Please use this zoom link for the event.
Please join us for a UW Data Science Seminar event on Thursday, March 11th from 4:30 to 5:30 p.m. The seminar will feature Hamed Alemohammad, Executive Director and Chief Data Scientist at the nonprofit organization Radiant Earth.
“Radiant MLHub: An Ecosystem to Advance Machine Learning Applications in Earth Science”
Abstract: Earth observations (EO) provide consistent data over different spatio-temporal scales that can be used for modeling the Earth and its changing environment. Such modeling efforts require characterization of complex and nonlinear processes that could benefit from data-driven techniques – in particular, machine learning (ML). ML techniques are rapidly being applied to EO data in order to serve numerous and diverse markets, from agriculture to medicine to transportation. These technologies present a game-changing opportunity: the ability to more accurately and more quickly identify and address unique, complex, and emerging challenges at local, regional, and global scales. From poverty alleviation, food security, and climate change adaptation to sustainable resource management and humanitarian response, the combination of EO with ML can help humanity see, understand, and respond to a rapidly changing world.
ML techniques learn from data and any uncertainty or bias in the data propagates to the model and future estimates. To enable successful applications of ML techniques in Earth science, it’s paramount to build an ecosystem for sharing and publishing training data, models and best practices. Such an ecosystem can make these techniques more accessible and bring transparency and trust in the results. In this presentation, I will discuss existing challenges for advancing ML applications in Earth science and introduce Radiant MLHub and the standards we have been working on to facilitate these applications. In the second part of the talk, I will present on new approaches to account for uncertainties in training datasets and filling gaps in training data using synthetic datasets for land cover classification.
Biography: Hamed Alemohammad is the Chief Data Scientist and Executive Director at Radiant Earth Foundation, leading the development of Radiant MLHub- the open repository for geospatial training data and models. He has extensive expertise in machine learning, remote sensing and imagery techniques particularly in developing new algorithms for multi-spectral and passive/active microwave observations. He serves on the Technical Advisory Boards of Lacuna Fund (established by The Rockefeller Foundation, Google.org and IDRC) and Enabling Crop Analytics At Scale (a Bill and Melinda Gates Foundation initiative). Prior to joining Radiant Earth, he was a Research Scientist at Columbia University. Hamed received his PhD in Civil and Environmental Engineering from MIT in 2014.
The UW Data Science Seminar is an annual lecture series at the University of Washington that hosts scholars working across applied areas of data science, such as the sciences, engineering, humanities and arts along with methodological areas in data science, such as computer science, applied math and statistics. Our presenters come from all domain fields and include occasional external speakers from regional partners, governmental agencies and industry.
All seminars will be hosted virtually for the 2020-2021 academic year, and are free and open to the public.