Sarah Al Omari got awarded a PhD Fellowship fundamental research for her research “Exploring Neuromuscular Fatigue in Stroke Survivors: Central-Peripheral Interplay and the Potential of Transcranial Alternating Current Stimulation (tACS)” under supervision of Eva Swinnen, David Beckwée, Mahyar Firouzi and Bart Jansen.

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On April 21st 2026 at 17:00, Salar Tayebi will defend their PhD entitled “BEYOND CONVENTIONAL METHODS FOR THE CHARACTERIZATION OF INTRA-ABDOMINAL PRESSURE”.

Everybody is invited to attend the presentation in room D.2.01 or online via this link.

This PhD thesis investigates how pressure inside the abdomen, known as intra-abdominal pressure (IAP), can be better understood and monitored, particularly in critically ill patients. Elevated IAP is a clinically important condition: when IAP rises beyond normal levels, it can impair organ function and, in severe cases, lead to life-threatening complications. For this reason, there is growing recognition that IAP should be monitored more systematically, similar to other vital signs in intensive care. The thesis begins by outlining the mechanisms that lead to increased abdominal pressure. IAP can rise either because the abdominal cavity becomes less compliant or because its internal volume increases, often due to fluid accumulation during severe illness. Increases in abdominal pressure can influence other body compartments, including the chest and the brain, highlighting the systemic nature of the problem. From a physical perspective, the abdomen is described as a semi-enclosed compartment bounded by both rigid and flexible structures. The thesis then reviews current techniques for measuring IAP. Clinically, IAP is most commonly assessed indirectly via the urinary bladder, which serves as a reference standard. However, this method is intermittent and not ideally suited for continuous monitoring. As a result, there is increasing interest in alternative approaches that estimate IAP non-invasively, for example by analyzing changes in body shape or tissue mechanics. To explore this, the thesis examines the relationship between IAP and anthropometric parameters in a cohort of intensive care patients. The results show that specific body measurements are associated with IAP. These findings support the idea that externally measurable changes in body geometry may serve as useful indicators of internal pressure. Building on this concept, the thesis investigates microwave reflectometry as a novel non-invasive method for IAP monitoring. This technique uses low-power electromagnetic waves to probe the abdominal wall and detect structural changes. Through a combination of computational models, laboratory experiments, and clinical studies, the work demonstrates that changes in abdominal wall displacement can be reliably captured. In particular, the time of flight of reflected signals emerges as a robust parameter for tracking IAP-related changes. Finally, the thesis addresses an important practical issue: the dependence of IAP measurements on body position and measurement site. Clinical studies show that IAP values can vary significantly with posture and with the location of measurement, emphasizing that IAP is not a fixed quantity but a context-dependent parameter. In summary, this thesis provides an integrated understanding of intra-abdominal pressure from physiological, methodological, and technological perspectives. It highlights the limitations of current measurement techniques and presents non-invasive alternatives that could enable more continuous and patient-friendly monitoring in the future.

On October 9th 2024 at 16:30, Esther Rodrigo Bonet will defend their PhD entitled “EXPLAINABLE AND PHYSICS-GUIDED GRAPH DEEP LEARNING FOR AIR POLLUTION MODELLING”.

Everybody is invited to attend the presentation in room I.0.02.

Air pollution has become a worldwide concern due to its negative impact on the population’s health and well-being. To mitigate its effects, it is essential to monitor pollutant concentrations across regions and time accurately. Traditional solutions rely on physics-driven approaches, leveraging particle motion equations to predict pollutants’ shifts in time. Despite being reliable and easy to interpret, they are computationally expensive and require background domain knowledge. Alternatively, recent works have shown that data-driven approaches, especially deep learning models, significantly reduce the computational expense and provide accurate predictions; yet, at the cost of massive data and storage requirements and lower interpretability.

This PhD research develops innovative air pollution monitoring solutions focusing on high accuracy, manageable complexity, and high interpretability. To this end, the research proposes various graph-based deep learning solutions focusing on two key aspects, namely, physics-guided deep learning and explainability.

First, as there exist correlations among the data points in smart city data, we propose exploiting them using graph-based deep learning techniques. Specifically, we leverage generative models that have proven efficient in data generation tasks, namely, variational graph autoencoders. The proposed models employ graph convolutional operations and data fusion techniques to leverage the graph structure and the multi-modality of the data at hand. Additionally, we design physics-guided deep-learning models that follow well-studied physical equations. By updating the graph convolution operator of graph convolutional networks to leverage the physics convection-diffusion equation, we can physically guide the learning curve of our network.

The second key point relates to explainability. Specifically, we design novel explainability techniques for interpretable graph deep modeling. We explore existing explainability algorithms, including Lasso and a layer-wise relevance propagation approach, and go beyond them to our graph-based architectures, designing efficient and specifically tailored explanation tools. Our explanation techniques are able to provide insights and visualizations based on various input data sources.

Overall, the research has produced state-of-the-art models that combine the best of both (physics-guided) graph-deep-learning-based and explainable approaches for inferring, predicting, and explaining air pollution. The developed techniques can also be applied to various applications in modeling graphs on the Internet such as in recommender systems’ applications.

Since you are required to follow preparatory courses, you will need to make two applications:

– One for the preparatory program (VRB Biomedical Engineering – 00404)
– One for the master program (MA Biomedical Engineering – 00399)

First visit to NPU- Xi’an China