Talks

The fundamental nature of dark matter and dark energy remains unknown and requires new physics to explain. This talk will present a cosmological quest to understand the properties of these two dominant but invisible components of our universe. I will present my work on early and late-universe searches for the identity of dark matter, and discuss cosmological limits on its mass, interactions with Standard Model, and production mechanisms, using the measurements of Big Bang Nucleosynthesis, Cosmic Microwave Background, 21-cm, and cosmic structure. Within the next decade, upcoming surveys will dramatically improve our measurements of these probes, enabling new insights into the invisible universe. If time permits, I will briefly introduce my work on cosmological searches for dark energy, as well as modified gravity.

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Recently, there has been much interest in black hole echoes, based on the idea that there may be some mechanism (e.g., from quantum gravity) that waves/fields falling into a black hole could partially reflect off of an interface before reaching the horizon. There does not seem to be a good understanding of how to properly model a reflecting surface in numerical relativity, as the vast majority of the literature avoids the implementation of artificial boundaries, or applies transmitting boundary conditions. Here, we present a framework for reflecting a scalar field in a fully dynamical spherically symmetric spacetime, and implement it numerically. We study the evolution of a wave packet in this situation and its numerical convergence, including when the location of a reflecting boundary is very close to the horizon of a black hole. This opens the door to model exotic near-horizon physics within full numerical relativity.

I introduce a unified framework for interpreting neural network classifiers tailored toward automated scientific discovery. In contrast to neural network-based regression, for classification, it is in general impossible to find a one-to-one mapping from the neural network to a symbolic equation even if the neural network itself bases its classification on a quantity that can be written as a closed-form equation. In this paper, I embed a trained neural network into an equivalence class of classifying functions that base their decisions on the same quantity. I interpret neural networks by finding an intersection between this equivalence class and human-readable equations defined by the search space of symbolic regression. The approach is not limited to classifiers or full neural networks and can be applied to arbitrary neurons in hidden layers or latent spaces or to simplify the process of interpreting neural network regressors.

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