Supervision
Doctoral researchers currently working towards their thesis are listed on the Group page.
PhD theses
- Elizabeth Louis Pereira, Designing quantum sources via integrated photonics with non-Hermitian topological protection (2026, co-supervised with Andrea Blanco-Redondo and Hongwei Li)
- Fedor Nigmatulin, Emergent phenomena in two-dimensional magnetic and excitonic materials (2026, co-supervised with Zhipei Sun)
- Marcel Niedermeier, Reinterpreting tensor networks: quantum-inspired solutions to problems in condensed matter physics (2025, co-supervised with Christian Flindt)
- Pascal Vecsei, Detection of Quantum Phase Transitions with a Lee-Yang Formalism and Many-Body Algorithms (2025, co-supervised with Christian Flindt)
- Rouven Koch, Engineering quantum matter with generative machine learning (2024)
- Maryam Khosravian, Designing artificial moiré van der Waals topological superconductivity (2023, co-supervised with Peter Liljeroth)
- Guangze Chen, Designing exotic phases of matter with magnetic van der Waals materials (2023)
MSc theses
- Kasper Vesala, Inferring Fermi surfaces from quasiparticle interference patterns in metals using generative diffusion models (2025)
- Henrik Stenbrink, Moire ferroelectricity in twisted hBN with machine learning potentials (2025)
- Kautilya Patel, Machine learning many-body non-Hermitian correlated models (2025)
- Evan Dobbs, Accelerating Searching for Quantum Error Correction Codes With Reinforcement Learning (2025)
- Netta Karjalainen, Hamiltonian parameter learning in one-dimensional spin chains with machine learning (2025, University of Helsinki)
- Vilja Kaskela, Interaction-driven Unconventional Superconductivity in Correlated Multiferroics (2023)
- Michael Denner, Study of strong in-plane magnetic fields in bilayer graphene (2019, ETH Zurich)
- Patrik Weber, Dynamical Correlators of Bilinear-Biquadratic Spin-1 Chains (2019, ETH Zurich)
BSc theses
- Rafał Wilk, Solving the 1D Transverse Field Ising Model with Neural Network Quantum States (2025)
- Jeanette Törrönen, Investigating Kondo Lattice Spin Liquids with Tensor Networks (2025)
- Ashutosh Lal, Quantum Kernels: Benchmark and Comparison with Classical ML classifiers (2025)
- Subham Das, Solving the Quantum Heisenberg model with neural-network quantum states (2025)
- Parv Gagarani, Quantum-inspired Active Machine Learning for Topological Superconductors (2025)
- Quan Hoang, Mesoscopic quantum transport with quantics tensor cross interpolation (2024)
- Velican Imeläinen, Engineering Topological Superconductors using van der Waals Materials (2024)
- Pinja Hirvinen, Machine Learning for Unconventional Superconductivity (2024)
- Henri Ojanen, Deep Reinforcement Learning Design of Unconventional Superconductors (2023)
- Netta Karjalainen, Inferring underlying Heisenberg Hamiltonian from a spin spectral function for a quantum spin liquid by a neural network (2022, University of Helsinki)
- Mikael Haavisto, Topological excitations in two-dimensional multiferroics (2021)
- Vilja Kaskela, Fractional topological excitations in interacting parafermions (2020)
- Senna Luntama, Interaction induced topological superconductivity with antiferromagnetic-superconducting solitons (2020)