Research
Four research themes
Attosecond XFELs, X-ray pulse shaping, coherent high-repetition-rate FELs, and AI for Science.
01
Attosecond XFEL
Hard X-ray attosecond pulses are a uniquely powerful route to resolving electronic motion and nonequilibrium dynamics on their natural timescales. The challenge is not merely to reach the attosecond regime, but to do so with sufficiently high peak power and with pulse properties that are actually useful for experiments.
This is my primary focus. I work on the generation and application of ultra-high-power hard X-ray attosecond pulses, with emphasis on beam shaping, pulse compression, source optimization, diagnostics, and the transition from proof-of-principle generation to robust scientific use.
02
Spatiotemporal shaping of X-ray pulses
Once pulse duration becomes controllable, the next question is whether one can also shape waveform, phase, and transverse mode content at the source. This matters because many future X-ray experiments will need not just shorter pulses, but pulses with designed spatiotemporal structure.
I study methods for directly shaping X-ray pulses in time and space, including orbital-angular-momentum control and other routes to wavefront-engineered or waveform-engineered X-ray emission.
03
Fully coherent high-repetition-rate FEL
Fully coherent FEL operation at high repetition rate is a major goal for next-generation light sources, but standard seeded FEL schemes run into practical limits, especially in seed-laser power and scalability. More generally, the question is how to retain seeded-FEL coherence while removing those bottlenecks.
In my work, the most concrete example is the self-modulation seeded FEL mechanism, which I proposed and experimentally validated. More broadly, I am interested in how self-modulation HGHG and related mechanisms can support compact, high-average-power EUV FELs as well as more general coherent high-repetition-rate FEL development.
04
AI for Science
Accelerator and FEL systems are already too high-dimensional and too nonlinear to rely only on manual tuning or brute-force scans. If we want light sources to become more adaptive and more precisely controlled, intelligent optimization must become part of the physics workflow.
I use Bayesian optimization, machine learning, and physics-informed algorithms for machine studies, FEL design, and operational optimization, especially where these methods can improve both physical insight and practical controllability.
