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I am a Ph.D. student at the Karlsruhe Institut of Technology (KIT) affiliated with the Institute for Data Processing and Electronics (IPE), with a primary focus on machine learning applications in energy systems. Throughout my research I explore different machine learning techniques, including federated learning, reinforcement learning, and time series forecasting, as they are increasingly important in the optimization of energy management systems.

• Federated learning enables collaborative model training without sharing sensitive data, allowing various energy devices to work together enhancing data security, privacy, latency, and bandwidth efficiency within the underlying communication network.
• Reinforcement learning enables energy management systems to make dynamic decisions through learning from interactions with the environment, leading to adaptive control strategies that optimize energy consumption, emissions, and operational costs.
• Time series forecasting, driven by machine learning algorithms, utilizes historical energy data to make accurate predictions, helping energy managers plan and allocate resources more efficiently, while improving grid stability.

I usally provide all source code required to reproduce research and build upon it. If you have any questions or stumble over any problem, feel free to reach out to me or open an issue on Github.