Generative AI and statistics
Delve into the intersection of the dynamic fields of Generative AI and statistics to uncover new methodologies and applications.
The objective of the ASI is to explore the frontiers of Generative AI and Statistics is to delve into the intersection of these two dynamic fields, uncovering new methodologies and applications that can enhance data-driven decision-making and innovation. By integrating statistical principles with the capabilities of generative AI, we aim to develop more robust models that can generate realistic data, improve predictive accuracy, and provide deeper insights into complex datasets. This exploration seeks to push the boundaries of what is possible, fostering advancements that can be applied across various industries, from healthcare to finance, ultimately leading to more informed and effective solutions.
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2 September — 3 September 2025
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Hong Kong Polytechnic University
Key topics
- The intersection of generative AI and statistics: Opportunities and challenges
- Bayesian inference and computational innovations: Advancing statistical machine learning in Biology
- Advancing statistical frontiers: Integrative analysis and deep learning in high-dimensional data science
- Bayesian knockoff filter for FDR control
- Toward robust and scalable Bayesian deep learning
- Exploring Bayesian structural equation modelling: Innovations and applications in medical and behavioural sciences
- Unveiling genetic complexity: Fine mapping with h2-D2 Bayesian approach in multi-ancestry settings
- Rethinking intelligence through embodiment and autonomous agents
- Collaboration and evolution of foundation and specialised models
- Integrating statistical modelling and computation: Innovations in bioinformatics, text mining, and social network analysis
- Machine learning and Bayesian approaches: Transforming empirical asset pricing in finance
- From academia to industry: Bridging statistical innovation in healthcare and actuarial science
Who the Institute is for
Early career researchers in fields relevant to the topic
If you have any enquiries about the course, please contact Ming Li by email at ming.li@polyu.edu.hk.
Course director
Hongxia Yang
Associate Dean (Global Engagement), Faculty of Computer and Mathematical Sciences, Hong Kong Polytechnic University
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