Condensed Matter Theory
Prof. Chung is working on numerical simulations of quantum many-body systems in condensed matter. One focus is on out-of-equilibrium dynamics in one dimensional topological systems, for example the Kitaev chain with interaction. Another direction is related to understanding superconductivity in cuprate high-temperature superconductors. He is also interested in developing efficient numerical techniques, mainly based on tensor network state and quantum Monte Carlo algorithms.
Prof. Huang is a theorist in the field of condensed matter physics and is skilled in group theory, quantum field theory, many-body physics, machine learning, etc. His specialties are symmetry analysis and mathematical modeling on systems including superconductors, charge density waves, skyrmions, multiferroics, and topological materials.
High Energy and Gravity
Prof. Giataganas works on high energy theoretical physics. His main research interests focus on the study of Strongly Coupled Systems using the Gauge/Gravity duality and the deeper understanding of the Black Hole Physics and Quantum Gravity. Part of his research includes the study of chaos in quantum theories. He is also interested in the application of theoretical physics frameworks, like the Renormalization group flow, on the theory of Neural Networks aiming to a better understanding of the evolution of information through the machine learning methods.
Prof. Ju's background is in formal theory. He is interested in geometric approaches to various fields in theoretical physics, including quantum information, quantum optics, string theories, and supersymmetric theories. Since many studies have shown that non-Hermitian quantum systems are incompatible with conventional quantum mechanics, he is now focusing on a geometric construction of (non-)Hermitian quantum mechanics that can accommodate both Hermitian and non-Hermitian quantum systems.
Material Sciences
Prof. Chiu is applying methods and models from computational chemistry to study chemical systems of interest, e.g. gas sorption or catalysis and other surface processes, just to name some examples. He uses quantum chemistry programs to investigate the energy profile of a reaction and use these data to model the kinetic and the thermodynamic behavior of a system. He also deals with some (simple) methodological aspects of simulations, e.g., how to convert the calculated energies for a complex system into macroscopic observables in both an effective and accurate way.
Prof. Chuang research focuses on understanding solid-state materials systematically via computer algorithms and theoretical modelling. This approach includes the use of various levels of total energy calculations, such as force field molecular dynamics, tight-binding molecular dynamics, first-principles calculations, density functional theory, and beyond. For the study of systems consisting of more than tens of atoms genetic algorithms need to be used, such as the Wang-Landau algorithm, and the greedy algorithm. He is also using machine learning techniques, such as high-throughput and neural networks for the study of novel materials. These algorithms were specifically developed to solve various problems encountered in the field of materials physics.
Prof. Lin adopts first-principles calculations and machine learning methods to investigate piezoelectric/ferroelectric materials, metallurgy, energy materials and catalytic reactions. Based on fundamental physics understanding, he designs and explores new materials and composite materials with known materials as building blocks, such as defect/doping engineered materials, superlattices, organic structures.
Prof. Lüder develops machine learning approaches to advance computational material science methods, including theoretical X-ray spectroscopy simulation and first-principles methods such as Density Functional Theory and Orbital-free Density Functional Theory .