Selected as a Moore/Sloan Data Science and Washington Research Foundation Innovation in Data Science Postdoctoral Fellow, Daniel has a joint position in PSC and UW’s eScience Institute, much like his supervisor, Anthony Arendt. In 2016, he earned his Ph.D. from the Department of Applied Mathematics under the supervision of Ian Joughin. Daniel is interested in using numerical models of glaciers to ascertain their current state and how they will respond to the climate of the next century. In particular, many of the properties that we need to know about a glacier to make predictions of its flow in the future are not directly visible from satellite or airborne remote sensing; inferring these properties amounts to the solution of an inverse problem. Daniel is currently working on applying these ideas to study the biggest glaciers in Greenland.
Daniel received his B.Sc. in Applied Mathematics from McGill University in 2010. In his free time, he enjoys playing piano and climbing things.