Mr. Lingbang ZHU was overall Champion of the Mr Armin and Mrs Lillian Kitchell Undergraduate Research Award competition in 2018. Mr. Zhu is a 3rd-year physics undergraduate student who carries out research under the supervision of Prof. Shengwang Du. In his project work “Generating Narrowband Entangled Photon Pairs from a Hot Atomic Vapor Cell”, Lingbang applied a spatially tailored hollow optical pumping beam to suppress uncorrelated noise photons from resonance fluorescence and achieved bright narrowband (2.9 MHz) biphoton generation from a Doppler-broadened hot rubidium atomic vapor cell. Lingbang’s result will have important applications in quantum information and quantum communication. Since joining Prof Du’s group in September 2015, Lingbang has coauthored 2 research papers: Applied Physics Letters 110, 161101 (2017) as first author, and Nature Communications 7, 12783 (2016) as contributing author.

The First Runner-Ups of the Mr Armin and Mrs Lillian Kitchell Undergraduate Research Award competition were Mr. Chun Tin YIP and Mr. Da Wei David REN. Mr. Yip and Mr. Ren are both 4th-year physics students. Mr. Yip carried out his award winning research “Random Walk on Complex Network and Application to Numerical Simulation for Statistical Physics” under the supervision of Prof. K.Y. Szeto. Mr. Ren carried out his award winning research “Confinement Effects on a Planar Dense Wake” under the supervision of Prof. Larry Li in the Department of Mechanical and Aerospace Engineering.

The Second Runner-Up of the Mr Armin and Mrs Lillian Kitchell Undergraduate Research Award competition was Mr. Juntao WANG. Mr. Wang is a 4th year physics undergraduate student who carried his award winning research out under the supervision of Prof. Michael K.Y. Wong. In the project entitled “Dynamics of Housing Prices”, Mr Wang applied Gaussian Process, which is a kernel method in machine learning, to investigate the dynamics of Hong Kong housing prices. By analyzing the frequency components in the auto-covariance function of the change rate of the housing prices, a new kernel function is introduced to characterize the inference relations between the data. Using the new kernel function, the Gaussian Process gives more accurate and reliable prediction of the future trends of the Hong Kong housing prices, compared with using other popular kernel functions. Mr. Wang’s previous research was published in the Proceedings of Complex Networks 2017 as contributing author and the Proceedings of the International Conference on Web Intelligence, WI’17 as first author.