Thomas Athey

Thomas Athey’s research focuses on novel machine-learning approaches for processing and analyzing image data. In his doctoral work, Thomas developed an approach for computationally tracing entangled neurons in whole-brain images, which outperformed state-of-the-art methods. Additionally, he studied the numerical properties of representing neuron traces as splines. Presently, his research has brought him to two other imaging settings. The first is electron microscopy, where Thomas is part of a team working to build a microscope that uses machine learning to guide image acquisition, accelerating overall imaging time. This work has the potential to greatly expand the scale of connectomics, which is the field concerned with mapping connections in the brain. The second is image segmentation and generative modeling of neuronal cell culture images. As an MIT-Novo Nordisk Artificial Intelligence Postdoctoral Fellow, Thomas will be working to identify cellular subtypes of amyotrophic lateral sclerosis in human motoneuron cell lines, with a goal of developing more comprehensive analysis tools of high-throughput cell culture imaging for impact in biological and pharmacological research.