Seminars - page 4

The DIG team holds a seminar about every two weeks with speakers either from the team, or invited.

You can add the seminars to your calendar with this ics file, and get emails about future seminars by subscribing to our mailing-list.

If you would like to present your work at our seminar, please contact Nils.

Upcoming Seminars

Towards Conflict-Aware LLM Reasoning Grounded in Open Multilingual Knowledge Graphs

Tuesday, September 08, 2026 11:45, 4A301

Laura Balbi (University of Lisbon)

Large Language Models (LLMs) increasingly operate in settings where they must reason over heterogeneous sources of knowledgecontaining ambiguities and conflicts whose detection and interpretation requires implicit semantic understanding. Knowledge graphs provide a structured and formally defined representation of such knowledge, making them particularly valuable for studying and supporting these reasoning processes. My research investigates how well LLM-based systems detect and reason over conflicts grounded in formal knowledge graph semantics.

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Structured Clinical Reasoning with Foundation Models

Tuesday, September 15, 2026 11:45, 4A301

Zhan Qu (Scads Dresden)

Foundation models have demonstrated strong capabilities across a wide range of clinical tasks, yet their application to longitudinal electronic health records (EHRs) remains limited by the difficulty of evaluating clinically grounded reasoning, maintaining consistent patient-state representations over time, and predicting future clinical events within large, weakly structured outcome spaces. My research addresses these challenges by introducing structured methods for clinical AI across evaluation, inference, and prediction. I develop an ontology-grounded evaluation framework that jointly assesses factual correctness and patient-contextual grounding, enabling systematic identification of clinically meaningful reasoning errors beyond conventional accuracy metrics. I further investigate protocol-constrained longitudinal reasoning through persistent patient-state representations that evolve with incoming clinical evidence, improving temporal consistency, calibration, and interpretability over extended patient trajectories. Finally, I reformulate longitudinal prediction as reasoning within structured hypothesis spaces by combining medical coding hierarchies with data-driven temporal and cross-modal associations to construct patient-specific candidate outcomes before inference. Together, these studies demonstrate how explicit clinical structure can be incorporated throughout the clinical AI pipeline, integrating biomedical knowledge, longitudinal patient states, and structured hypothesis spaces to improve the reliability, interpretability, and clinical grounding of foundation models for longitudinal decision support.

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