Women in Machine Intelligence Dinner schedule

Nina D’Amato is the Chief of Staff at the San Francisco Department of Technology, The San Francisco Department of Technology is an enterprise information and technology services organization that supports approximately 35,000 employees and 56 departments of the City and County of San Francisco. With an estimated operating budget of $120 million, the department’s portfolio includes the operations and maintenance of IT services essential for City operations through its management of City data centers, telecommunications, Fiber WAN, SFGovTV, cybersecurity, web services, public safety radio system maintenance, application support for citywide services such as email and telephone services, and installation and maintenance of over 240 miles of underground fiber-more underground fiber than any city in the country or private sector organization. Nina focuses on strategic plan development, implementation, and performance. She oversees the Project Management Team, Geographic Information Systems group, and the Salesforce team. She is a Lieutenant Colonel in US Marine Corps (Reserve) with combat service in Afghanistan and Iraq. While serving in Afghanistan with the US Marines during 2009-2011 she, in coordination with the Government of Afghanistan, US State Department, United Nations, and other non-government agencies and private organizations, implemented Afghanistan’s National Education Strategic Plan over the two southwest provinces of Helmand and Nimrooz. Upon leaving, approximately 22,000 Afghan students were beginning school for the first time in 30 years—including close to 1,000 girls. She was selected to serve as a US Department of Defense Congressional Fellow with Senator Robert Casey of Pennsylvania where she managed a $100M defense appropriations portfolio; centered on research, development, testing, and evaluation of emerging technologies.

She is a current member of the San Francisco Fleet Week Association and California Alumni Association Boards of Directors. In her spare time, she is completing her dissertation at UC Berkeley, focused on innovation and designing interventions that increase the capacity and technical fluency of the organization’s workforce. She lives in San Francisco, CA and is a native of Santa Rosa, graduating from Santa Rosa High School in 1991.

Understanding Content in News Feed

At Facebook they strive to provide a personalized experience to everyone. They have done a pretty good job in doing so by looking at people-to-people interaction but haven't invested much in understanding the content itself. In this talk Annie will talk about how Facebook approach the content understanding problem by combining signals from text, images, videos, and audios.

Annie is a Research Scientist working on modeling user interest at Facebook with the goal to provide a more personalized newsfeed experience. Previously, Annie worked on improving the quality of the entity graph of Facebook with the magic of machine learning and crowdsourcing.

Deep Learning for Search Relevance

Facebook Search is a product which helps people finding what the world is saying about topics which matter to them. Given that the index has more than 3 trillion posts and more than 2B highly personalized queries, the relevance (query <-> document) is a challenging problem. Through the application of deep neural networks for learning-to-rank methods, the need for extensive feature engineering was obviated.

Christina Scheau is an Engineering Manager at Facebook, with expertise in artificial intelligence, search engines and natural language processing.

Interpret with Concept Activation Vectors

Been Kim is a research scientist at Google Brain. Before joining Brain, she was a research scientist at Institute for Artificial Intelligence (AI2) and an affiliate faculty in the Department of Computer Science & Engineering at the University of Washington. Her research focuses on building interpretable machine learning. The vision of her research is to make humans empowered by machine learning, not overwhelmed by it. She received her PhD. from MIT. Prior to her PhD, she worked at the MathWorks as a software engineer.

Learning to be Relevant, Enabling Life-long Learning Through AI

Online learning platforms have grown tremendously in recent years, having an impact from K-12 to lifelong learning. Come learn about Recommender System theory applied into practice to the domain of Online Education. This talk presents the algorithms behind course recommendations with insights drawn from large scale A/B Testing experiments.

Shivani Rao is a Senior Applied Researcher working in the Learning Relevance group at LinkedIn. Shivani has accrued research experience in Industry and academia in areas of Machine Learning, Data Mining and Computer Vision. Outside of R&D work, Shivani loves engaging with the larger technical community, via writing and speaking and serving on the organizing committee of workshops and conferences. Shivani is also passionate about mentoring and supporting women in tech.