![]() Bowman, MD, who died in September 2011 at 88, had just become the first tenured African American faculty member in medicine at the University. ![]() And in the well-intentioned zeal to roll out mass testing and raise awareness, little attention was being paid to the accuracy of information disseminated to the public and the ethical implications of screening on the scale that was being embraced, said an outspoken professor of medicine and pathology from the University of Chicago. The following year, he signed the National Sickle Cell Anemia Control Act, which authorized funding for screening, outreach and research to “reverse the record of neglect on this dread disease.”Īfter decades of inaction, there was a headlong rush to tackle sickle cell anemia. In February 1971, President Richard Nixon designated sickle cell anemia one of two critical areas for urgent investment under his proposed “National Health Strategy.” The other was cancer. On the streets, the Black Panther Party took matters into its own hands, utilizing a newly available testing kit to mobilize screening in African American communities, including in Chicago. Yet funding was a fraction of that for less prevalent disorders afflicting other groups, the author wrote. Roughly one in 500 African Americans was born with the condition, noted an article in the Journal of the American Medical Association that October. ![]() For patients, life expectancy was about 20 years. ![]() Can the computer algorithms be implemented in a biologically plausible neural network and can they contribute to generating hypotheses on how the visual cortex processes input? Can information gleaned about how cortex processes the acoustic and visual input contribute to improving the computer algorithms? I am also interested in other domains of brain function, specifically stochastic models for learning and memory, and encoding and decoding of neurons in the motor cortex.In 1970, there was no treatment for the blood disorder sickle cell anemia, and knowledge of how to manage it was rudimentary. After all the our cortex performs these tasks far better than any computer algorithm. Research in object recognition in vision and speech naturally raises questions about the relation between the computer algorithms and biological processing in the cortex. Although the speech recognition is a more mature field of research than vision, there are some interesting insights from vision that may contribute to increased robustness and stability of speech recognition algorithms. I am also interested in importing ideas developed in computer vision into the domain of speech recognition. The models have been implemented in concrete applications such as reading license plates on photos of cars, reading handwritten zipcodes, detecting faces, cars or various objects of interest in biological images and videos. Models for individual objects can be composed to create models for entire scenes. The simplicity and transparency of the statistical models enables training with small samples, and give rise to efficient computational methods. Although not extensively used in computer vision these emerge as a powerful tool in developing recognition algorithms which allow for proper modeling of object and data variability. The main focus of my research is the formulation of statistical models for objects. A primary goal of computer vision is to develop algorithms that can learn representations of objects from training sets and subsequently label digital images with instances of these objects.
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