Enriching Creativity in Applied AI with Interdisciplinary Backgrounds and Diverse Perspectives
Today AI is being created by a very homogeneous set of people. Not only it is homogeneous in terms of race and color but also in terms of academic backgrounds. I am a very unlikely researcher to become an AI engineer but I believe it is crucial so that AI created by heterogenous set of people is more fair and inclusive. I will be sharing my personal journey into AI coming from engineering, applied mathematics, biophysics and music background. I will also be sharing my machine learning project at AT&T: how I am utilizing machine learning to detect network outage and prevent unnecessary dispatch in an event of network outage.
Himani Agrawal, PhD is a Machine Learning Engineer at AT&T Chief Data Office where she is applying machine learning in the telecommunications industry to proactively predict network outage and avoid customer dispatch. She has a very interdisciplinary research background encompassing fields such as applied mathematics, biophysics and data science. Apart from work, she is passionate about promoting women in technology and actively participates with the Anita Borg Institute, Women in Machine Learning and Data Science, Women in Machine Learning and Society of Women Engineers. She is actively involved in the machine learning and data science communities at AT&T: Data Night Live and Data Powered Insights. She has had speaking engagements at international conferences like Grace Hopper Celebration of Women in Computing, Society of Women Engineers Annual Conference, American Society of Mechanical Engineers and Society of Engineering Science Annual Technical Meeting. She is passionate about Italian Classical singing and currently trains with University of North Texas Voice Professor Dr. Lauren McNeese.