TimeXAI
We develop and evaluate explainable AI methods for complex time-series classification models.
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Applied AI Lab
We develop novel methods for trustworthy and explainable AI for time series, images, and video. Our research combines methodological advances in reliable machine learning with challenging interdisciplinary applications in science, engineering, and healthcare.
Led by Prof. Dr. Jennifer Hannig at Technische Hochschule Mittelhessen and hessian.AI.
Current Research
Selected Projects
VisSURG-AID
We investigate transparent visual AI methods for reliable decision support based on surgical video.
Explore project →Selected Publications
Recent Research Outputs
Association of daily physical activity with pulmonary artery pressure in HFpEF and HFmrEF NYHA class III patients: a pilot trial—feasibility and first results
Towards Trustworthy AI in Cardiology: A Comparative Analysis of Explainable AI Methods for Electrocardiogram Interpretation
The Stuff We Swim in: Regulation Alone Will Not Lead to Justifiable Trust in AI
Video Podcast
Making AI Understandable
In this video podcast, Prof. Dr. Jennifer Hannig discusses how eXplainable AI makes model decisions more transparent and why trustworthy AI is essential in high-stakes applications.
Watch the video podcast →Open Positions
Discover the opportunities we offer!
We are always interested in connecting with motivated and talented individuals.
Whether you are a student, researcher, or industry expert, we welcome opportunities to collaborate on projects and exchange expertise.
Work with us and contribute to advancing applied artificial intelligence.
