Research
- Study physics in solid-state materials: how defects & quasiparticles affect their electrical and optical properties.
- Analyze defect states and local structure with transient spectroscopy (PICTS/DLTS) and tip-enhanced nanospectroscopy.
- Develop and apply interpretable machine learning to extract physically meaningful information from experimental data.
1. Defect Physics in Solid-State Materials
How do defects and quasiparticles shape the electrical and optical properties of solid-state materials?
- Defect states in oxide semiconductors and their relation to device characteristics and stability.
- Defect-mediated control of carrier type in 2D semiconductors for complementary devices.
- Exciton modulation in 2D semiconductors through coupling to plasmonic nanostructures.
2. Defect and Nanoscale Spectroscopy
How can defect states and local structure be measured, from devices to the nanoscale?
- Transient spectroscopy (PICTS/DLTS) of defect states in 2D semiconductors.
- Nanoscale mapping of defects and strain in 2D materials by tip-enhanced spectroscopy.
- Electromagnetic simulation of near-field enhancement in tip-enhanced spectroscopy.
3. Interpretable AI for Science
How can machine learning extract physically meaningful information from experimental data?
- Interpretable machine learning relating Raman spectra to electrical properties.
- Deep-learning noise reduction of experimental spectra that preserves spectral parameters.
- Simulation of analog in-memory learning based on measured synaptic characteristics of ferroelectric InSe neuromorphic devices.
- Numerical precision and the reliability of scientific machine learning.
- Review of the physical interpretability of machine-learning analysis in spectroscopy.