Abstract: 

Earth’s branching stream networks expand with floods and contract with droughts. Can we map these stream networks in near real-time to forecast ecosystem drought and provide a data-informed classification of stream-flow frequency? In this talk, I discuss the efforts of project WOTUS, as part of the Frontier Development Lab 2020 research accelerator. Project WOTUS explores our ability to augment human mapping by applying AI to high-resolution, high-cadence satellite imagery in a partnership with USGS, NGA, Planet, and NASA.

Bio: 

Alfredo joined Element AI as a Research Engineer in the AI for Good lab in London, working on applications that enable NGOs and non-profits.
He is one of the primary co-authors of the first technical report made in partnership with Amnesty International, on the large-scale study of online abuse against women on Twitter from crowd-sourced data. He's been a Machine Learning mentor at NASA's Frontier Development Program, helping teams apply AI for scientific space problems. More recently, he led the joint-research with Mila Montreal on Multi-Frame Super-Resolution, which was awarded by the European Space Agency for their top performance on the PROBA-V Super-Resolution challenge.His research interests lie in computer vision for satellite imagery, probabilistic modeling, and AI for Social Good. Prior to joining Element AI, he was a Senior Data Scientist in Digital Shadows, specializing in cyber-security and digital risk management, and a consulting Data Scientist in Microsoft's Xbox EMEA team, where he also collaborated with Microsoft Research Cambridge. He has been a research scientist with the Department of Statistical Science at University College London, working on probability models to better understand ordinal data coming from surveys, and later on the interface of Machine Learning and Signal Processing to detect faults in the low-voltage power-line grid. During his time with UCL, his team won the first data challenge competition organized by the Royal Statistical Society, for which he designed and developed his team's algorithm for the analysis of resting f-MRI time-series data. He earned his MSc in Artificial Intelligence from the University of Edinburgh, and his PhD in Machine Learning from the University of Sheffield under the supervision of Professor Neil Lawrence. His PhD research led to contributions in probability methods for dimensionality reduction of data and developed methods for gene-expression time-series to discover genetic factors of disease.