Research Areas
AI for Scientific Discovery
Using machine learning to accelerate scientific progress, particularly in theoretical physics
Theoretical & Computational Physics
Foundations of physics, quantum algorithms, relativity
Deep Learning & Symbolic AI
Transformer architectures, representation learning, and symbolic regression for interpretable models
Physics-Informed Machine Learning
Incorporating physical laws and symmetries into neural network architectures
Research Experience
My interdisciplinary background has shaped my research interests at the intersection of physics and machine learning:
Currently, I am working on foundation models for gravitational wave analysis at Radboud University, exploring transformer-based symbolic regression for interpretable modeling of gravitational wave signals.
Previously, I interned at Forschungszentrum Jülich's PGI-8 Institute of Quantum Control, where I developed metrics for learning wave function complexity using Neural Quantum States (NQS), employing transformer networks and information theory to understand quantum many-body systems.
I also worked on Lagrangian Neural Networks (LNN) and symbolic regression under the guidance of Assistant Professor Arkadaş Özakın at Boğaziçi University, exploring how machine learning can uncover theoretical quantities like the Lagrangian from data alone. This project exemplified how AI can be utilized to extract theoretical quantities from experimental data. I later shared these findings in a seminar I organized at my university.