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 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 collects mussels and documents what they find inside, including pharmaceuticals, oil, and other chemicals. They also assess the stress of the organisms use 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 tell the public what is in the water, and machine learning to describe and predict pollution. What’s exciting about Kimani’s work, he says, is learning new things and staying on the cutting edge of his field.