The first paper is “Learning Reduced Order Dynamics via Geometric Representations”, and co-authored by Imran and Melanie Webber (Harvard). The second paper is “Using Neural Implicit Flow To Represent Latent Dynamics Of Canonical Systems”, co-authored by Imran and Joao Lucas De Sousa Almeida (IBM). Imran is an AI Engineer at IBM (web page) and Visiting Lecturer in Mathematics at Surrey. The papers will be presented at the International conference on Scientific Computation and Machine Learning (SCML) to be held 19-23 March in Kyoto Japan. Conferences on ML and AI serve as premier forums for presenting and discussing the latest advancements in ML and AI research. Unlike the more popular venues such as ICML and ICLR, SCML has a clear focus on Scientific Machine Learning (SciML). The significance of this conference series highlights how advancements in computational methods and machine learning techniques can be applied to solve complex scientific problems, ranging from physics and chemistry to biology and environmental sciences. SCML is quickly establishing itself as a leading venue within the SciML community. SCML 2024 will be held in the Kyoto Research Park and a screenshot of the KRP webpage is shown below.