The Summer Research Program targets for outstanding end of year 3 undergraduate students who are interested in pursuing research postgraduate study in the School of Science. The program allows participants to engage in research work under the supervision of our faculty mentors and experience research life in HKUST. Please visit http://pgsummerprogram.ust.hk/application/ to check out the details and apply! The application deadline is 17 Mar 2023.  

To support high school students to discover the mystery of science, HKUST School of Science is going to organize a series of SEVEN Science Laboratory Workshops in April and May 2023. You will be able to visit HKUST campus to physically explore science. The workshops aim at sharing with you the university learning experience in science disciplines. Through the interactive activities, you will get the exclusive opportunities to learn intellectually through some interesting science demonstration and experiments, and it helps you to explore and find out your favourite. Please visit https://bit.ly/3VW3Zxy to check out the details and register. The application deadline is 28 Feb 2023 (23:59). Come join us now!

Expected Shortfall (ES), also known as superquantile or Conditional Value-at-Risk, has been recognized as an important measure in risk analysis and stochastic optimization. In finance, it refers to the conditional expected return of an asset given that the return is below some quantile of its distribution. In this work, we consider a joint regression framework that simultaneously models the conditional quantile and ES of a response variable given a set of covariates, for which the state-of-the-art approach is based on minimizing a joint loss function that is non-differentiable and non-convex.    Motivated by the idea of using Neyman-orthogonal scores to reduce sensitivity with respect to nuisance parameters, we propose a statistically robust and computationally efficient two-step procedure for fitting joint quantile and ES regression models. With increasing covariate dimensions, we establish explicit non-asymptotic bounds on estimation and Gaussian approximation errors, which lay the foundation for statistical inference. Under high-dimensional sparse models, we consider a lasso-penalized two-step approach and its robust counterpart, and propose debased estimators for inference. We further discuss a more general weighted-average quantile regression framework, including ES regression as a special case. This talk is based on joint works with Xuming He, Kean Ming Tan and Shushu Zhang.