Vivian Evans
Professor Mathematics
Bryant & Stratton College
Contact: vivian @vivianevans.com
Over the years, I've had the privilege of teaching math to many students—from those returning to school after years away to aspiring nurses and future professionals. My years of teaching in the U.S. have been especially rewarding. I served as a Student Ambassador and a professional math tutor at Cuyahoga Community College. I provided academic support, mentored peers, and helped foster a welcoming, achievement-oriented learning environment. In this role, I tutored students in person and online, using WebEx to facilitate virtual study sessions and one-on-one support. I'm also familiar with Zoom and other virtual platforms, allowing me to adapt to various online learning environments easily. I hold a full-time faculty position as an Associate Math Professor at Bryant & Stratton College. I teach in-person and online classes using Microsoft Teams, providing flexible instruction tailored to diverse student needs.
My teaching philosophy is grounded in believing that every student can succeed in mathematics with the right support, encouragement, and learning strategies. I strive to demystify math by breaking complex topics into manageable concepts and using real-world examples that make the material relatable. I emphasize growth mindset, resilience, and critical thinking, encouraging students to solve problems and understand the "why" behind the methods. I believe that meaningful learning occurs when students feel safe, respected, and empowered—and my responsibility as an educator is to create that environment.
I am deeply committed to promoting equity in education, celebrating diversity, and creating inclusive classroom spaces. No one should feel left behind, unseen, or unheard. My classrooms welcome students from all backgrounds and walks of life. I prioritize culturally responsive teaching, inclusive dialogue, and the use of materials that reflect my students' diverse identities. I strive to recognize and address barriers to learning, advocate for accessibility, and foster a sense of belonging for everyone.
I aim to prepare my students not just to pass a course—but to apply mathematical thinking in their everyday lives and future careers. My ultimate goal is to inspire a love of learning, especially in students who may have struggled with math.
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.
Mathematical Association of America
Association for Women in Mathematics
National Council of Teachers of Mathematics
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