Postdoctoral Exchange Program with the Institute of Data Science at the National University of Singapore

PI: Associate Professor Mehul Motani, Department of Electrical and Computer Engineering, National University of Singapore

Overview: Generalization is the ability of a learning algorithm to perform well on unseen data. Explainability deals with providing clear and understandable reasons for the predictions of a learning algorithm. In this project, we will explore generalization and explainability from an information theoretic perspective. We propose to use information theoretic metrics based on mutual information, which are scalable and efficient to compute. We aim to establish that these metrics track generalization and memorization, as well as allow for explainability and fairness analysis. This project builds on some existing work being done in my lab which has shown that information theoretic methods show great promise for the problems being proposed.

Key Objectives:

1. Explore information theoretic metrics suitable for machine learning systems
2. Study generalization performance with respect to information theoretic metrics
3. Study explainability and fairness in machine learning in the context of information theory

Key Milestones (within 12 months):

1. Establish the properties of a suitable information theoretic metric
2. Characterize the use of the metric for predicting generalization performance
3. Characterize the use of the metric for explainability purposes

Qualifications:

Essential skills:

• Information Theory
• Machine Learning Theory
• Experience with implementing deep learning frameworks
• Experience with Python and other programming languages

Bonus skills: 

• Github and online software development

Relevance to healthcare: Generalization and explainability are key issues in healthcare problems because of the life and death nature of the problems being studied. Information theoretic ideas have the potential to shed light on these two issues. While they have been explored in more general scenarios, the application to healthcare has been less explored and holds significant potential.

PI: Professor Ng See-Kiong, Institute of Data Science and School of Computing, National University of Singapore

Overview: Collaborative machine learning is an appealing paradigm to build high-quality machine learning (ML) models by training on the data from many parties. In the healthcare domain, the ability to develop machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices while preventing data leakage, will be a key enabler for AI in healthcare, as medical data are often scarce within individual institutions. However, concerns over trust and security have hindered the sharing of data as the “data silos” are inherently difficult to break at the data level, especially when personal or proprietary data are involved. We posit that the ML models can be more amenable for sharing as they are inherently more compact and self-contained with purpose-compiled knowledge from the data. Rather than requiring the learning collaborators to contribute their private data, this project will focus on enabling collaborative machine learning through allowing the collaborators to share heterogeneous black-box models, and to be appropriately incentivized based on their self-interests. Given that most current research are focused on the data level, this project will develop new model-centric collaborative machine learning methods, as well as new notions for trustable model-centric sharing and effective model management techniques for real-world model-centric platforms. We seek a postdoc who will study and develop such collaborative machine learning approaches for the healthcare domain.

Key Objectives:

1. Develop new concepts and algorithms in federated learning and artificial intelligence for trusted collaborative machine learning in healthcare
2. Be up-to-date on state-of-the-art methodologies in related technical fields (federated learning, AI) and application domains (healthcare)
3. Develop ideas for application of research outcomes
4. Contribute to knowledge exchange activities with external partners and collaborators

Key Milestones (within 12 months):

1. Collating healthcare datasets that can be used to develop/demonstrate/evaluate federated learning approaches
2. Develop (one-shot) FL methodologies for healthcare applications
3. Explore other related approaches such as ML data/model valuation, FL incentivisation techniques, machine unlearning

Qualifications:

Essential skills:

• Specialization related to machine learning and artificial intelligence for healthcare and/or prior experience in federated learning, Bayesian optimization, or privacy/security research in data sharing
• Proven ability to conduct independent research with a strong and relevant publication record
• Experienced in using the latest machine learning, AI, and big data platforms
• Excellent interpersonal communication and oral presentation skills in English

Bonus skills: 

• Github and online software development

Relevance to healthcare: While this project is focused on one-shot federated learning approaches, the postdoc will focus on such approaches in the healthcare setting. The ability to develop machine learning models over datasets distributed across data centers such as hospitals, clinical research labs, and mobile devices while preventing data leakage, will be a key enabler for AI in healthcare, given that medical data are often scarce within individual institutions.