What We'RE Doing and Why
This summer, four of us at NASA FDL are creating 3D models of asteroids. Our core team comprises two planetary scientists (Agata Rozek and Sean Marshall), two machine learning engineers (Adam Cobb and me), plus mentors from both disciplines (Chedy Raissi, Michael Busch, and Yarin Gal). We’re creating the 3D models from radar data. It's a difficult computational problem, but knowing an asteroid’s 3D shape helps us predict its future trajectory (/whether it will collide with Earth!).
The formal introduction to our problem reads as follows:
Delay-Doppler radar imaging is a powerful technique to characterize the trajectories, shapes, and spin states of near-Earth asteroids and has yielded detailed models of dozens of objects. Since the 1990s, delay-Doppler images have been analyzed using the SHAPE software developed originally by R. S. Hudson and S. J. Ostro [1, 2]. SHAPE normally performs sequential single-parameter fitting. Recently, multiple-parameter fitting algorithms have been shown to more efficiently invert delay-Doppler data sets, thus decreasing runtime while improving accuracy . However, reconstructing asteroid shapes and spins from radar data is still, like many inverse problems, a computationally intensive task that requires extensive human oversight. The FDL 2016 team explored two new techniques to better automate delay-Doppler shape modeling: Bayesian optimization  and deep generative models . The FDL 2017 team is refining that work and exploring new directions for more quickly and accurately generating 3D models of near-Earth asteroids from delay-Doppler images.
 R. Scott Hudson. Three-dimensional reconstruction of asteroids from radar observations. Remote Sensing Reviews 8, 195–203, 1993.
 Christopher Magri, Michael C. Nolan, Steven J. Ostro, and Jon D. Giorgini. A radar survey of main-belt asteroids: Arecibo observations of 55 objects during 1999-2003. Icarus 186, 126–151, 2007.
 Adam H. Greenberg and Jean-Luc Margot. Improved algorithms for radar-based reconstruction of asteroid shapes. The Astronomical Journal 150(4), 114, 2015.
 Jonas Mockus. Bayesian Heuristic Approach to Discrete and Global Optimization: Algorithms, Visualization, Software, and Applications. Springer-Verlag, Berlin, Heidelberg, 2010.
 Ruslan Salakhutdinov. Learning deep generative models. Annual Review of Statistics and Its Applications 2, 361–385, 2015.
 Shane Carr, Roman Garnett, and Cynthia Lo. BASC: applying Bayesian optimization to the search for global minima on potential energy surfaces. International Conference on Machine Learning. 2016.