Biography
Hajira Jabeen is a senior researcher at Biomedical Informatics (BI-K), University Hospital Cologne, specializing in Artificial Intelligence for healthcare. She develops scalable AI models and algorithms to manage and analyze complex biomedical data, focusing on robust, reproducible, and FAIR‑compliant data practices. Her work uses Knowledge Graphs, Natural Language Processing, and FAIR principles to turn heterogeneous data into actionable insights for clinical research use.
Previously, she led the Big Data Analytics team at GESIS–Leibniz Institute for the Social Sciences, where she worked on large‑scale data analytics and the development of Methods Hub, a community portal for open computational methods and workflows. She also worked as an team lead at Smart Data Analytics (SDA) on distributed semantic analytics, and served as a Data Science Expert at the University of Cologne within the CEPLAS cluster. Her research spans distributed analytics, data mining, semantic web technologies, and data FAIRification. She has contributed to multiple EU‑funded Horizon 2020 projects, building scalable data architectures across healthcare, maritime, energy, agriculture, social sciences, smart cities, and plant sciences.
Hajira is active in teaching, academic leadership, and fostering collaboration between academia and industry, with a focus on building sustainable data infrastructures and practical, transferable methods.
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Areas of Expertise
Current Teachings
RDM Journal Club
Each week, we engage in discussions on recent research papers and relevant topics in the field of Research Data Management, covering best practices, emerging challenges, and innovative solutions. The selected readings aim to enhance our collective understanding and keep us informed about the latest developments in data stewardship, FAIR principles, and related areas.
Show in KLIPSAI in Medicine Series
Artificial intelligence is already fundamentally changing medicine, but how do the underlying methods work, and what opportunities and challenges do they present? In this series of seminars, each session will cover a new, practical topic, including the basics of some AI methods, ethical challenges and possible solutions. The lectures, depending on the speaker, could be in German or English, are thematically linked but self-contained
Show in KLIPSMedical AI - Introduction to Deep Learning in Medicine and its Applications
[Der Kurs wird nur in Englisch abgehalten] This course provides a structured introduction to deep learning with a focus on medical applications. It begins by clarifying key concepts in artificial intelligence, machine learning, and deep learning, emphasizing their relevance in modern medicine. Students will explore the basic structure of neural networks and understand how models are trained and evaluated. The course then introduces convolutional neural networks (CNNs)followed by an overview of transformers and foundational models used for analyzing clinical text, genomics, and multimodal data. Real-world case studies and clinical examples illustrate how deep learning is applied across radiology, pathology, dermatology, and beyond. The final sessions explore challenges in deploying AI in clinical settings, including issues of bias, explainability, and ethical use. Optional components may include hands-on demonstrations or guided review of influential research papers in the field.
Show in KLIPS
