KAUST hosted a regional Women in Data Science (WiDS) conference, part of a global event held at over 100 regional institutions led by Stanford University. The KAUST event featured exclusively female speakers and aimed to highlight data science research and applications. KAUST is launching a 'Women in Data Sciences and Technology' initiative to support women's education and careers in the field. Why it matters: This initiative can help address the underrepresentation of women in data science in Saudi Arabia and the broader region.
MBZUAI's Women in AI (WAI) club, founded by master’s students Asma Hashmi and Ameera Bawazir, aims to increase female representation in AI at MBZUAI and the UAE. The club aligns with the International Day of Women and Girls in Science, addressing the underrepresentation of women in AI globally (22%). MBZUAI reports 31% female students in its first cohort and hopes to increase this, supported by faculty like Prof. Najwa Aaraj. Why it matters: This initiative highlights efforts to close the gender gap in AI within the UAE's leading AI university, fostering a more inclusive and diverse tech ecosystem.
A 2016 KAUST Winter Enrichment Program seminar, "Women in Science and Engineering," convened female scientists from KAUST and abroad. Panelists like Jasmeen Merzaban and Charlotte Hauser shared their career experiences and addressed challenges faced by women in STEM. They noted that women constitute 60% of higher education graduates in Saudi Arabia and will be vital to the Kingdom's knowledge economy. Why it matters: The event highlights the increasing role of women in Saudi Arabia's STEM fields and KAUST's commitment to supporting female scientists.
KAUST is launching the "Dear AI" campaign and hackathon to address gender bias and under-representation of women and Saudi/Arab people in AI, after finding AI image tools return only 1% women for prompts like "imagine entrepreneur." The campaign calls for accurate representation in AI datasets from Saudi Arabia and beyond. KAUST notes that 47% of graduates in their AI academy are women. Why it matters: This campaign highlights the need for more inclusive AI training data and addresses gender imbalances in STEM fields in Saudi Arabia.
This paper focuses on analyzing surveys of women entrepreneurs in the UAE using machine learning techniques. The goal is to extract relevant insights from the data to understand the current landscape and predict future trends. The study aims to support better business decisions related to women in entrepreneurship.
MBZUAI is highlighting five female leaders in AI for International Women’s Day, noting its 28% female student body. Dr. Farida Al Hosani is developing an AI healthcare solution for non-communicable diseases and was appointed VP of MBZUAI’s Alumni Advisory Board. Dr. Hanan Aldarmaki focuses on improving Arabic automated speech recognition and recently won an award for a paper on Arabic speech processing. Why it matters: Showcasing women in AI leadership helps promote diversity and inclusion in the field, especially in the context of the rapidly growing AI ecosystem in the UAE.
KAUST's 2018 Winter Enrichment Program (WEP) featured a significant number of female speakers, highlighting the growing role of women in STEM. Events like "The Rise of Nanomachines" and "Women in STEM" provided platforms for female scientists to share their work and experiences. A "Speed Mentoring" session facilitated mentor-mentee relationships between graduate students and women in STEM at KAUST. Why it matters: Such initiatives help to foster a supportive environment for women in science and engineering within Saudi Arabia's leading research university.
This study analyzes the evolution of data science vocabulary using 16,018 abstracts containing "data science" over 13 years. It identifies new vocabulary introduction and its integration into scientific literature using techniques like EDA, LSA, LDA, and N-grams. The research compares overall scientific publications with those specific to Saudi Arabia, identifying representative articles based on vocabulary usage. Why it matters: The work provides insights into the development of data science terminology and its specific adoption within the Saudi Arabian research landscape.