13 Prof. QIAO Zhonghua conferred RGC Research Fellow 2020/21 Prof. Qiao has been making significant contributions to numerical analysis and scientific computing, in particular to numerical investigations of nonlinear partial differential equations in phase-field simulations, which have become increasingly important in many applications on the phase transition of multi-component mixtures. He has undertaken a systematic study of the numerical approximation for the Cahn-Hilliard type equation, a key component of phase-field modelling. Prof. Qiao has also designed and analysed semi-implicit unconditionally energy stable numerical methods for solving phase field models, on which he has introduced an efficient adaptive time stepping method. Prof. Qiao was selected as one of the ten awardees of the RGC Research Fellow Scheme 2020/21 with his project “L-infinity Stability of Exponential Time Differencing Numerical Schemes for Phase Field Models with High-order Dissipations”. Prof. Qiao is now focusing mainly on providing a theoretical justification of L-infinity boundedness of numerical solutions of exponential time differencing methods for phase field models with high-order dissipation. Prof. SUN Defeng conferred RGC Senior Research Fellow 2022/23 Department of Applied Mathematics In this research project "Nonlinear Conic Programming: Theory, Algorithms and Software", Prof. Sun and his team will conduct a thorough study from both the theoretical perspective and the numerical perspective on the large-scale nonlinear conic programming problem (NLCOP). They will move forward to investigate the strong regularity for the Karush-Kuhn-Tucker points of the NLCOP. To achieve this goal for the NLCOP, they will investigate the calmness of the solution mappings and the quadratic growth conditions of the NLCOP. From the perspective of algorithmic design, they will design Newton type algorithms and the augmented Lagrangian method (ALM) for solving the NLCOP. Moreover, as an exploration research topic, they will investigate the reinforcement learning techniques for tuning parameters in the designed optimisation algorithms. The potential success of this project can make a significant contribution to the optimisation research and many real applications. Prof. CHEN Xiaojun received RGC Collaborative Research Fund 2021/22 Prof. Chen is the Associate Director of the University Research Facility in Big Data Analytics. Her research interests include optimisation theory and algorithms with applications in data sciences. She has made a significant contribution to mathematical optimisation, stochastic variational inequalities, and nonsmooth analysis. In 2022, she received an RGC Collaborative Research Fund titled “High performance deep learning clusters for big data analytics”. Prof. Chen has been collaborating with distinguished researchers from the Department of Computing, Department of Land Surveying and Geo-Informatics, School of Accounting and Finance, School of Optometry in PolyU and professors in computing science and applied mathematics from local universities. The new high-performance deep learning clusters will enhance the collaborative research for big data analytics in smart cities, medical imaging analytics, digital economy, data storage and retrieval using peptides, etc. Our Achievements
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