About Fred Hutchinson Cancer Center

Carrie Wright, PhD, serves as a Senior Staff Scientist at Fred Hutchinson Cancer Center and holds an affiliated faculty position at Johns Hopkins Bloomberg School of Public Health, where she focuses on making data science and computational biology more accessible to diverse audiences. Her expertise spans multiple domains, teaching courses including "AI for Decision Makers," "AI for Efficient Programming," "Avoiding AI Harm," "Best Practices for Ethical Data Handling," "Data Management and Sharing for NIH Proposals," and "Write Smarter with Overleaf and LaTeX." Her distinguished career includes significant contributions as a former Assistant Scientist in Biostatistics at Johns Hopkins and postdoctoral research at the Lieber Institute for Brain Development, where she studied genetic mechanisms in psychiatric disease. As a member of the Open Case Studies team, the Genomic Data Science Community Network, and chair of the ITCR OPEN Group, she demonstrates her commitment to advancing science, medicine, and social justice through accessible data science education. Her innovative work includes co-founding the LIBD rstats club and teaching at various institutions, including the Baltimore Underground Science Space and Johns Hopkins Center for Talented Youth.

Ava Hoffman, PhD, serves as a Senior Staff Scientist at Fred Hutchinson Cancer Center, where she leads groundbreaking initiatives in making genomics research more accessible through cloud-based resources. Her unique expertise spans both ecological and data sciences, with particular focus on population genetics and statistical modeling in natural environments. As an instructor for multiple courses including "AI for Decision Makers," "AI for Efficient Programming," "Data Management and Sharing for NIH Proposals," and "Exploring AI Possibilities," she brings a practical approach to complex topics. Her research interests include urban plant evolution and its connection to public health outcomes, particularly studying how plants adapt to city environments. Beyond her academic work, she serves on the board of Slow Food Baltimore, demonstrating her commitment to sustainable practices. Her work with the AnVIL Project and the Genomic Data Science Community Network (GDSCN) showcases her dedication to democratizing genomics research. She combines her technical expertise with practical applications, engaging in activities from coding to climbing, while maintaining a DIY approach to problem-solving.

Candace Savonen has established herself as a dedicated educator and innovator in genomic data science, focusing on making complex bioinformatics tools and concepts accessible to diverse audiences. Her work centers on developing comprehensive educational materials that emphasize reproducibility and scalable teaching methods in bioinformatics and cancer genomics. As a key contributor to various educational initiatives, she has been instrumental in creating and delivering bioinformatics education materials specifically tailored for cancer genomics research. Her approach combines practical application with theoretical understanding, ensuring that learners can effectively apply genomic tools to their specific areas of expertise. Through her involvement in developing online learning modules and educational resources, she has demonstrated a commitment to breaking down barriers in genomic data science education, making these essential tools and knowledge more attainable for researchers and practitioners across different fields. Her teaching methodology emphasizes the importance of reproducible research practices and sustainable approaches to data analysis, contributing significantly to the democratization of genomic data science education.

Elizabeth Humphries, PhD, serves as a distinguished instructor at Fred Hutchinson Cancer Center, specializing in artificial intelligence policy and technical writing education. As an instructor for multiple courses including "AI for Decision Makers," "AI for Efficient Programming: Harnessing the Power of LLMs," "Developing AI Policy," and "Write Smarter with Overleaf and LaTeX," she brings expertise in bridging complex technical concepts with practical applications. Her teaching portfolio demonstrates her commitment to making advanced technological concepts accessible to diverse audiences, from decision-makers to technical practitioners. Through her comprehensive curriculum design, she addresses both the technical aspects of AI implementation and the critical policy considerations that shape its responsible development. Her work focuses on empowering professionals to understand and navigate the intersection of artificial intelligence, policy development, and technical documentation, while emphasizing practical skills in programming and scientific writing using tools like LaTeX.

Emma Pierson serves as a distinguished instructor at Fred Hutchinson Cancer Center and is a computer scientist at Cornell University, where she specializes in using AI and data science to address health disparities across demographic groups. Her academic credentials include a bachelor's in physics and master's in computer science from Stanford, a master's in statistics from Oxford as a Rhodes Scholar, and a doctorate in computer science from Stanford. As the instructor for "Practical Steps for Building Fair AI Algorithms," she brings her expertise in developing ethical AI solutions that address systemic inequalities in healthcare and social systems. Her groundbreaking research includes analyzing COVID-19 transmission patterns, racial disparities in traffic stops, and women's health data across 109 countries. Beyond academia, she contributes to major publications like The New York Times and The Atlantic, making complex data science concepts accessible to broader audiences. Her work has directly influenced policy changes, including the Los Angeles Police Department's revision of their stop practices and state health departments' COVID-19 reopening strategies

Kowe Kadoma serves as an instructor at Fred Hutchinson Cancer Center, specializing in ethical AI implementation and algorithmic fairness. As an instructor for "Practical Steps for Building Fair AI Algorithms," she focuses on teaching professionals how to develop and implement AI systems that promote equity and fairness across diverse populations. Her course curriculum emphasizes practical approaches to understanding and addressing bias in AI algorithms, covering crucial topics such as ethical data collection, model training principles, and healthcare algorithm design. Through her teaching, she helps students master the complexities of algorithmic fairness definitions, transparency in AI systems, and the challenging ethical dilemmas that arise in AI implementation. Her approach combines theoretical understanding with hands-on application, ensuring students gain practical skills in designing and implementing fair AI solutions while considering their broader societal impact.