In this episode, I caught up with Kai Jeggle to discuss his experience pursuing a PhD at the intersection of machine learning and remote sensing. The conversation covers Kai's work on IceCloudNet, a deep learning model that reconstructs 3D cloud structures from 2D imagery with sparse depth measurements. Data fusion and sparse machine learning are fascinating topics. I learned a lot from this conversation, and I hope you do to.

* 👤 Kai on LinkedIn

* 📃 IceCloudNet paper

* 🖥️ Code

* 💾 Dataset

* 📺 Video of this conversation on YouTube

Bio: Kai is passionate about leveraging machine learning to tackle climate change. His research lies at the intersection of ML, remote sensing, and climate science. He studied industrial engineering and computer science before completing his PhD in Atmospheric Physics at ETH Zurich under Prof. Ulrike Lohmann, with visiting research stays at UV Valencia and the ESA Phi Lab. He also worked as a software engineer at the Stockholm-based MLOps startup LogicalClocks. Kai is a core team member and former vice-chair of Climate Change AI, a global non-profit that catalyses impactful work at the intersection of climate change and machine learning. In his next role, he will join the meteo data team at Dexter Energy in Amsterdam, working to improve renewable energy yield forecasts.

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