PROF, ASOC -
In Engineering, visual assessment of infrastructure performance has been a typical practice. However, as one could imagine, it is time consuming and sometimes dangerous. Among many such tasks, visual assessment of rain gardens (which are many on campus and play critical roles in storm water management) is a representative one. Can machines replace us for doing this? Today, some artificial intelligence algorithms shed promise for a limited number of scene understanding problems. The golden rule is that computers have to start from somewhere or learn from existing captioned or labeled data (as known as training data). At Dr. Chen’s lab at UMKC, several smart apps with a cloud computing backend have been developed. Built atop of this, we are striving to build training data sets for training future machine learning algorithms that can assess rain gardens. Based on our smart apps, the training data sets will be automatically sent to the cloud. Further development is necessary for feeding these training data to an AI engine, which is, for example, Google’s cloud-based machine learning platform, TensorFlow.
Programming in Java and Python, knowledge in MongoDB, and a keen interest in machine learning or image understanding.
Logistics and Compensation
Eligible for SEARCH or SUROP grant. Other research grants and financial support maybe available depending on the skills of students.
Programming, testing, and photographing on campus
Mar 24, 2016
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