4-Year PHD Studentship in Diffusion based generative models and their application to generative data augmentation and uncertainty quantification (NTU, Singapore) Nanyang Technological University (NTU) in Singapore

Diffusion-based generative models for images such as Ho et al. (2020), Dockhorn et al. (2021) have made significant impact, and nowadays become routinely used in smartphones.

An interesting potential application of such methods for Machine Learning applications is to use them to increase the diversity of existing datasets via generative data augmentation, allowing us to provide better performance, or similar level of performance with much less labelled training data.

Although there have been promising initial results in this area such as Zheng et al (2023), this is not yet completely understood, and there are still significant improvements needed in computational efficiency before such methods can be widely adopted.

We will develop new algorithms that will be numerically evaluated on various machine learning test problems. These methods will improve the consistency and computational scalability of generative data augmentation.

Explainability and uncertainty quantification will be considered as well.

In addition to algorithmic advances, the project will also develop a mathematical theory showing consistency results for our methods.

Students with strong computational and mathematical backgrounds are encouraged to contact me directly at [email protected] . You can find out more about my research at my website https://sites.google.com/site/paulindani/ .

A fully funded NTU Research Scholarship is available for this project,

https://www.ntu.edu.sg/admissions/graduate/financialmatters/scholarships/rss#Content_C006_Col00

For international applicants, this is 2700 SGD for the first year, and 3200 SGD later on after passing the required courses and doing an oral presentation about the research plans.

Applications for an August 2025 start will be considered until the 15th of May, 2025.