Kathleen Ashbaker

My journey in data science began when I was a statistician for a local health department, when I was entrusted to make predictions about the prevalence and spread of West Nile Virus in my jurisdiction.  This role sharpened my statistical analysis skills, which later became the foundation for my expertise in mathematical modeling. As I transitioned into the role of a bioinformatician at the University of California San Diego, I used these skills to develop computational models as well as invaluable visualizations  using advanced machine learning techniques. During this period, I constructed my first convolutional neural network (CNN) decision algorithm, designed to classify patient ECGs as normal or abnormal. This project was my initial foray into integrating complex AI solutions into practical healthcare applications.

Currently, I am leading a critical project at the University of Washington, developing a CNN for breast cancer triaging. This project focuses on backend API integration using object-oriented programming languages and involves optimizing the model's performance through rigorous testing protocols. My role underscores my initial focus on advancing medical informatics, emphasizing the practical application of AI to improve diagnostic accuracy and enhance clinical decision-making processes. This expertise is invaluable not just in healthcare but across various industries where data-driven decision-making and efficient process automation are crucial.

Throughout my career, I have consistently aimed to integrate AI-driven tools into healthcare systems to enhance diagnostic procedures and streamline workflows. My ability to apply complex algorithms and machine learning models extends beyond healthcare, offering significant benefits in industries like finance, retail, and public sector administration, where predictive analytics and process optimization can transform operational efficiencies and strategic outcomes.

My educational journey began in microbiology, where I first cultivated my analytical skills. However, I soon discovered a strong affinity for statistics and data, prompting me to expand my academic pursuits. Throughout my high school and early college years, I delved into numerous computer science courses, including programming in C, C++, C#, Java, and JavaScript. I also mastered data-centric languages like R, SQL, and Python, and gained proficiency in web development with HTML and CSS.

Further enhancing my technical repertoire, I pursued advanced studies in data structures and analysis, which laid the groundwork for my later specialization in fields such as machine learning and artificial intelligence. My education also encompassed user experience design, project management, and methodologies such as Agile, Six Sigma, and Lean, reflecting my commitment to comprehensive skill development.

To bridge my technical expertise with healthcare, I took specialized courses in clinical informatics, electronic health records (EHRs), and software as a service (SaaS), which equipped me to navigate and innovate within the evolving landscape of medical technology.