Current research projects often process hundreds of terabytes of satellite observations. Here, transferring the data can become as important a bottleneck as computation. We develop trainable selection masks that identify and transfer only the parts of an input that matter for a task. For petabyte-scale archives, automated preselection can substantially reduce data movement and end-to-end inference time.

This line of work directly supports the scalable Earth-observation pipelines developed in AI4Forest.

Our work exploits the parallelism of modern many-core systems through hardware-aware algorithm design. Buffer k-d trees batch search requests for massively parallel nearest-neighbor search on GPUs. Instead of traversing a tree independently for every query, buffers collect queries at tree nodes and process them together. This reorganizes irregular control flow into large, hardware-friendly batches and improves memory access on GPUs.

We have extended this perspective to data-intensive learning models and scientific pipelines, including parallel regression for satellite time series. In change detection, such methods can reduce computations over billions of time series from weeks or years to hours or days. The central principle is to co-design algorithms, memory movement, vectorization, and distributed execution for the target architecture.

Astronomy and particle physics were important application areas in an earlier phase of our research. Modern sky surveys produce very large image and catalogue collections in which rare, scientifically relevant objects must be identified among millions or billions of observations. Manual inspection is therefore impossible, making accurate and scalable machine-learning pipelines essential.

For astronomical surveys, we developed nearest-neighbor methods for photometric redshift estimation and for discovering previously unknown high-redshift quasars. We also designed convolutional neural networks that distinguish genuine transient events from imaging artefacts and thereby reduce the number of candidates requiring expert review. These projects established our focus on scalable inference, efficient candidate selection, and close collaboration with domain scientists.