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Women in Data-Driven Discovery Conference Recap: On-Demand Videos & Photos | Stanford HAI
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Women in Data-Driven Discovery Conference Recap: On-Demand Videos & Photos

Date
March 13, 2025
Topics
Communications, Media

On March 6, 2025, at Stanford, the Women in Data-Driven Discovery Conference brought together an inspiring community of researchers and industry leaders to celebrate the achievements of women in data science, AI, and computational research. The event featured thought-provoking keynote talks, engaging panel discussions, and interactive sessions that explored cutting-edge methodologies and real-world applications across diverse fields. 

Speakers shared insights on the latest advancements in machine learning, biomedical data science, ethics in AI, and data-driven policy, sparking meaningful conversations and new collaborations. Attendees had the opportunity to network, exchange ideas, and strengthen the growing community of women driving innovation in data science. The energy and enthusiasm throughout the day underscored the importance of fostering inclusivity and advancing research that pushes the boundaries of discovery.

You can relive the moments through our session videos on our YouTube channel and enjoy some of the event photos below. 

On-Demand Videos

  • Opening Remarks: Laura Gwilliams

  • Stanford Panel: Data-Driven Discovery in Research, Carla Pugh, Diyi Yang, Madeleine Udell, Risa Wechsler; Moderator: Laura Gwilliams

  • Research Talk: Judith Fan

  • Lightning Talk—Towards Concept-Aware Large Language Models, Chen Shani

  • Lightning Talk—Synthetic Control in Disaggregated Data Settings, Lea Bottmer

  • Lightning Talk—Faithful synthetic data generation via penalized optimal transport network, Sophia Lu

  • Enhancing Student Learning in the AI Era: A Data-Driven Approach to AI Policy Enforcement, Diane Woodbridge

  • Closing Remarks: Julia Palacios

See below for a small gallery of images from the day, courtesy of David Gonzales Photo.

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This article is a part of the Stanford Data Science legacy publication. Read more about the HAI and Stanford Data Science merger.

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