Machine Learning & Edge AI

Our machine learning research focuses on data-driven methods for analyzing complex, often high-dimensional datasets - for example, terrestrial point clouds in geophysical exploration or time-series based air traffic forecasting. We combine supervised learning, deep learning, and hybrid approaches (e.g., LSTMs with evolutionary algorithms) to model and interpret patterns, anomalies, and temporal dynamics. A key focus is the development of model-driven ML pipelines that integrate heterogeneous data sources and enable automated reasoning - including the extraction of physically grounded prediction models from neural networks.

Our research also focuses on deploying ML models on resource-constrained platforms such as ESP32 microcontrollers, wearables, and mobile robots. By combining efficient model design with hardware-aware optimization and data preparation, we enable on-device inference where energy, memory, and processing power are limited. The result: autonomous systems that can make fast, reliable decisions directly at the edge, without constant reliance on remote servers.