Introduced a new framework for frequentist, optimization-based intervals that provably achieve desired coverage in ill-posed inverse problems without introducing a prior distribution [AoS, under revision]. The framework unifies many previously proposed optimization-based intervals and disproves a conjecture dating back to 1965. Explored sampling techniques based on the frameworks, with applications in particle physics problems and satellite data from NASA Jet Propulsion Laboratory (preprint in progress).
Introduced the concept of data-likelihood constraints in the two-player zero-sum game setting of Wald, which makes solving the game computationally tractable and helps navigate the inherent accuracy-robustness tradeoff of statistical inference [JCP]. The output of the algorithm is a robust estimate and a certificate of worst-case optimality. Applied the framework earthquake number prediction in the Groningen region in the Netherlands, introducing the notion of aleatoric and epistemic uncertainty to the field [Seismological Research Letters].
Developed methods for learning and solving PDEs [JCP, under review], Opeator Learning [JCP], and Measure Transport [SIMODS, under review], showing in all cases that Gaussian Processes lead to algorithms with stronger convergence guarantees than every other Machine Learning method without sacrificing accuracy. Also used GP-based methods to encode information-theoretic information in RNA classification application [Physica D]
Introduced a method to discover the structure of computational graphs using the variance decomposition properties of Gaussian Processes [PNAS]. We apply this to chemistry and biology examples, and to control of a lunar rover through an ongoing collaboration with NASA JPL.