As time goes on, our interaction with large datasets tends to be more involved and less intuitive. Most datasets can no longer be visualized in their entirety and we have to interact with them mathematically, making blind data exploration challenging. In this talk I will present some theoretical considerations of data analysis and data complexity, and I will show that performing a "cooling" operation on data can automatically reveal the inherent structure of a dataset and the properties of its underlying population. I will then present a new tool, the Sequencer, which can generically perform such a cooling operation on arbitrary datasets. I will show that it provides a new way to explore complex datasets and restore visual insight.

Almost two decades of observations from the first major X-ray Observatories, launched by NASA and ESA in 1999, have revolutionized our knowledge of the X-ray Universe. X-ray data is being utilized by a large fraction of the astronomical community and has transitioned into the mainstream of astronomical research. The science and discoveries of these two great observatories has been significantly enhanced by smaller missions which complement and broaden the observing opportunities worldwide. I will review some of the major discoveries and breakthroughs in our understanding of celestial sources resulting from X-ray observations with the exquisite (~0.5 arcsec) spatial resolution of NASA's Chandra X-ray Observatory, one of NASA's fleet of Great Observatories.

X-ray Observatories enable us to see the otherwise invisible. However broad progress relies on multi-wavelength, and recently multi-messenger, data which reveal multiple aspects of celestial sources. Our current space observatories will continue to operate for 5-10 more years, but to continue our quest to understand more about our Universe, we need to make plans for maintaining our multi-wavelength view. I will close with a look towards X-ray astronomy beyond Chandra.

Over the course of the past two decades, observational surveys have un-veiled the intricate orbital structure of the Kuiper Belt, a field of icy bodies orbiting the Sun beyond Neptune. In addition to a host of readily-predictable orbital behavior, the emerging census of trans-Neptunian objects displays dy- namical phenomena that cannot be explained by interactions with the known eight-planet Solar System alone. Specifically, the observed physical cluster-ing of orbits with semi-major axes in excess of ~250 AU, the detachment of perihelia of select Kuiper belt objects from Neptune, as well as the dynamical origin of highly inclined/retrograde long-period orbits remain elusive within the context of the classical view of the Solar System. This newly outlined dynamical architecture of the distant solar system points to the existence of planet with mass M9 ~ 10M⊕ on a moderately inclined orbit with semi-major axis a9 ~ 400-800 AU and eccentricity e9 ~ 0.4-0.6. In this talk, I will review the observational motivation, dynamical constraints, and prospects for detection of this proposed object known as Planet Nine.

Many of the multi-planet systems discovered around other stars are dynamically packed to capacity. This implies that orbital integrations with masses or orbital parameters too far from the actual values will destabilize on short timescales; thus, long-term dynamics allows one to constrain the orbital architectures of many closely packed multi-planet systems. I will present a recent such application in the TRAPPIST-1 system, with 7 Earth-sized planets in the longest resonant chain discovered to date. In this case the complicated resonant phase space structure allows for strong constraints. A central challenge in such studies is the large computational cost of direct integrations, which preclude a full survey of the high-dimensional parameter space of orbital architectures allowed by observations. I will discuss our recent successes in training machine learning models capable of reliably predicting orbital stability a million times faster than direct integrations. This opens a wide discovery space for exoplanet characterization and planet formation studies as the next generation of spaceborne exoplanet surveys prepare for launch next year.

I will discuss and connect two long-standing, and at first glance entirely unrelated, problems of prediction: (1) the long-term dynamical stability of the Solar System, and (2) price movements and volatility in financial markets. These phenomena have radically different governing mechanisms, and their characteristic time scales are vastly different, but they share a key common basis in the random walk, and the study of both can be traced directly back to the work of Henri Poincaré.