Conference Agenda

Model merging under unseen test-time distribution shifts often renders naive strategies, such as mean averaging unreliable. This challenge is especially acute in medical imaging, where models are fine-tuned locally at clinics on private data, producing domain-specific models that differ by scanner, protocol, and population. When deployed at an unseen clinical site, test cases arrive in unlabeled, non-i.i.d. batches, and the model must adapt immediately without labels. In this work, we introduce an entropy-adaptive, fully online model-merging method that yields a batch-specific merged model via only forward passes, effectively leveraging target information. We further demonstrate why mean merging is prone to failure and misaligned under heterogeneous domain shifts. Next, we mitigate encoder classifier mismatch by decoupling the encoder and classification head, merging with separate merging coefficients. We extensively evaluate our method with state-of-the-art baselines using two backbones across nine medical and natural-domain generalization image classification datasets, showing consistent gains across standard evaluation and challenging scenarios. These performance gains are achieved while retaining single-model inference at test-time, thereby demonstrating the effectiveness of our method.

Manual expert gating remains the standard approach for defining specific cell populations in flow cytometry. However, as the number of measured parameters per cell continues to increase, manual gating becomes increasingly difficult to scale. In addition, high inter-rater variability limits reproducibility, particularly in multicentre studies, making the process both labour-intensive and inconsistent.

Here, we introduce ConvexGating, an AI tool that automatically learns gating strategies for cell sorting in an unbiased, fully data-driven, and interpretable manner. A central advantage of ConvexGating over existing computational gating approaches is that the inferred strategies are expressed as polygon gates on conventional two-dimensional scatter plots. These gates can be directly implemented on standard cell sorters without modification. By preserving compatibility with established workflows, ConvexGating bridges the gap between advanced computational analysis and practical cell sorting.

ConvexGating scales efficiently to high-dimensional parameter spaces and generates robust strategies with low contamination of the target population, applicable to both well-characterized and previously undefined or poorly resolved cell types. Importantly, the inferred gating strategies are independent of predefined parent populations. For example, plasmacytoid dendritic cells (pDCs) can be fully identified as CD57^- CD13^- CD45RA⁺ CD123⁺ cells using a single, self-contained gating hierarchy.

We validated ConvexGating-derived strategies for CD8⁺ naive and CD8⁺ Temra cells, as well as progenitor populations from mouse white adipose tissue, using 384-well–based single-cell RNA sequencing (scRNA-seq). In all cases, ConvexGating yielded more homogeneous sorted populations compared to conventional manual strategies. Beyond flow cytometry, ConvexGating derives transferable gating strategies for CyTOF (Cytometry by Time of Flight) and CITE-seq (Cellular Indexing of Transcriptomes and Epitopes by Sequencing) data, and it supports optimal marker panel design for targeted cell sorting applications.

Transitioning to renewable energy is essential for mitigating climate change, but the variable and decentralised nature of such generation systems presents major challenges when maintaining grid stability for reliable operation. AI-driven solutions have the potential to address these challenges, particularly in the form of more powerful and robust forecasting. However, progress at scale is hampered by the lack of standardised, high-quality renewable energy datasets. Existing models are therefore often limited in geographic scope, restricted to a specific generation or data type, and evaluated on proprietary datasets that prevent broad comparison. Additionally, these models disregard the spatio-temporal couplings influencing long-term grid stability, as they consider only local weather inputs or ignore weather dependencies altogether.

RenewBench addresses these limitations by fusing renewable energy generation and weather data to create a global, open-source energy benchmark. We consolidate openly available generation datasets with high temporal and spatial resolution into a standardised Zarr-based structure. These are combined with meteorological reanalysis data and enriched with comprehensive SpatioTemporal Asset Catalog (STAC) metadata to create an AI-ready findable, accessible, interoperable, and reusable (FAIR) dataset. By leveraging a STAC FastAPI and PgSTAC backend, researchers can perform nearly instantaneous metadata queries and automated retrieval via a dedicated Python package to facilitate many benchmarking tasks.

