VU NU Gebouw, room: NU-5A47
ADS Drinks & Data: AI solutions for Climate Change
Join us for this in-person Meetup on the 21st of September at 16:00! 👏
Artificial Intelligence has the potential to tackle the biggest challenge on the planet, Climate Change. What could the role of AI and Data Science be in this huge problem? How can we address this challenge, with AI technology? Let’s talk about, Sustainable Energy, Waste Management, Smart Building, Improve Transportation Efficiency, Improve Energy Efficiency, Predicting weather forecasts and many more.
Location: VU NU Gebouw, room: NU-5A47
16:00 Introduction & Welcome
16:05 Talk #1: Nick Schutgens (Earth & Climate, VU)
16:25 Talk #2: Sandra Merten (Elsevier)
16:45 Talk #3: Chiem van Straaten (XAIDA, KNMI)
Talk #1 by Nick Schutgens (Earth Sciences, VU)
Talk #2 by Sandra Merten (Elsevier)
Talk #3 by Chiem van Straaten (KNMI)
Chiem van Straaten was educated in economics and hydrology at VU Amsterdam and Utrecht University, He transitioned into atmospheric science during an internship at the KNMI, where he worked on quantitative precipitation forecasts. Currently, he pursues a PhD in the long range forecasting of European weather. Such forecasts require an understanding of drivers of predictable extreme events. To that end Chiem uses tools from Machine Learning and eXplainable AI. The obtained knowledge is used to improve weather predictions that are relevant for society. Chiem also works as a story-teller and poet.
Understanding climate extremes through AI
As extreme summers become the rule instead of the exception, a two-fold call is heard. First is the call for better and earlier predictions, such that society can prepare itself. Second is the call for insight into the role of climate change, such that its current effects can be clearly communicated and such that future risks can b better quantified.
I will show how AI is used to disentangle the complex processes behind extremes. Strong challenges must be met. Among these are the high dimensionality of the earth system, the lack of labeled data, and the presence of fluctuations on