Linglong Kong
Appointment
Canada CIFAR AI Chair
Pan-Canadian AI Strategy
About
Appointed Canada CIFAR AI Chair – 2021
Linglong Kong is a Canada CIFAR AI Chair at Amii. He is a 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. He is an associate editor for several journals, including the Canadian Journal of Statistics, the Journal of the American Statistical Association, Applications & Case Studies, and Statistics and Frontiers in Neuroscience.
Awards
- Fellow of the American Statistical Association (ASA), 2024
- 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
- Jiang, Y., Chang, X., Liu, Y., Ding, L., Kong, L., and Jiang, B. (2023). Gaussian Differential Privacy on Riemannian Manifolds, Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023).
- Liu, Y., Hu, Q., Ding, L., Jiang, B. and Kong, L. (2023). Online local differential private quantile inference via self-normalization, Proceedings of the 40th International Conference on Machine Learning (ICML 2023).
- Liu, Y., Sun, K., Jiang, B., and Kong, L. (2022). Identification, Amplification, and Measurement: A bridge to Gaussian Differential Privacy, Proceeding of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022).
- Liu, M., Ding, L., Yu, D., Liu, W., Kong, L. and Jiang, B. (2022). Conformalized Fairness via Quantile Regression, Proceeding of the 36th Conference on Neural Information Processing Systems (NeurIPS 2022).
- 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).