Prof. Anne Schwerk
IU International University of Applied Sciences
Anne Schwerk is Professor of
Artificial Intelligence at IU International University of Applied
Sciences and Deputy Department Head for IT & Engineering. In
parallel, she is a Senior Manager for Trustworthy AI at Accenture,
advising on AI governance, risk, and compliance (e.g., EU AI Act)
and leading cross-functional initiatives on responsible AI. Her
work centers on explainable and trustworthy AI, human-centered
evaluation, LLMs and NLP, and data quality for high-stakes
applications. She serves on the board of the German Data Science
Society (GDS) and speaks regularly at international forums on
explainable AI and AI in healthcare. Previously, Anne led the
TBase rollout at Charité/BIH, headed scientific management at the
Berlin Institute of Health Center for Regenerative Therapies
(BCRT), and held AI leadership roles at CENTOGENE and the German
Research Center for AI.
Speech title: "SHAP-Guided
Feature Reduction to Improve Cross-Subject Generalization in
Multimodal Physiological Emotion Recognition
Abstract: Multimodal emotion recognition from physiological signals has reported high classification performance in controlled evaluation settings, yet such results often rely on subject-dependent validation protocols that substantially overestimate real-world generalization. In this work, we systematically investigate whether explainability-driven feature reduction can improve subject-independent performance in multimodal physiological emotion recognition under strict leave-one-subject-out (LOSO) validation. Using the WESAD dataset, we study a three-class classification task (baseline, stress, amusement) based on chest-worn physiological signals and evaluate three representative model architectures: Random Forest, XGBoost, and a one-dimensional convolutional neural network. Feature relevance is quantified using SHAP values computed exclusively on training data within each LOSO fold. The resulting SHAP-based rankings are used to derive compact feature or channel subsets, followed by full model retraining under identical conditions. Across all evaluated architectures, SHAP-guided feature reduction consistently improves cross-subject generalization, supported by stable feature relevance across subjects. In particular, XGBoost trained on a reduced SHAP-selected feature set achieves statistically significant gains in both accuracy and macro F1-score compared to full-feature baselines. Class-wise analysis further reveals pronounced improvements for the amusement class, which is typically challenging to distinguish in physiological emotion recognition. These findings demonstrate that explainability can serve not only as a post-hoc analysis tool, but also as a principled mechanism for model optimization under realistic evaluation constraints. More broadly, the results highlight the importance of combining subject-independent validation with explainability-driven feature selection to obtain reliable and deployable physiological emotion recognition systems.
Asst. Prof. Ayaz H. Khan
King Fahd University of Petroleum and Minerals (KFUPM)
Dr. Ayaz H. Khan is an Assistant Professor in the Computer Engineering Department at King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, and a Research Fellow at the SDAIA–KFUPM Joint Research Center for Artificial Intelligence. His research focuses on GPU-accelerated computing, distributed systems, high-performance computing, and AI-driven data analytics. He works on scalable AI infrastructure, multi-GPU optimization, distributed communication middleware, and energy-efficient computing systems. Dr. Khan is a certified NVIDIA Deep Learning Institute (DLI) Instructor and actively collaborates with industry and research partners on AI and high-performance computing solutions. His work contributes to building scalable and sustainable intelligent systems for next-generation computing applications. Beyond his academic and professional pursuits, he is an enthusiastic traveler, having visited more than 176 cities across 11 countries.
Speech title: "
Abstract: After natural disasters such as earthquakes, floods, or wars occur, cellular communication networks often sustain significant damage or become impaired. In these critical situations, first responders must coordinate with other rescue teams to communicate essential information to central command and survivors. To address this challenge, we have developed a reliable and rapidly deployable wireless ad hoc system for post-disaster management using Data Distribution Service (DDS) middleware, specifically RTI-DDS, named R-RDSP. The R-RDSP further enhances these metrics, achieving a 14.5% improvement in end-to-end delay and a 20.24% improvement in round-trip delay over the RDSP scheme. The R-RDSP system consists of three main modules: client, relay, and server. Each module connects to others via an ad hoc network, ensuring direct device-to-device communication without relying on existing infrastructure. The client module collects and sends the victim’s location and emergency messages. The relay modules forward these messages across the ad hoc networks, ensuring minimal delay and high reliability. Finally, the server module receives the messages, processes them, and coordinates the response. Leveraging RTI-DDS for reliable message distribution, the system demonstrates robust performance even under challenging network conditions.