Philipp Wiesner is a researcher-engineer bridging distributed systems, machine learning, and sustainable energy, with 11 years of industry and academic experience spanning startups and top universities. He completed a PhD on carbon-aware optimization at TU Berlin and now works on enabling renewable-aware distributed AI—both as a postdoc and as a research scientist at Exalsius, where he helps automate multi-cloud GPU cluster provisioning. His contributions to cloud simulation (notably improving power modeling and network APIs in the popular CloudSimPlus framework) reflect a pragmatic focus on accuracy and maintainability. Philipp’s background ranges from building RNN-based extraction pipelines and large-scale training infrastructure to production data platforms, giving him a rare blend of hands-on software engineering and systems-level research. Based in Berlin, he frequently combines open-source work with field deployments to close the gap between renewable-aware algorithms and operational GPU fleets.
10 years of coding experience
11 years of employment as a software developer
Bachelor of Science Media Informatics, Bachelor of Science Media Informatics at Ludwig-Maximilians-Universität München
Doctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at Technische Universität Berlin
Semester Abroad Computer Science, Semester Abroad Computer Science at UAM Universidad Autónoma Metropolitana
State-of-the-art Framework 🏗 for Cloud Computing ⛅️ Simulation: a modern, full-featured, easier-to-use, highly extensible 🧩, faster 🚀 and more accurate ☕️ Java 17+ tool for cloud computing research 🎓. Examples: https://github.com/cloudsimplus/cloudsimplus-examples
Role in this project:
Backend Developer
Contributions:13 reviews, 9 commits, 10 PRs in 2 months
Contributions summary:Philipp primarily worked on improving the power modeling and network functionalities of the cloud simulation framework. They refactored the power model implementation, introducing new interfaces and classes like `PowerMeter` and `PowerModelHost` while removing deprecated models. They also migrated the network API from ID-based to object-based, improving code readability and maintainability. Furthermore, the user addressed bugs related to event scheduling and power measurement intervals, ensuring the simulation's accuracy.
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