Jesper Dramsch
Jesper Dramsch works at the intersection of machine learning and physical, real-world data. Currently, they're working as a scientist for machine learning in numerical weather prediction at the coordinated organisation ECMWF.
Before, Jesper has worked on applied exploratory machine learning problems, e.g. satellites and Lidar imaging on trains, and defended a PhD in machine learning for geoscience. During the PhD, Jesper wrote multiple publications and often presented at workshops and conferences, eventually holding keynote presentations on the future of machine learning.
Moreover, they worked as consultant machine learning and Python educator in international companies and the UK government. Their courses on Skillshare have been watched over 30 days by over 5000 students. Additionally, they create educational notebooks on Kaggle, reaching rank 81 worldwide. Recently, Jesper was invited into the Youtube Partner programme.
Sessions
Numerous scientific disciplines have noticed a reproducibility crisis of published results. While this important topic was being addressed, the danger of non-reproducible and unsustainable research artefacts using machine learning in science arose. The brunt of this has been avoided by better education of reviewers who nowadays have the skills to spot insufficient validation practices. However, there is more potential to further ease the review process, improve collaboration and make results and models available to fellow scientists. This workshop will teach practical lessons that can be directly applied to elevate the quality of ML applications in science by scientists.
Nowadays we know the social media and tech giants are honesting tons of data from their users and most of us agree that the capability of these companies to deliver their suggestions and customization for you is driven by big data.
However, this brings a question: Is more data always better? Do more data equal to more accurate model? When do you need big data and when does it start becoming a bad idea? Let's find out in this panel session.