums, professors, researchers, and students asked questions regarding the summer research project. The research that was done was on the unsustainable ways that Phosphorus (P) is used today, and the ways in which P can safely be removed from wastewater for reuse. Linear Regression, K-Nearest Neighbors, Decision Trees, and Random Forests were the four examined machine learning methods. A challenge that needs to be overcome in the future is the challenge of sparse data. For machine learning algorithms, samples with missing data can’t be used to train the model. It was concluded that machine learning methods cannot be used to accurately predict unknown candidate P-recovery materials because of the amount of inconsistent parameters across the literature. Therefore, broader and more consistent datasets are desired. Despite all of this, the correlations between the materials can still be examined.

I graduate in May of 2023. I am an Aerospace Engineer with an Engineering Innovation minor. I have knowledge in project management, engineering leadership, structural analysis, MATLAB, engineering entrepreneurship, and finite element analysis.