Ahmad Bin Qasim
Doctoral Student at DKFZ German Cancer Research Center
Heidelberg, Baden-Württemberg, Germany
Summary
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Senior🎓
Top SchoolAhmad Bin Qasim is a Clinical AI Consultant and doctoral researcher in bioinformatics with 11 years of experience applying machine learning to biomedical problems. Trained at Technical University of Munich and currently completing a PhD at DKFZ, he has focused on label-efficient deep learning for biomedical images—combining active, self-supervised and semi-supervised methods during his MSc thesis and doctoral work. His background spans industry and research, from data-science roles in insurance and backend development to hands-on deep learning engineering at Helmholtz centers. Ahmad blends practical deployment experience with rigorous academic publication, as reflected on his GitHub and Google Scholar profiles. Based in Heidelberg, he brings a pragmatic, interdisciplinary approach to translating AI methods into clinically relevant tools. An underappreciated strength is his track record of moving projects from prototype (working-student roles) to research-grade solutions, bridging gaps between code, data, and clinical impact.
11 years of coding experience
3 years of employment as a software developer
Master of Science - MS, Informatics, 1.5/4.0 (lowest grade to pass: 3.7), Master of Science - MS, Informatics, 1.5/4.0 (lowest grade to pass: 3.7) at Technical University MunichBachelor of Science - BS, Computer Science, 3.63/4.00 (lowest grade to pass: 1.3), Bachelor of Science - BS, Computer Science, 3.63/4.00 (lowest grade to pass: 1.3) at National University of Science and TechnologyIntermediate Certificate, Pre-Engineering, A+ (lowest grade to pass: D), Intermediate Certificate, Pre-Engineering, A+ (lowest grade to pass: D) at Punjab College of Science
English, Urdu, German
Github Skills (27)
biomedical10
semi-supervised-learning9
supervised-learning9
medical9
medical-imaging8
active-learning8
classification8
regression8
datasets8
imaging8
semantic-segmentation7
image-segmentation7
deep-learning6
segmentation6
pipeline6
Programming languages (2)
Github contributions (5)
marrlab/Med-AL-SSL
Jul 2020 - May 2021
Repository for implementation of active learning and semi-supervised learning algorithms and applying them to medical imaging datasets
Contributions:249 commits, 28 PRs, 230 pushes in 10 months
supervised-learningsupervisedactive-learningdeep-learningsemi
AhmadQasim/unet-segmentator-brats
Sep 2019 - Mar 2020
Modifying the U-net implementation by qubvel (https://github.com/qubvel/segmentation_models) for the BRATS dataset
Contributions:9 commits, 2 PRs, 1 push in 6 months
datasetu-netbratssegmentation-modelssegmentation
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