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Five Strategic Research Areas

AI and Arts & Humanities

AI and Arts & Humanities
AI for Science & Health
Cybersecurity
Fundamentals of AI & Data Science
Intelligent Robotics & Systems
HKU IDS explores the full range of intersection between artificial intelligence, arts, and humanities. As AI models evolve and make their way into social and economic applications, these issues will be central to AI driven growth and development. 
 
In particular, there are a number of philosophical questions that must be addressed. At a methodological level, our scholars are interested in the nature of learning and learnability and the measurement of intelligence, as well as the nature of consciousness, agency, and personhood as it applies to increasingly complex models. 
 
At a more practical level, we are also interested and actively working on better understanding the relationship between AI and human values, including safety, alignment, and unintended consequences. 
 
Finally, we are passionate about better understanding and shaping the societal impacts of AI, including governance and regulatory issues pertaining to social justice and welfare as well as legal issues surrounding patent and copyright of AI systems and the underlying infrastructure used to develop them. 

Publications & Projects​

  • Jingfeng Wu*, Difan Zou*, Vladimir Braverman, Quanquan Gu, Sham M. Kakade, Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression. Proceedings of the 39th International Conference on Machine Learning. (2022) [Long Presentation]
    Difan Zou*, Jingfeng Wu*, Vladimir Braverman, Quanquan Gu, Dean P. Foster, Sham M. Kakade. The Benefit of Implicit Regularization from SGD in Least Square Problems. Conference on Advances in Neural Information Processing Systems. (2021)
  • Difan Zou*, Jingfeng Wu*, Vladimir Braverman, Quanquan Gu, Sham M. Kakade. Benign Overfitting of Constant-Stepsize SGD for Linear Regression. Annual Conference on Learning Theory. (2021)
  • Difan Zou, Pan Xu, Quanquan Gu. Faster Convergence of Stochastic Gradient Langevin Dynamics for Non-Log-Concave Sampling. International Conference on Uncertainty in Artificial Intelligence. (2021)
  • Difan Zou, Quanquan Gu. On the Convergence of Hamiltonian Monte Carlo with Stochastic Gradients. International Conference on Machine Learning. (2021)