### I believe in promoting equality, celebrating diversity, and ensuring inclusive education. No one must get left behind, isolated, or excluded from quality classroom instruction. As a teaching professional, I strive to create a positive learning environment where everyone can enjoy the learning experience.

### Throughout my years of teaching math, it has brought me great joy to witness my students grow in their mathematical skills and self-assurance. I am deeply passionate about teaching math and aim to inspire a love for the subject in each of my students. To foster a positive and inclusive learning environment, I prioritize supporting, valuing, and respecting all of my students so that they may achieve their full potential inside and outside the classroom.

### EDUCATIONAL BACKGROUND

### 2021 Masters of Science in Mathematics, Cleveland State University (Cleveland, OH)

### 2019 Associate of Science, Cuyahoga Community College (Cleveland, OH)

### 2019 Associate of Arts, Cuyahoga Community College (Cleveland, OH)

### 2001 Masters of Science in Teaching Mathematics (27 Credit Hours), University of Science and Technology of Southern Philippines

### 1999 Bachelor of Science in Education Major in Mathematics, University of Science and Technology of Southern Philippines

### PROFESSIONAL EXPERIENCE

### September 2022 - Present Mathematics Professor, Bryant & Stratton College

### September 2018 - Present Math Tutor, Cuyahoga Community College

### September 2018 - May 2019 Student Assistant, Cuyahoga Community College

### March 1999 – August 2019 Math Tutor, Private

### March 2000 – August 2004 Highschool Math Teacher (Licensed Professional), Philippines

### MASTERS THESIS

### Application of Hidden Markov Model. Cleveland State University. Under the supervision of Dr. Luiz Felipe Martins.

In my master's thesis, I offer a detailed exploration of Hidden Markov Models (HMMs) in meteorology, focusing on precipitation analysis. I begin by outlining the theoretical foundations of HMMs, tracing their origins to the work of Andrei Markov, whose pioneering research on stochastic processes laid the groundwork for these models. I then explain the mathematical principles that make HMMs suitable for modeling complex weather systems, followed by a comprehensive literature review that examines advancements and challenges in applying HMMs to meteorological data, especially in identifying hidden weather states and transitions crucial for understanding and forecasting precipitation.

The core of my thesis is dedicated to the practical implementation of HMMs for analyzing and predicting precipitation patterns. I include detailed case studies demonstrating how HMMs, grounded in Markov's theories, can be effectively applied in meteorology. To further assist readers, I provide practical Python code examples throughout the thesis, guiding them through the entire modeling process—from data preprocessing and parameter estimation to model evaluation and interpretation of results. These examples are designed to offer practical insights, ensuring that readers feel confident in applying HMMs to their weather prediction models.

I conclude my thesis by discussing the implications of using HMMs to enhance the accuracy of weather forecasts and climate studies. I explore the potential integration of HMMs into existing meteorological frameworks, highlighting their benefits for short-term weather prediction and long-term climate analysis. My thesis aims to be a valuable resource for researchers, meteorologists, and data scientists interested in leveraging HMMs, built on the foundational work of Andrei Markov, for improved precipitation forecasting and climate studies.

### ORGANIZATIONS

Mathematical Association of America

Association for Women in Mathematics

National Council of Teachers of Mathematics

### LANGUAGES

English

Tagalog

Cebuano