معرفی
Linglong Kong is a Professor and Canada Research Chair in Statistical Learning at the University of Alberta's Department of Mathematical and Statistical Sciences. He is also a Fellow at the Alberta Machine Intelligence Institute (Amii) and holds a Canada CIFAR AI Chair. His expertise spans statistical machine learning, neuroimaging data analysis, and AI-driven healthcare solutions.
- Education:
- PhD in Statistics, University of Alberta (2009)
- MS in Statistics, Peking University (2002)
- BS in Probability and Statistics, Beijing Normal University (1999)
Research Interests: Kong focuses on functional neuroimaging data analysis, robust statistical methods, and applications of machine learning in healthcare. His work emphasizes fairness, privacy, and interpretability in AI systems. Key areas include differential privacy, quantile regression, and predictive modeling for medical imaging and precision medicine.
Recent Trends in Articles: His publications address cutting-edge challenges in privacy-aware machine learning, federated learning frameworks, and neuroimaging biomarkers. Recent work includes advancements in fairness for precision medicine and scalable methods for high-dimensional data analysis.
- Awards:
- Fellow of Amii (2021)
- Great Supervisor Award (2018)
- Josephine Mitchell Mentoring Award (2017)
Grants & Advising: Kong leads major grants on AI ethics, neuroimaging biomarkers, and labor market equality. He has advised numerous students, including PhD candidates and postdocs, in statistical machine learning and healthcare analytics.
Labs & Teams: Active in the Alberta Machine Intelligence Institute (Amii) and collaborates with interdisciplinary teams in neuroimaging, precision medicine, and AI ethics initiatives.
