Physics-aware Machine learning for Earth Observation
2. prosince 2022
Řečníci
Gustau Camps-Valls
Řečník · 0 sledujících
O prezentaci
**Title**: "Physics-aware Machine learning for Earth observation" **Abstract**: Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem. Inferences mean understanding variables relations, deriving models that are physically plausible, that are simple parsimonious, and mathematically tractable. Machine learning models alone are excellent approximators, but very often do not respect the most elementary laws of physics, like mass or energy conservation, so consistency and confidence are compromised. I will review the main challenges ahead in the field, and introduce several ways to live in the Physics and machine learning interplay. Physics-aware machine learning models are just a step towards understanding the data-generating process, for which learning causal representations promises great advances. I'll review some recent methodologies to cope with it too. This is a collective long-term AI agenda towards developing and applying algorithms capable of discovering knowledge in the Earth system. **Bio**: Gustau Camps-Valls (born 1972 in València) is a Physicist and Full Professor in Electrical Engineering in the Universitat de València, Spain, where lectures on machine learning, remote sensing and signal processing. He is the Head of the Image and Signal Processing (ISP) group, an interdisciplinary group of 40 researchers working at the intersection of AI for Earth and Climate sciences. Prof. Camps-Valls published over 250+ peer-reviewed international journal papers, 350+ international conference papers, 25 book chapters, and 5 international books on remote sensing, image processing and machine learning. He has an h-index of 78 with 29000+ citations in Google Scholar. He was listed as a Highly Cited Researcher in 2011, 2020 and 2021; currently has 13 «Highly Cited Papers» and 1 «Hot Paper», Thomson Reuters ScienceWatch identified his activities as a Fast Moving Front research (2011) and the most-cited paper in the area of Engineering in 2011, received the Google Classic paper award (2019), and Stanford Metrics includes him in the top 2 He has been Program Committee member of international conferences (IEEE, SPIE, EGU, AGU), and Technical Program Chair at IEEE IGARSS 2018 (2400+ attendees) and general at AISTATS 2022. He served in technical committees of the IEEE GRSS IEEE SPS, as Associate Editor of 5 top IEEE journals, and in the prestigious IEEE Distinguished Lecturer program of the GRSS (2017-2019) to promote «AI in Earth sciences» globally. He has given 100+ talks, keynote speaker in 10+ conferences, and (co)advised 10+ PhD theses. He coordinated/participated in 60+ research projects, involving industry and academia at national and European levels. He assisted the aerospace industry in Advisory Boards; Fellow Consultant of the ESA PhiLab (2019) and member of the EUMETSAT MTG-IRS Science Team. He is compromised with open source/access in Science, and is habitual panel evaluator for H2020 (ERC, FET), NSF, China and Swiss Science Foundations. He coordinates the ‘Machine Learning for Earth and Climate Sciences' research program of ELLIS, the top network of excellence on AI in Europe. He was elevated to IEEE Fellow member (2018) in two Societies (Geosciences and Signal Processing) and to ELLIS Fellow (2019). Prof. Camps-Valls is the only researcher receiving two European Research Council (ERC) grants in two different areas: an ERC Consolidator (2015, Computer Science) and ERC Synergy (2019, Physical Sciences) grants to advance AI for Earth and Climate Sciences. In 2021 he became a Member of the ESSC panel part of the European Science Foundation (ESF), and in 2022 was elevated to Fellow of the European Academy of Sciences (EurASc), Fellow of the Academia Europeae (AE), and Fellow of Asia-Pacific Artificial Intelligence Association (AAIA).**Title**: "Physics-aware Machine learning for Earth observation" **Abstract**: Most problems in Earth sciences aim to do inferences about the system, where accurate predictions are just a tiny part of the whole problem. Inferences mean understanding variables relations, deriving models that are physically plausible, that are simple parsimonious, and mathematically tractable. Machine learning models alone are excellent approximators, but very often do not respect the most elementary laws of phy…
Organizátor
NeurIPS 2022
Účet · 960 sledujících
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