As a student who studies in the United States now, I have attended several conferences across the U.S. and Europe. One of the most rewarding outcomes is the chance to engage with local researchers. The forum presents an opportunity to connect with researchers and brilliant minds in Hong Kong, fostering potential future collaborations and building connections.
I also see the forum will be a place for both learning and giving. Being a Hong Kong resident, I have long heard of the Shaw Prize and its role in recognizing important contributions to science. Meeting with these scholars in the field would be a precious experience. I look forward to learning from their valuable insights and perspectives which would inspire my research. At the same time, I hope to give back by sharing my story as a PhD student abroad. Having been an undergraduate in Hong Kong myself, I understand uncertainties students may feel about pursuing research or studying overseas. I would be glad to share my experience and offer guidance to the younger generation.

The Chinese University of Hong Kong

My research interest is astrophysics with gravitational waves. I have been working on gravitational wave (GW) lensing since undergraduate. When a gravitational wave is lensed by a massive object, there are multiple copies of the signal with different magnifications and arrival times. We study the microlensing of GW by stellar objects and compute the wave optics amplification factor which is introduced when the size of the lens is comparable to the wavelength of the GW. Recently, we are working on the diffraction effect of lensing near caustic.
In addition, I also work on the population inference of GW. Astrophysical models, which are based on our understanding of the universe, can be built to describe the distribution of the black hole parameters including masses, spins, and redshifts. In the standard approach, posterior distributions of the black hole parameters undergo Monte Carlo integration to obtain the population parameters. We study the systematics of replacing the posteriors with density estimates using machine learning techniques including Gaussian Mixture Model and Normalizing Flow.