|Intro||Ethiopian computer scientist|
|Type|| Technology |
|Birth||1991, Addis Ababa, Ethiopia|
Rediet Abebe (Amharic: ረድኤት አበበ) is an Ethiopian computer scientist working in the fields of algorithms and artificial intelligence. She is a Junior Fellow at the Harvard Society of Fellows. Her research develops algorithmic and computational techniques to mitigate socioeconomic inequality. She co-founded Mechanism Design for Social Good (MD4SG), a multi-institutional and interdisciplinary research initiative working to improve access to opportunity for historically disadvantaged communities. She is also an advocate for diversity and inclusion in computing and is the co-founder for Black in AI.
Early life and education
Abebe was born and raised in Addis Ababa, Ethiopia. She was educated in the Ethiopian National Curriculum at Nazareth School before winning a merit-based scholarship awarded to four students from the country to attend the International Community School of Addis Ababa when she was in eighth grade.
Abebe joined Harvard University where she earned a Bachelor of Arts degree in mathematics and later a Master of Science degree in applied mathematics. As an undergraduate, she co-authored research papers in mathematics, physics, and public health. She completed her master’s degree from Harvard SEAS, conducting research with David C. Parkes.
After college, she attended the University of Cambridge as a Harvard-Cambridge scholar. She was the Governor William Shirley Scholar at Pembroke College. She completed the Mathematics Tripos and earned a Master of Advanced Studies in pure mathematics under the supervision of Imre Leader.
In 2015, Abebe started her doctoral degree in computer science at Cornell University as a researcher in theoretical computer science and artificial intelligence (AI), with a focus their applications to equity and social good concerns. She was advised by Jon Kleinberg. Abebe was the first Black woman to complete a Ph.D. in computer science at Cornell.
Research and career
Abebe’s research develops techniques in AI and algorithms to improve access to opportunity for historically under-served and disadvantaged communities. Her paper using search queries to understand health information needs in Africa is one of the first known works to employ large Web and social media-based analysis to study health across all 54 nations in Africa.
Throughout 2019 Abebe served on the National Institutes of Health Working Group on AI along AI experts including Kate Crawford, Dina Katabi, Daphne Koller, and Eric Lander. The working group was tasked with developing a comprehensive report and recommendations, which were unanimously approved by the Advisory Committee to the Director and NIH General Director Francis Collins.
Abebe was selected as a Junior Fellow at the Harvard Society of Fellows in 2019. She is the second Junior Fellow with a CS Ph.D. and first female computer scientist to be inducted into the Society.
Mechanism Design for Social Good
In 2016 Abebe co-founded the Mechanism Design for Social Good (MD4SG) initiative, a multi-disciplinary research collective that use algorithms and mechanism design to tackle inequality. MD4SG hosts an annual workshop series highlighting work and connecting the community of researchers committed to using algorithms to improve societal welfare. Abebe was honored as a pioneer in the 2019 MIT Technology Review’s Innovators Under 35 in part for her work co-founding MD4SG.
Black in AI
Abebe co-founded Black in AI, a network of 1,500 researchers working on AI, with Timnit Gebru, in 2016. The organization arranges annual workshops at the Conference on Neural Information Processing Systems (NeurIPS) and offers networking and collaborative opportunities. Through Black in AI, Abebe has spearheaded programs such as a graduate application mentoring program for which she was honored in the 2019 Bloomberg 50 list as a one to watch.
At Cornell University, Abebe has been involved with mentoring students from underrepresented groups. During her time at Cornell University, she helped with efforts that led to an unprecedented number of students from underrepresented minority students enrolling in the computer science doctoral program.