Timnit Gebru

I am a PhD student in the Stanford Artificial Intelligence Laboratory, studying computer vision under Fei-Fei Li. My main research interest lies in data mining large scale publicly available images to gain sociological insight, and working on computer vision problems that arise as a result. Stay tuned for related news coming soon. Some of these include fine-grained image recognition, scalable annotation of images, and domain adaptation. Prior to joining Fei-Fei's lab I worked at Apple designing circuits and signal processing algorithms for various Apple products including the first iPad. I also spent an obligatory year as an entrepreneur (as all Stanford undergrads seem to do). My research is supported by the NSF foundation GRFP fellowship and currently the Stanford DARE fellowship


Using Deep Learning and Google Street View to Estimate the Demographic Makeup of the US

Timnit Gebru, Jonathan Krause, Yilun Wang, Duyun Chen, Jia Deng, Erez Lieberman Aiden, Li Fei-Fei

Under Review at PNAS 2017


Scalable Annotation of Fine-Grained Objects Without Experts

Timnit Gebru, Jonathan Krause, Jia Deng, Li Fei-Fei

CHI 2017


Fine-Grained Car Detection for Visual Census Estimation

Timnit Gebru, Jonathan Krause, Yilun Wang, Duyun Chen, Jia Deng, Li Fei-Fei

AAAI 2017


Visual Census: Using Cars to Study People and Society

Timnit Gebru, Jonathan Krause, Yilun Wang, Duyun Chen, Jia Deng, Li Fei-Fei

CVPR BigVision 2015


Learning Features and Parts for Fine-Grained Recognition

Jonathan Krause, Timnit Gebru, Jia Deng, Li-Jia Li, Li Fei-Fei

ICPR 2014


Drivers of Variability in Energy Consumption

Adrian Albert, Timnit Gebru, J Ku, J Kwac, Jure Leskovec, Ram Rajagopal



Invited Talks

Predicting Demographics Using 50 Million Images

CVPR LSVisCom 2015   AI With the Best 2016   AI In FinTech Forum 2017

Slides coming soon...

Other Stuff

Addis Coder: Algorithms and Programming for High Schoolers

An intensive 1 month summer class for high school students in Ethiopia, organized by Jelani Nelson. This is the most diverse/inclusive classroom I have ever been in. All regions of Ethiopia were represented with many religions and at least 10 languages (there were 85 students). There were different income levels ranging from students working as shoe shiners to put themselves through school to kids who went to private middle schools. All students currently go to public schools. The class had students from rural areas and cities, close to 50/50 female/male ratio and people with disabilities (e.g. Misgina who is deaf but is top of his class while going to a school that gives no resources for deaf people). Some kids had never touched a computer before while others have programmed in Java. But all of them currently understand the basics of recursion, dynamic programming, graphs etc. And they only took this class for one month. I hope to one day see a computer science classroom in the US that is this diverse.


More Mentorship

I am involved in various outreach/mentorship activities including EDGE (2014-2016), EJHS Scholars Program (this year) and SAILORS (2015).