Leonie Weber

EDUCATION

College / University

Technische Hochschule Ingolstadt (THI)

Highest Degree

Bachelor of Science

Major Subjects

Artificial Intelligence

Country

Germany

Lab Experience

Working with EEG, Eyetracking, PPG and EDA sensor, Designing and conducting biophysiological stress experiments in flight simulator, Time series analysis, Data Preprocessing techniques, Dimensionality Reduction techniques, Machine and Deep learning techniques (Training RNNs, CNNs, Reinforcement Learning)

Projects / Research

2026: Enhanced thesis project by integrating an OpenBCI EEG device and Tobii Eyetracking device in cockpit simulator, streamed and recorded data with Lab-Streaming-Layer (LSL) at Airbus Manching
2025 – 2026: Thesis, Designed and conducted an experimental study on stress and cognitive workload detection in flight simulator and collected multimodal physiological data (PPG, EDA, skin temperature) using wearable biosensors, developed a labeled biosignal dataset based on task-induced stress scenarios and subjective workload measures and performed statistical analysis and trained Random Forest for stress classification at Airbus Manching
2025: Trained diffusion-based models (DDPM, DDIM) for MRI image super-resolution and implemented brain masks to focus learning on neuroanatomically relevant regions at THI
2024 – 2025: Worked with wearable biosensors for stress and affective state research, preprocessed physiological signals (EDA, PPG) and trained neural networks WESAD dataset for stress classification and focused on transferability of laboratory-based stress models to applied environments at Airbus Immenstaad
2024: Authored a seminar paper on the use of reinforcement learning combined with neurofeedback as a therapeutic approach for major depressive disorder at THI

Scholarships / Awards

SCIENTIFIC INTERESTS AND GOALS

With my computational background, I am fascinated by the extent to which artificial intelligence can help us better understand complex neurological diseases of the central nervous system, as well as identify neurological and cellular markers and processes that can accelerate the diagnostic process. I am particularly interested in conditions that remain relatively underexplored, such as post-viral illnesses, and in the identification of different subtypes. My goal is also to contribute to the improvement of brain organoid and computational models of the brain to make mental and neurological disorders more measurable and more quickly classifiable through the discovery of physical markers. I want to help uncover the underlying processes and patterns of neuronal communication, so that accurate diagnoses can be made more quickly and appropriate treatment can begin sooner.