Zhi Qin Tan
About
My research project
Robust multi-modal learning for data-driven batch and sequential inferenceA wide range of applications, such as computer vision, target tracking, finance, navigation, and robotics, involve inherently multi-modal, high-dimensional data (such as images and texts). This wealth of data provides an exciting opportunity to develop artificial intelligence (AI) techniques and also present a major challenge to develop AI solutions that generalise well when deployed in real world due to the curse of dimensionality, data heterogeneity and bias.
This project will develop robust machine learning techniques that capture and express the complexity of multimodal data in real-world AI applications, using digital health as a clinically important and methodologically convenient exemplar. Our research vision is to establish a principled process to incorporate information and uncertainty present in multi-modal, high-dimensional digital health data to improve robustness of AI solutions and accelerate their adoption. The project will focus on both batch learning and sequential, online learning.
Supervisors
A wide range of applications, such as computer vision, target tracking, finance, navigation, and robotics, involve inherently multi-modal, high-dimensional data (such as images and texts). This wealth of data provides an exciting opportunity to develop artificial intelligence (AI) techniques and also present a major challenge to develop AI solutions that generalise well when deployed in real world due to the curse of dimensionality, data heterogeneity and bias.
This project will develop robust machine learning techniques that capture and express the complexity of multimodal data in real-world AI applications, using digital health as a clinically important and methodologically convenient exemplar. Our research vision is to establish a principled process to incorporate information and uncertainty present in multi-modal, high-dimensional digital health data to improve robustness of AI solutions and accelerate their adoption. The project will focus on both batch learning and sequential, online learning.