John Shovic, Ph.D.

Summary: Applying AI to a variety of problems including automation of lab equipment, appling AI techniques to epidemiology problems especially correlated with environmental conditions and data.

Minimum Classes: A coding class (not HTML or CSS)

Projects:
1) Using Qualitative Association Rule Mining, implemented as a genetic algorithm, to find correlations between environmental factors, such as smoke, pesticide usage, air quality, soil moisture, and meteorological data in the state of California on a per county basis, and case rates of coccidioidomycosis, otherwise referred to as Valley Fever.
2) Using LSTM and xLSTM neural networks to predict case rates of coccidioidomycosis, otherwise referred to as Valley Fever, based on environmental factors, such as smoke, pesticide usage, air quality, soil moisture, and meteorological data in the state of California on a per county basis.
3) Using Qualitative Association Rule Mining, implemented as a genetic algorithm, to investigate potential correlations between air pollution in major cities in the United States, and rates of asthma or other chronic lung diseases.