Daniel is an Associate Professor in the Science, Mathematics, and Technology Cluster at the Singapore University of Technology and Design. He obtained his PhB and PhD degrees in physics from the Australian National University. His doctoral thesis pioneered the theory of waveguide lattices hosting singularities in their energy-momentum spectrum and won the Australian Institute of Physics Bragg Gold Medal. From 2015 to 2017 Daniel worked as a Research Fellow at Nanyang Technological University, where he developed efficient numerical methods for the simulation and design of topological phases of light, predicting the existence of robust topological edge solitons and enabling the first experimental observation of Weyl points for light. Moving to a Junior Research Team Leader position at the Institute for Basic Science, Korea (2017-2020), he pioneered methods to measure bulk topological invariants using nonlinear and non-Hermitian wave media. During a Senior Research Fellowship at the National University of Singapore (2020-2024) he developed applications of machine learning techniques including topological data analysis to physics and quantum algorithms for quantum chemistry and materials science. Since 2022 he also serves as an Associate Editor for the American Physical Society.

My research broadly studies connections between different fields of physics, involving a combination of creativity, pen and paper analytical calculations, numerical simulations, and collaboration with experimental groups.

For example, photonics places particular emphasis on analogies between optical and condensed matter systems. Concepts such photonic band gaps inspired by electronic band gaps have given us new ways to guide and localise light. More recently, topology has emerged as a powerful paradigm for the design of reliable optical devices including waveguides, resonators, lasers, and sensors, which form key components of light-based technologies in the communication, data processing, and environmental sensing industries.

Similarly, applications of quantum processors currently being developed on the road to large-scale fault-tolerant quantum computer rely on identifying problems that can be shown to be analogous to some many-body quantum system. I am studying the capabilities and limitations of near-term quantum algorithms, with emphasis on applications to quantum chemistry, materials science, and topological data analysis.

I am also interested in how new machine learning and artificial intelligence techniques can change the way we do physics.

For a complete list, see my Google Scholar page.