About

Karen Feng is a software engineer at Databricks, where she is currently working on developing scalable healthcare solutions to accelerate genomic medicine and improve patient outcomes. With a passion for developing healthcare solutions, Karen has previously worked as a biostatistics researcher at Princeton University, where she developed multi-armed bandit algorithms motivated by experimental design in biology. Karen's work as a researcher has been published in the Thirty-second Annual Conference on Neural Information Processing Systems, where she presented her paper titled "PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits". Additionally, she developed a bandit-based method on single-cell RNA sequence data to improve the efficiency of cell sampling. Karen has also worked as a software engineering intern at Databricks, where she developed full-stack solutions for user groups and API tokens. With 4.87 years of relevant experience, Karen's technical skills include software engineering, research science, and Databricks. Karen holds a Bachelor's degree in Computer Science from Princeton University, where she graduated in 2018. Prior to that, she completed her high school education at Monta Vista High School in 2014.