What do you do with Data Science?
The primary focus of my research is on Machine Learning for extracting patterns and insights from scientific data in order to drive biomedical discovery. While much of AI has focused on matching known patterns for classification, there is a great need for using AI to find unknown patterns and to generate plausible scientific hypotheses. My work is at the intersection of several fields including applied math, deep learning, data geometry, topology, manifold learning, and graph signal processing, all serving to tackle key challenges in data science. The problems I address are motivated by the ubiquity of high-throughput, high-dimensional data in the biomedical sciences -- a result of breakthroughs in measurement technologies like single cell sequencing, proteomics, fMRI and vast improvements in health record data collection and storage. While these large datasets, containing millions of cellular or patient observations hold great potential for understanding the generative mechanisms, the state space of the data, as well as causal interactions driving development, disease and progression, they also pose new challenges in terms of noise, missing data, measurement artifacts, and the so-called “curse of dimensionality.” My research has been addressing these issues, by developing denoised data representations that are designed for data exploration, mechanistic understanding, and hypothesis generation.
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