Dr. Caitlin Augustin
Caitlin’s dissertation research focused on the deployment of carbon capture and storage (CCS) technologies under regulatory uncertainty. Caitlin hates it when people think carbon capture and storage is the same as fracking. It's not the fracking same. So stop it. Just stop.
Since graduating, Caitlin has become a huge slacker. She is: the head of learning and technical practice at DataKind (bio here), the DataKind lead for the AI X Prize competition (in the top 10 for the milestone prize), just presented at NIPS 2017 on machine learning in the developing world (Dec 2017), presenting at the Sorenson Winter Impact Summit on data science in education (Jan 2018), presenting a paper on nudges and study plan adherence at the Eastern Educational Research Association's annual conference (Feb 2018), serving as a session co-chair for the IPCC Cities conference and presenting on AI for Smart Cities (March 2018), leading a training session and presenting on a tool she codeveloped with researchers at NCEAS for evidence synthesis (tool is Colandr) at CEE (Apr 2018), part of a #FailFest presentation at Good Tech Fest (May 2018), serving as a session co-chair for the AGOS conference and leading the session on machine learning for the sciences (Jun 2018), presenting some of her PhD work on using cluster analysis to identify potential CO2 leaks and methodology extensions to other near-surface pollutants, and is serving as part of the faculty for NYU's Tandon School of Engineering in the Department of Technology Management and Innovation and teaching/developing graduate classes in operations research/management science/risk management, and research methods/development. We're confident that she'll get back on track soon, though.
To contact Caitlin, email her at: email@example.com.