Abdul Haseeb

• Developed ML pipelines for single-cell cytometry data, modeling patient samples as point clouds of cell marker features. • Trained PointNet, DGCNN, Point Transformer, and graph-based models for patient-level disease classification. • Built preprocessing workflows for heterogeneous datasets with varying marker panels and cell counts. • Applied XAI methods to identify disease-relevant cells and protein markers, supporting collaboration with biologists.

• Curated high-quality datasets for Human Activity Recognition in collaboration with TU Dortmund. • Annotated and managed training data using Sciebo, ensuring consistency and ML-readiness. • Coordinated progress meetings, tracked milestones, and supported timely project completion. • Collaborated with other student assistants to identify and resolve data preparation discrepancies, improving data quality and workflow consistency.

Grade: 1.6 | Coursework • Advanced Artificial Intelligence • Explainable Artificial Intelligence • Data Driven Methods in Signal Processing • Computer Architectures in Deep Learning • Cloud Computing • Computational Intelligence in Automation • Digital Signal Processing • Automation Technologies • Radio Navigation and Radar • Modelling and Simulation of Mechatronics Systems