Since 2006, the annual workshop for Women in Machine Learning (WiML) has brought together female researchers in industry and academia, postdoctoral fellows, and graduate students from the machine learning community to exchange research ideas and build mentoring and networking relationships. The one-day workshop has been especially beneficial for junior graduate students, giving them a supportive environment in which to present their research (in many cases, for the first time) and enabling them to meet peers and more senior researchers in the field of machine learning. The networking opportunities provided by the workshop have also helped senior graduate students find jobs following graduation.

Intellectual Merit: This workshop will advance machine learning knowledge and foster collaboration within the machine learning community. As invited speakers, established researchers at top universities and research labs will teach workshop participants about cutting-edge ideas from diverse areas of machine learning. Students will present their own research and receive valuable feedback from both senior researchers and their peers. By enabling women at all stages of their careers in machine learning to exchange research ideas and form new relationships, we expect that new connections and research collaborations will be established, thereby advancing the state-of-the-art of the field.

Broader Impact: This workshop will provide a forum for female graduate students, postdoctoral fellows, junior and senior faculty, and industry and government research scientists to exchange research ideas and establish networking and mentoring relationships. Undergraduates, particularly those who are interested in pursuing graduate school or industry positions in machine learning, are also welcome to attend. Bringing together women from different stages of their careers gives established researchers the opportunity to act as mentors, and enables junior women to find female role models working in the field of machine learning. The workshop will also benefit the wider machine learning community: Firstly, the WiML website, which lists all previous workshop presenters, serves as a useful resource for organizations looking for female invited speakers. Secondly, co-locating with a major machine learning conference enhances the visibility of female researchers among the wider machine learning community. Thirdly, travel funding provided to workshop participants also facilitates their travel to the co-located conference, which for some participants would otherwise not be possible. Finally, all workshop materials (slides, abstracts, etc.) will be made available on the workshop website in order to ensure broad dissemination.

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Duke University
United States
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