Prof Ahmed Ali

Theoretical Physicist with an extensive research background centered on fundamental physics, quantum field theory, and mathematical topologies.
Author of the EQST-GP framework: an 11-dimensional unification of M-theory, QCD, and cosmological dynamics.

My work focuses on leveraging advanced mathematical structures and string theory framework—including M-Theory and Calabi-Yau compactifications—to address long-standing challenges in cosmology, such as the Hubble tension and the cosmological constant.Driven by the foundational principles of theoretical physics, my research bridges the gap between deep abstract mathematics and cutting-edge computational paradigms. I specialize in developing Physics-Guided Neural Networks (PGNNs) and formulating topological learning rules, utilizing geometric deep learning to model continuous learning and advanced AI architectures. Additionally, I apply theoretical modeling to low-energy nuclear processes and screening effects, maintaining a rigorous mathematical approach across all domains of inquiry.

Current research focuses on the intersection of fundamental physics and machine learning: deriving physically-inspired loss functions, topological learning rules, and quantum-analog algorithms for scalable artificial general intelligence (AGI).