Welcome
I am a theoretical physicist working on computational physics and quantum mechanical many-body theory, quantum computing, quantum machine learning, quantum technologies, and machine learning and AI applied to the physical sciences.
For those interested in thesis topics on quantum computing, quantum technologies, or machine learning applied to the physical sciences, please see the thesis projects page. You can explore my research interests using the navigation above, or visit my GitHub and Google Scholar profiles.
All teaching material and course codes I have developed over the years are freely available from my GitHub repositories — feel free to use them wherever you are located.
Research highlights
Quantum Many-Body Theory
Strongly interacting fermionic systems, emergent correlations and entanglement, nuclear structure, neutron stars, ultracold atomic gases.
Machine Learning in Physics
Neural-network quantum states, physics-informed neural networks, phase-transition detection, surrogate modeling for nuclear and quantum systems.
Quantum Computing & QML
VQE and UCCSD, quantum error mitigation, hybrid quantum–classical workflows, quantum-enhanced learning architectures, electrons-on-helium quantum hardware.