In this poster we present the current status of RenewBench, including incorporated geographic regions, database setup, and initial benchmarking ideas. By building this open and unified global benchmark, we contribute to democratising data access for the next generation of data-driven energy-meteorology solutions.

Foundation Models (FMs) are built by pretraining on extensive datasets, followed by fine-tuning for specific downstream applications. By leveraging large-scale unlabeled data, FMs learn generalized task-agnostic feature representations that enables the integration of diverse Earth Observation (EO) data, for applications such as global carbon stock mapping, forest canopy height estimation, and long-term monitoring. Most EO modality, such as optical imagery, are represented as dense 2D grids, making them well suited for image-based pretraining. However, not all EO data follow this structure. Some sensors, such as the Global Ecosystem Dynamics Investigation (GEDI), produce sparse and irregular observations, creating a fundamental challenge for multimodal learning. GEDI is a spaceborne LiDAR sensor mounted on the ISS that emits laser pulses toward the Earth's surface and records the full waveform of the reflectance. GEDI produces precise measurements of forest canopy height, vertical structure and surface elevation. Despite its powerful capability for measuring vegetation height, GEDI shots are very sparse compared to gridded EO imagery. The spacing between GEDI shots is approximately 600 m across-track and 60 m along-track. In contrast, products such as the 30 m Harmonized Landsat Sentinel-2 (HLS) dataset provide dense spatial coverage. This mismatch makes direct integration of GEDI with grid-based 2D modalities difficult during training. To address this challenge, we treat the GEDI full waveform as a 1D continuous signal and apply a masking strategy in which portions of the signal are randomly masked for each GEDI shot and then reconstructed in a masked autoencoder framework, enabling self-supervised learning of general waveform representations from large collections of unlabeled GEDI data. The pretrained waveform representation are then tokenized into embeddings and fused with other modality to capture cross-modal dependencies. We then refine the map with the pretrained GEDI and finally estimate the vegetation height for each pixel of the 2D dense images.

Datasets created in clinical settings, such as imaging, biosignals, or genetic data, often lack the structured metadata (age, sex, medication, medical diagnoses, etc.) required for research. Restrospective creation of such structured metadata for research is complicated and expensive. Electronic health records typically contain rich free-text clinical notes describing medical conditions, patient’s history, symptoms, and others, which represent a valuable but unstructured source for generating such metadata. Retrieval-augmented generation (RAG) has been shown to enhance LLMs on knowledge-intensive tasks, but has not yet been systematically applied to diagnosis coding on large open-access clinical datasets. This work investigates whether large language models (LLMs), grounded on reliable diagnosis ontologies, can be used to automatically and reliably extract diagnosis codes from free-text clinical notes, in a tracable and explainable manner, to produce research-ready metadata.

A three-step pipeline was devised. First, Named Entity Recognition (NER) is applied to identify spans in clinical text indicating disorders and symptoms. Second, semantically similar diagnosis codes are retrieved from a vector database of embedded International Classification of Diseases (ICD) descriptions and complementary ontologies with equivalent terminologies. Third, 'gpt-oss' (120B) is prompted for self-verification and output a final set of ICD codes.

This pipeline was evaluated on 200 clinical notes of the MIMIC IV 3.1 benchmark. On the level of a three-digit ICD code, i.e., a concrete diagnosis that groups its subtypes, it attained a recall of 0.56 and precision of 0.16 compared to single-shot LLM prompt recall of 0.42 and precision 0.55. On the level of two-digit ICD codes, it attained a recall of 0.67. We propose a metric of Jiang-Conrath (JC) "closeness” between diagnoses, based on PheCodeX or ICD-9, enabling relaxed evaluations that can be tuned on case-by-case needs.

The long-tail distribution of the MIMIC dataset, with a few very frequent codes and many codes not so frequently used, revealed a clear trade-off that most of the proposed systems apply toward prioritizing the accurate identification of the most common diagnoses, at the cost of additional false positives.

Overall, this work proposes a framework for creating structured and machine-readable metadata tables from routine clinical documentation for research use, grounding LLM capabilities to reliable ontologies.