Hannah Lawrence is interested in developing theoretical foundations for new algorithms at the interface of machine learning, data science, and signal processing, with potential impact in drug development and other applications.
Pursuing a PhD at the Massachusetts Institute of Technology, she is particularly interested in broadening our understanding of how structure and scientifically informed priors can be incorporated into learning problems and frameworks.
Hannah is currently a research analyst at the Center for Computational Mathematics of the Flatiron Institute in New York City, where she is developing symmetry-preserving neural nets for image processing in cryogenic electron microscopy, a widespread method for determining 3D structure of proteins.
A 2019 graduate of Yale University with a BS in applied mathematics and computer science, Hannah has researched various mathematical algorithms for data science, including optimal online optimization algorithms and spectral graph algorithms for ill-conditioned systems. She interned at Microsoft Research New England and at Reservoir Labs, where, in both cases, she developed sublinear sparse recovery methods for signal processing applications.
Hannah is a member of Phi Beta Kappa and a recipient of the Russell Henry Chittenden Prize, awarded to a single graduating senior at Yale in science, technology, engineering and mathematics.
In her free time, she enjoys reading, running, cooking, playing the violin and flute, and visiting her hometown of Port Jefferson Station, Long Island.
