The following quote is from ChatGPT's memory feature, which remembers my preferences and past interactions to personalize responses. According to ChatGPT, this is who I am:
"[Jessica] is knowledgeable in probability, statistics, linear algebra, calculus, machine learning fundamentals, programming in Python, and using deep learning libraries. They also have experience with GPUs and TPUs for computing, and data handling and preprocessing."
There was a time when I thought math wasn't for me, but today I have extensive knowledge of machine learning and neural networks. As a child, I believed quantitative sciences were only for "smart kids." After high school, I worked in retail, where I discovered I had a knack for math—handling cash quickly and accurately was deeply satisfying. Inspired, I returned to school to study math.
Starting with basic arithmetic, I fell in love with numbers and pushed past the limits I once felt. I progressed rapidly, from arithmetic to multivariable calculus, driven by a pure desire to learn. A logic class introduced me to proofs, sparking a new way of thinking and naturally leading me to programming, where math and logic seamlessly came together.
I eventually transferred to a four-year university and majored in statistics. During this time, I worked as a tutor and teaching assistant, helping others overcome the same belief I once held—that math wasn't for them. My foundation in statistics and passion for programming opened the door to machine learning and data science. I joined the Data Science Club at California State University, East Bay, where I served as vice president and later president. It was during this time I discovered my love for Natural Language Processing (NLP) and the potential of textual data, guiding me toward the world of Large Language Models (LLMs).
When it came time to apply for graduate school, I knew exactly where I wanted to go: the University of San Francisco. I was accepted into their Master of Science in Data Science program, with a competitive 17% acceptance rate that year. The program was rigorous, but I thrived, balancing my studies with a role as a contracted data scientist at Pacific Gas and Electric Company. There, I applied my skills to train a neural language model using the company’s internal knowledge base.
Now, I am eager to transition into a full-time data science role. I’m excited to continue growing, leveraging my experience in NLP, machine learning, and statistics to solve real-world problems. From struggling with math as a child to where I am today, I’m proud of the journey that brought me here and excited for what comes next.