Angelica Willis
Speaker at AMLD Africa 2024
Angelica Willis is a software engineer and AI Researcher at Google who has a passion for leveraging artificial intelligence to advance the quality and longevity of human life. Her core work involves developing AI-driven healthcare solutions to help doctors make better decisions. Previous career adventures include machine learning research at Apple, NASA, and Bank of America.
Willis received a master's degree in computer science, with a focus in artificial intelligence, and in human-computer interaction, from Stanford University. Her graduate school research focused on AI-based tutoring systems designed to help K-12 students gain math, science, and reading skills outside of the classroom. She earned an undergraduate degree in computer science from North Carolina A&T State University.
Honored as an NSF Graduate Research Fellow, Willis is also a Google Women Techmaker Scholar, an Apple HBCU Scholar, and a Buick National Achiever. She is an advocate for computer science education and diversity in STEM, and was recognized by President Obama as a 2016 White House Champion of Change for Computer Science Education. In her free time, Angelica enjoys advising startups through the Google for Startups Founder's Academy program and cooking (both her own recipes and ones generated by an AI model she built).
At AMLD Africa
United States International University Africa, Nairobi, Kenya
Improving Global Access to Maternal Healthcare using AI and Ultrasound
Every day, hundreds of women around the world die from preventable causes related to pregnancy and childbirth. The majority of these deaths occur in Africa, and most could have been avoided through interventions identified via ultrasound examination. AI can dramatically reduce the amount of training required to conduct maternal and fetal health ultrasound, potentially expanding global access to screening and improving health outcomes in underserved communities and countries. This talk will demonstrate the utility of AI-interpreted fetal ultrasound, even when using portable, low-cost probes and novice operators with no prior ultrasound experience. Results show that our on-device models are non-inferior, and in many cases, outperform sonographers on two critical diagnostic tasks: estimating fetal age and fetal malpresentation.
