Single-photon array detectors promise the ultimate in sensitivity by eliminating read noise. These devices could provide extraordinary benefits for photon-starved applications, e.g., seeing the first stars in the Universe, imaging exoplanets, fast wavefront sensing, and probing the human body through optical transilluminescence. Recent implementations are often in the form of sparse arrays that have less-than-unity fill factor. For imaging, fill factor is typically enhanced by using microlenses, at the expense of photometric and spatial information loss near the edges and corners of the pixels. Other challenges include afterpulsing and the potential for photon self-retriggering. Both effects produce spurious signal that can degrade the signal-to-noise ratio. This talk reviews development and potential application of single-photon-counting array detectors, including highlights of initiatives in the Center for Detectors at the Rochester Institute of Technology. Current projects include single-photon-counting imaging detectors for the Thirty Meter Telescope, a future NASA terrestrial exoplanet mission, and imaging LIDAR detectors for planetary and Earth science space missions.

Many of today's important scientific breakthroughs are made by large, interdisciplinary collaborations of scientists working in geographically distributed locations, collecting, producing, and analyzing vast and complex data sets. This deluge of scientific data has been called the "data tsunami." Large-scale science projects require software tools that support, not only insight into exponentially growing data, but collaborative science discovery.

This interdisciplinary research area has recently become known as eScience. In this talk, I discuss some of the new research directions opening up in eScience, and describe Sunfall, a collaborative visual analytics system developed for the Nearby Supernova Factory, an international astrophysics experiment and one of the largest data volume supernova searches in operation. Sunfall utilizes interactive visualization and analysis techniques to facilitate deeper scientific insight into complex, noisy, high-dimensional, high-volume, time-critical data. The system combines novel image processing algorithms, statistical analysis, and machine learning with highly interactive visual interfaces to enable collaborative, user-driven scientific exploration of supernova image and spectral data. I will also discuss related results from computer science research, and conclude the talk with some lessons learned about developing software to support scientific collaborations, and potential future directions for eScience.

On-going searches for extrasolar planets, despite certain limitations in sensitivity, have already revealed a remarkable diversity of worlds, from close-in super-Earths to far out super-Jupiters, and challenged our preconceptions many times over. Meanwhile, comparative studies of exoplanet physical properties have begun in earnest: planets caught in transit and those imaged directly are best suited for detailed characterization, especially of their atmospheres. I will discuss recent results and future prospects,including the possibility of extending these techniques to lower-mass planets.