BIDS Senior Fellow Michael Mahoney — with colleagues from UC Berkeley, Berkeley Lab, and the University of Washington — will host the 1st Workshop on Scientific-Driven Deep Learning (SciDL) on Wednesday, July 1, 2020. The event will be held virtually via Zoom. Register here.
Deep learning is playing a growing role in the area of fluid dynamics, climate science and in many other scientific disciplines. Classically, deep learning has focused on an model agnostic learning approaches ignoring any prior knowledge that is known about the problem under consideration. However, limited data can severely challenge our ability to train complex and deep models for scientific applications. This workshop focuses on scientific-driven deep learning to explore challenges and solutions for more robust and interpretable learning.
Key Note Speakers
- Frank Noé (FU Berlin)
- Lars Ruthotto (Emory University)
- Yasaman Bahri (Google Brain)
- Omri Azencot (UCLA)
- Michael Muehlebach (UC Berkeley)
- Alejandro Queiruga (Google, LLC)
- Elizabeth Qian (MIT)
Michael Mahoney works on algorithmic and statistical aspects of modern large-scale data analysis. Much of his recent research has focused on large-scale machine learning including randomized matrix algorithms and randomized numerical linear algebra; geometric network analysis tools for structure extraction in large informatics graphs; scalable implicit regularization methods; and applications in genetics, astronomy, medical imaging, social network analysis, and internet data analysis.