Dr. HO Chin Pang (何展鹏博士) BS(UCLA), MSc(Oxford), PhD(Imperial)



Assistant Professor


Contact Information Office: LAU-16-228
Phone: (+852) 3442-4031
Email: clint.ho@cityu.edu.hk
Web: Personal Homepage

Research Interests Decision Making under Uncertainty
Robust Optimization
Reinforcement learning
Operations Research



Clint Chin Pang Ho received a BS in Applied Mathematics from the University of California, Los Angeles (UCLA), an MSc in Mathematical Modeling and Scientific Computing from the University of Oxford, and a PhD in computational optimization from Imperial College London. Before joining CityU, Clint was a Junior Research Fellow (now known as Imperial College Research Fellow) in the Imperial College Business School.
Clint's current research focuses on decision making under uncertainty. He studies optimization algorithms and computational methods for structured problems, as well as their applications in machine learning and operations research.


Publications Show All Publications Show Prominent Publications
Journal Ho, C. P. , Petrik, M. & Wiesemann, W. (2021). Partial Policy Iteration For L1-Robust Markov Decision Processes. Journal of Machine Learning Research.
Ho, C. P. & Parpas, P. (2019). Empirical Risk Minimization: Probabilistic Complexity and Stepsize Strategy. Computational Optimization and Applications.
HO, C. P. , Kocvara, M. & Parpas, P. (2019). Newton-type Multilevel Optimization Method. Optimization Methods and Software.
Li, Y. , Ho, C. P. , Toulemonde, M. , Chahal, N. , Senior, R. & Tang, M.-X. (2017). Fully Automatic Myocardial Segmentation of Contrast Echocardiography Sequence Using Random Forests Guided by Shape Model. IEEE Transactions on Medical Imaging.
Ho, C. P. & Parpas, P. (2014). Singularly Perturbed Markov Decision Processes: A Multiresolution Algorithm. SIAM Journal on Control and Optimization.


Conference Paper Behzadian, B. , Petrik, M. & Ho, C. P. (2021). Fast Algorithms for L-infinity-constrained S-rectangular Robust MDPs. Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS).
Behzadian, B. , Russel, R. H. , Petrik, M. & Ho, C. P. (2021). Optimizing Percentile Criterion using Robust MDPs. The 24th International Conference on Artificial Intelligence and Statistics (AISTATS).
Ho, C. P. , Petrik, M. & Wiesemann, W. (2018). Fast Bellman Updates for Robust MDPs. 35th International Conference on Machine Learning (ICML).
Li, Y. , Ho, C. P. , Chahal, N. , Senior, R. & Tang, M.-X. (2016). Myocardial Segmentation of Contrast Echocardiograms Using Random Forests Guided by Shape Model. Medical Image Computing and Computer-Assisted Intervention (MICCAI).
Chen, L. , Tong, T. , Ho, C. P. , Patel, R. , Cohen, D. , Dawson, A. C. , Halse, O. , Geraghty, O. , Rinne, P. E.M. , White, C. J. , Nakornchai, T. , Bentley, P. & Rueckert, D. (2015). Identification of Cerebral Small Vessel Disease Using Multiple Instance Learning. Medical Image Computing and Computer-Assisted Intervention (MICCAI).
Chen, X. , Ho, C. P. , Osman, R. , Harrison, P. & Knottenbelt, W. (2014). Understanding, Modelling and Improving the Performance of Web Applications in Multi-core Virtualised Environments. 5th ACM/SPEC International Conference on Performance Engineering (ICPE).




Recruitment I am currently looking for PhD student(s)/RA(s) with strong mathematical background and exceptional proficiency in computer programming. Prospective students are welcome to email me with a CV. Exceptionally qualified students are encouraged to apply through the Hong Kong PhD Fellowship Scheme (HKPFS).



Last update date : 23 Dec 2021