Climate change is affecting extreme weather – we identify how and when society is at risk
Seasonal to sub-seasonal (S2S) predictability could provide societies with valuable information on weather-related risk, allowing decision-makers to initiate early warning action plans and to optimize resource management. Using Artificial Intelligence (AI), we are developing a rigorous data-driven framework that enables automatic detection of interpretable physical drivers on S2S timescales. Combining expert knowledge, causal inference and a variety of machine learning techniques, our tool has worked for science. Now we aim for societal impact by making predictions for stakeholders (e.g. NGO’s).
Read also the article at te VU website in the exhibition ‘A better World @ VU’ Artificial Intelligence for Long-term Weather Predictions – Meer over – Vrije Universiteit Amsterdam (vu.nl)
Fundamental Frequencies founders are two electronic engineers with a passion for music, and we’ve been talking to a lot of musicians and we’ve been asking them what do they really want? We keep getting the same reply. They want to go back to the sounds of the modular analog synthesizer of the 70‚Äôs. That’s why […]
Calibration-free webcam eye-tracker based on deep learning DeepEye records eye movements using a regular webcam and cutting-edge AI algorithms. Eye-tracking is widely used to understand how humans process information. This information provides solutions for scientific research, marketing & advertising, building human-computer interfaces, and diagnosing mental disorders. DeepEye is designed to make eye-tracking available to everyone […]
We create Smart Campus Tools. We use sensors and data to improve campus experience for students, while also innovating campus management for the University. We started to develop the PLEQ-app, a study spot finder based on real time occupancy data of study rooms around campus. We want to create the future campus. A campus where […]