I am currently a postdoctoral researcher at Microsoft Research, New York City in the FATE (Fairness Transparency Accountability and Ethics in AI) group.
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. The Economist and others have recently covered part of this work. Some of the computer vision areas I am interested in 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
CVPR BigVision 2015
ECML PKDD 2013
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.