Linglong Kong
Appointment
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
About
Linglong Kong is a Canada CIFAR AI Chair at Amii. He is an associate professor in the Department of Mathematical and Statistical Sciences at the University of Alberta, where he is a Canada Research Chair in Statistical Learning.
Kong’s research interests include functional and neuroimaging data analysis, statistical machine learning, robust statistics and quantile regression, and artificial intelligence in smart health.
Awards
- Canada Research Chair in Statistical Learning, University of Alberta, 2020
- Representative to Future Leaders Program of Japan's STS Forum, NSERC, 2018
- Great Supervisor Award, University of Alberta, 2018
- Josephine Mitchell Mentoring Award, University of Alberta, 2017
- Research Fellow, Stat and Applied Math Sciences Institute (SAMSI), 2015
Relevant Publications
- Wang, Y., Sun, K., Liu, Y., Zhao, Y., Pan, B., Jui, S., Jiang, B., and Kong, L. (2021). Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization, Proceeding of the 35th Conference on Neural Information Processing Systems (NeurIPS 2021).
- Han, P., Kong, L., Zhao, J. and Zhou, X. (2019). A General Framework for Quantile Estimation with Incomplete Data. Journal of Royal Statistical Society: Series B. Vol. 81, P. 2, 305-333.
- Mavrin, B., Zhang, S., Yao, H., Kong, L.., Wu, K., and Yu, Y. (2019). Distributional Reinforcement Learning for Efficient Exploration, Proceedings of the Thirty-sixth International Conference on Machine Learning (ICML-19).
- Zhang, L., Cobza, B., Wilman, A. And Kong, L. (2018). Significant Anatomy Detection through Sparse Classification: A Comparative Study. IEEE Transition in Medical Imaging, Vol. 37, No. 1, 128-137.
- Zhu, H., Fan, J. and Kong, L. (2014). Spatially Varying Coefficient Model for Neuroimaging Data with Jump Discontinuities. Journal of the American Statistical Association, Vol. 109, No. 507, 1084-1098.
Support Us
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.