Summer Research

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Coordinator: Dr. Hao Liu

Faculty of Science supports undergraduate students to conduct frontier research in summer. The aim is to provide opportunity to nurture students who are interested in and have potential in conducting cutting-edge research. Students interested in inter-disciplinary research are strongly encouraged and may invite one (or more than one) supervisor for guidance.

A stipend of up to $8,000 will be provided to undergraduate students in any science discipline who have attained outstanding academic achievement and will be able to conduct frontier/cutting-edge research during the summer. Preference will be given to students conducting inter-disciplinary research. The stipend is now open for application with details below:

  1. Stipend
    • A lump sum up to HK$8,000 to each awardee for the whole period of research. The amount of stipend will depend on the merits of the research proposal.
    • Students can also apply to the First-Generation University Student Fund for additional support.
    • First-Generation University Student Fund
  2. Eligibility for Application
    All undergraduate students studying Year 2/Year 3 of 4-Year programmes offered by the Faculty of Science.
  3. Application
    Interested parties have to submit an application form with details of the research proposal stating the aims and objectives of the research project, identifying key issues and problems to be addressed, outlining research plan and methodology, and the expected deliverables. You may contact the General Office of the Department of Mathematics at 12/F, Fong Shu Chuen Library for the details. (Application deadline: to be announced)
  4. Summer Research Projects:
    • 2023-2024
      • Dr. LIU Hao, Fast algorithms for image processing
      • Dr. LIU Hao, Error bounds for approximations with deep ReLU networks
      • Dr. HOU Liangshao, Self-consistent field iteration for trace-ratio structure optimization
      • Dr. PAN Junjun, Non-negative matrix factorization for facial recognition
    • 2022-2023
      • Dr. LIU Hao, Numerical methods for partial differential equations
      • Dr. ZHOU Le, Debiased inference for heavy-tailed data in high-dimensional data
      • Dr. ZHOU Le, Feature screening for high-dimensional data
      • Dr. HON Yu Sing, Preconditioning for time-dependence PDE problems (linear)
    • 2021-2022
      • Dr. LIU Hao, The recognition methods of banana maturity period and device based on depth convolutional neural networks
      • Dr. LAM Kei Fong, Soft-body dynamics with differential equations
      • Dr. LIU Hao, Solving Partial Differential Equations by Physics Informed Neural networks and the Deep Ritz method and compare their differences
      • Dr. LIU Hao, Learning Partial Differential Equations from a Given Data Set
      • Dr. HON Yu Sing, Deep Neural Network Approximations
      • Dr. LAM Kei Fong, Generative Adversarial Nets (GANs) and Deep Fakes
      • Dr. LAM Kei Fong, Well-posedness of partial differential equations
      • Dr. LAM Kei Fong, Synthetic Financial Data with Generative Adversarial Networks
    • 2020-2021
      • Dr. LAM Kei Fong, Line search and trust region methods in optimization
      • Dr. LAM Kei Fong, Using neural networks to simulate differential equations (focus: Physics-informed neural networks)
      • Dr. LAM Kei Fong, Using neural networks to simulate differential equations (focus: Residual neural networks)
      • Dr. LAM Kei Fong, Exploring shape and topology optimization
      • Dr. HON Yu Sing, Preconditioning for time-dependent differential Equations
    • 2019-2020
      • Dr. ZHOU Zirui and Dr. TONG Tiejun, Distributionally robust convex optimization and its application
      • Dr. ZHANG Lu (COMP) and Dr. TONG Tiejun, Machine learning model for disease prediction on 22 human common diseases
      • Dr. FAN Jun, Kernel-based expectile regression and its applications
      • Dr. FAN Jun, Functional data analysis and its applications
      • Prof. LING Leevan, Numerical methods for PDEs on rough domains
      • Prof. LING Leevan, Math in cooking science
    • 2018-2019
      • Dr. FAN Jun, Robust quantile regression and its application
      • Dr. FAN Jun, Convolutional neural network and its applications
      • Dr. TONG Tiejun, Meta-analysis for continuous outcomes with non-normal distributions
      • Prof. ZHU Lixing, Order determination for spiked population models
      • Prof. ZHU Lixing, Order determination for spiked Autocovariance models
      • Prof. ZHU Lixing, Order determination for spiked Fisher models
    • 2017-2018
      • Dr. PANG Amy, Comonoids on Finite Vector Spaces
      • Dr. TONG Tiejun, Estimating the Absolute Risk Reduction and the Number Needed to Treat
      • Prof. NG Michael, Multigrid Method for Fractional Diffusion Equations
      • Prof. ZHU Lixing, Multiple Comparison-Based Test for High-Dimensional Means
      • Prof. ZHU Lixing, Permutation Test for High-Dimensional Means
      • Prof. ZHU Lixing, Testing the Equality of Covariance Matrices of Several High-Dimensional Populations
    • 2016-2017
      • Dr. TONG Tiejun, Estimating the Mean and Standard Deviation for Skewed Data
      • Prof. NG Michael, Statistical Study of Windshear Data
      • Dr. ZHANG Jin, Partial Penalty + Splitting Method for MPECs - Theory
      • Dr. ZHANG Jin, Bilevel Programming and Mpecs - Splitting Methods and Applications
      • Prof. ZHU Lixing, Dimensionality Determination in Independent Component Analysis
      • Dr. LI Yutian, Parameter Estimates for Several Stock Return Models
      • Dr. TONG Tiejun, Improvement for Confidence Interval Estimation for Number of Patient-years Needed to Treat
      • Dr. TONG Tiejun, Estimating the Mean and Standard Deviation for Uniform Data
      • Prof. NG Michael, Study of Hong Kong Observatory Data
      • Prof. ZHU Lixing, Dimension Reduction with Least Squares Formulation
      • Dr. TONG Tiejun, Statistical Issues on the Open-ended Category
      • Prof. ZHU Lixing, Determining the Number of Components in Principal Component Analysis
      • Prof. ZHU Lixing, Dimensionality Determination in Principal Component Analysis
      • Dr. YANG Can, Computationally Efficient Implementation of Statistical Methods
      • Prof. ZHU Lixing, Determining the Number of Components in Independent Component Analysis
    • 2015-2016
      • Prof. ZHU Lixing, An Investigation of Resampling Method with R Language
      • Dr. ZENG Tieyong, Image Restoration and Colorization
      • Dr. LIU Hongyu, Cloaking Devices by Metamaterials
      • Dr. NGAN Henry Y T, Outlier Detection for Large-scale Traffic Data
      • Dr. TONG Tiejun, Estimating the Sample Mean and Standard Deviation from the Summary Statistics
      • Dr. YIP Ming Ham, A Decomposition Framework for Image Denoising
      • Prof. YUAN Xiaoming, Numerical Study on Some Operator Splitting Methods for Convex Programming
      • Prof. ZHU Lixing, Programming Machine Learning Methods in R
      • Dr. YANG Can, Statistical Learning
      • Dr. LIU Kwong I, Enhancement of SAAW – Statistical Application Accessible by Web: An Online Statistics Software
      • Prof. ZHU Lixing, New Methods to Assess Validity of Regression Model and Corresponding R Language

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