As an environmental chemist and data scientist, Kimani measures contaminants in the environment, mines chemical and spatial data, then uses statistics and machine learning to describe the data.
Kimani studied chemistry and marine science at Hampton University because he received a scholarship, and he found that he liked the problem-solving aspect of working with equations. At NOAA, Kimani is part of the Great Lakes Mussel Watch, a group of scientists that collect mussels and document what they find inside, such as pharmaceuticals, oil, and other chemicals. They also evaluate the stressors that organisms may be facing using DNA and metabolomic measurements.
Because they are filter feeders, mussels make good indicators of the potential contaminants within an aquatic ecosystem. Kimani’s work in the Great Lakes region helps Mussel Watch inform the public about what is in the water and, through machine learning, to describe and predict pollution within Great Lakes ecosystems. What’s exciting about Kimani’s work, he says, is learning new things and staying on the cutting edge of his field.