About Me
I am a PhD student in the field of quantum machine learning, affiliated with the Optimization of Machine Learning Systems Group at the University of Basel, under the supervision of Prof. Dr. Aurelien Lucchi and Prof. Dr. Jiři Černý. My research is funded by the SNSF and involves collaborative efforts with IBM Research Zürich.
In my free time I enjoy experimenting with the musical production of various genres and arrangements. Some of my creations are archived on my YouTube channel.
Research
I am currently focused on the mathematical analysis of the loss landscape of quantum neural networks. Specifically, my work involves the design of initialization strategies that optimally balances the expressive power, and the trainability of the model. With "trainability" I mean escaping the barren-plateau problem, which is crucial for enabling learning through gradient-based methods.
Furthermore, I am interested in questions related to quantum advantage, i.e. situations where quantum devices exhibit speedups compared to classical machines; a field that belongs to the category of computational complexity theory.
I am also keen to explore the training dynamics of these models in the near future.
Conferences & Workshops
QTML 2024: 8th International Conference on Quantum Techniques in Machine Learning. University of Melbourne, Australia.Teaching
Fall Semester 2023: Teacher Assistant in Mathematics of Data Science- Introduction to probability theory and mathematical statistics
- Random matrix theory, concentration inequalities, and dimensionality reduction
- Graph theory, spectral clustering, and diffusion maps
Curriculum Vitae
2023 - Present: PhD in Quantum Machine Learning, University of Basel2021 - 2023: Master of Science in Mathematics, University of Basel
- Specialized in Algebraic Number Theory, Analysis of PDE & Stochastic Analysis
- Master Thesis on the Hessian Eigenspectrum of Nonlinear Neural Networks
- Electives in Theoretical Quantum Mechanics
- Electives in Physics & Computer Science