Introducing the 2019 Disaster Prevention Team

 
david_belo.png

Researcher: Andrew Annex
Andrew Annex is a 3rd-year planetary science Ph.D. candidate at the Johns Hopkins University. He earned his M.A. in Earth and Planetary Sciences while at Johns Hopkins in the spring of 2018. At Johns Hopkins, he studies the geologic history of Mars, focusing on exposures of sedimentary rocks. He applies deep learning techniques to high-resolution remote sensing data to study the structure of these sedimentary units. By investigating these structures, information about the changing climate and habitability of Mars may be recovered. Through his career, Andrew hopes to develop deep learning techniques to derive geologic insights for the planetary science community.

Before starting his Ph.D., Andrew was a software engineer contributing to open source geospatial projects. In his spare time, he contributes to other open source software projects in the planetary sciences and participates in pub trivia.


dylan.png

Researcher: Dylan Fitzpatrick

Dylan Fitzpatrick is a PhD candidate in Machine Learning and Public Policy at Carnegie Mellon University, where he researches novel statistical machine learning algorithms for pattern detection. As PhD student, Dylan works mostly in the domains of public health and criminology, exploring new methods for disease outbreak detection and crime forecasting in spatial data. Most recently, Dylan has focused on individual-level opioid use monitoring, developing techniques for assessing risk of opioid misuse in patients and identifying unsafe opioid prescribing practices from prescription data. Dylan participated in University of Chicago’s Data Science for Social Good program, where he used machine learning methods to identify instances of collusion and corruption in bidding for World Bank-funded development projects around the world. While working for IBM Research, Dylan developed a framework for active imitation learning using generative adversarial networks (GANs), in which an optimal policy for decision-making is learned by selectively querying an expert agent. Dylan earned a BA in Economics from Middlebury College and an MS in Computer Science from Carnegie Mellon University.

Outside of research, Dylan enjoys spending time outdoors with his dog, playing guitar, practicing yoga, and baking rhubarb pies. Dylan is an avid Minnesota Vikings fan who often wishes he had a choice in the matter.


chelsea.png

Researcher: Chelsea Sidrane

Chelsea Sidrane is a third-year PhD student at Stanford University in the Aero-Astro department. She studies safety for machine learning in cyber-physical systems such as cars, planes, and spaceships. Her undergraduate degree is in mechanical engineering with a focus on robotics and control, and the focus of her graduate coursework has been machine learning and optimization. She is interested in all things space, and is particularly interested in how space technology can be used for social good.