Philipp Steurer
Papers
1
Total Citations
1
H-Index
1
About
Philipp Steurer is a researcher at the forefront of privacy-preserving machine learning and sustainable industrial automation. His work centers on developing secure, data-driven solutions for energy prediction in robotics, addressing critical challenges at the intersection of artificial intelligence, data privacy, and manufacturing efficiency. Steurer’s major contribution lies in demonstrating the feasibility of cloud-based energy consumption forecasting for industrial robots without compromising sensitive operational data. His most-cited paper, “Towards Privacy-Preserving Machine Learning for Energy Prediction in Industrial Robotics,” evaluates neural network architectures—including dense, LSTM, and convolutional–LSTM hybrids—to model energy use with high accuracy while integrating privacy safeguards. This pioneering approach enables manufacturers to optimize energy efficiency and reduce costs through secure, scalable cloud services. With over 1 citation to date, Steurer’s work is gaining traction among researchers and practitioners in green manufacturing and secure AI. His research not only advances the practical deployment of machine learning in industry but also sets a foundation for responsible, privacy-aware automation—a vital step toward sustainable and trustworthy smart factories.
Research Focus
Key Achievements
Top Papers
- 1