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Women have played a pivotal role in shaping the field of data science, yet their contributions have often gone unrecognized. In an article published in the June 2025 issue of Patterns, a top data science journal from Cell Press, researchers from the University of Hawaiʻi at Mānoa highlight the achievements of women in the field and propose steps toward a more inclusive data science community.
Lauren Higa, a PhD student in the Molecular Biosciences and Bioengineering Program, and her advisor, Dr. Youping Deng, Professor and Director of the Bioinformatics Core at the John A. Burns School of Medicine (JABSOM), co-authored the piece titled “Highlighting the Achievements and Impact of Women in Data Science.” The article pays tribute to historical pioneers such as Ada Lovelace, Florence Nightingale, and NASA mathematicians Katherine Johnson, Dorothy Vaughan, and Mary Jackson—women who have played critical leadership roles in the development of modern data science. It also highlights the work of contemporary leaders, including Fei-Fei Li, whose ImageNet project helped transform artificial intelligence and computer vision in the past decade.
Despite these contributions, women account for only 23% of the global data science workforce, according to a 2024 report by Anaconda. The authors argue that the lack of varied perspectives limits innovation, perpetuates bias in algorithms, and reduces the field’s ability to meet the needs of the entire community. In response, Higa and Dr. Deng propose practical strategies to enhance the representation of women in data science. These include expanding mentorship programs, ensuring fair opportunities for publication and authorship, and implementing policies that support work-life balance and career development.
By reflecting on the legacy of past pioneers and proposing institutional strategies, researchers from JABSOM’s Bioinformatics Core aim to strengthen the real-world impact of data science by supporting broader participation in the field. To read the full article in Patterns, click here
