I'm Marten Lienen and I care about solving problems and improving efficiency with software and machine learning. Aesthetically pleasing code makes me extra happy. At the moment, I am a PhD student at TUM's DAML group working on spatiotemporal data modeling and ML+Science in general. Before that, I worked at BMW's autonomous driving campus on 3D object detection.
For quite some time, I have also been interested in the various paradigms of thinking about programming that different programming languages have to offer and I like to read obscure books about them. You can find some of my own code on my github profile.
Publications
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Unfolding Time: Generative Modeling for Turbulent Flows in 4D AI4Science Workshop @ ICML, 2024 -
From Zero to Turbulence: Generative Modeling for 3D Flow Simulation International Conference on Learning Representations, 2024 -
Add and Thin: Diffusion for Temporal Point Processes Neural Information Processing Systems, 2023 -
torchode: A Parallel ODE Solver for PyTorch The Symbiosis of Deep Learning and Differential Equations Workshop @ NeurIPS, 2022 -
Learning the Dynamics of Physical Systems from Sparse Observations with Finite Element Networks International Conference on Learning Representations (Spotlight), 2022 -
Scalable Optimal Transport in High Dimensions for Graph Distances, Embedding Alignment, and More International Conference on Machine Learning, 2021 -
FLGR: Fixed Length Gists Representation Learning for RNN-HMM Hybrid-based Neuromorphic Continuous Gesture Recognition Frontiers in Neuroscience, 13 (2019) -
Rate-Adaptive Link Quality Estimation for Coded Packet Networks 2016 IEEE 41st Conference on Local Computer Networks (LCN)