Muhammad Mahmood Ahmed
Papers
2
Total Citations
27
H-Index
2
About
Muhammad Mahmood Ahmed bridges the frontiers of nanotechnology and high-performance computing, with research spanning green synthesis of nanomaterials, proteomics, and machine learning optimization. His pioneering work on incubating green synthesized iron oxide nanorods for proteomics-driven oncogenesis exploration—garnering 16 citations—demonstrates a novel fusion of nanotechnology with deep learning, where he characterized rod-shaped iron oxides averaging 17.32 nm using advanced spectroscopic and mass spectrometry techniques. This work holds promise for targeted cancer diagnostics and therapeutics. Equally impactful is his development of AAQAL, a machine learning-based tool for optimizing parallel sparse matrix-vector product (SpMV) computations using Block CSR, cited 11 times. This contribution addresses a critical bottleneck in high-performance computing for scientific and analytical applications, enhancing efficiency in solving large sparse linear systems. By integrating experimental nanomaterial synthesis with computational algorithm design, Ahmed exemplifies interdisciplinary innovation, offering tools that advance both biomedical research and computational science. His achievements highlight a commitment to solving complex, real-world problems through creative, cross-domain methodologies.
Research Focus
Key Achievements
Top Papers
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