Broadly speaking, I am interested in understanding the fundamental laws of nature, in both the smallest and the largest scales. In physics, such fields are typically classified as particle physics, which studies the smallest fundamental particles that consist nature and mediate fundamental forces, and cosmology, which studies the largest-scale properties of the universe and its dynamic evolution history.
What is fascinating is that the study of the two regimes are not unrelated, but in fact closely knit together. For instance, astronomical observations suggest the existence of dark matter and dark energy, which hint at imperfections in particle physics and quantum field theory. Similarly, particle physics is important for cosmology, because the universe was in a much smaller and hotter state at its early stage and its dynamics depends sensitively on particle physics at the smallest scales.
The amount of experimental and observational data used in these areas is extremely large and growing faster than ever before, and the analyses naturally require high-performance computing and advanced data-analysis algorithms. Hence, I am interested in developing and applying machine learning and other novel data analysis algorithms to facilitate physics research. I am also more generally interested in artificial intelligence.
I currenlty work on the ATLAS Experiment at the Large Hadron Collider (LHC), located in Geneva, Switzerland. The LHC accelerates charged particles like protons to extremely high energy (close to the speed of light) and collide them into each other. This creates an environment with extremely high energy-density—similar to the universe during its early times—where a series of energetic events that are rare or unavailable in our daily lives such as the production of heavier quarks and the Higgs boson happen. Such physical processes are observed via particle detectors located around the LHC, one of which is ATLAS.
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