FENG Yuting personal page

FENG Yuting

FENG Yuting

Research Fellow


As a Research Fellow, I am working for the WP4 of the DesCartes Program.


My collaborators are:

Vincent Y. F. Tan. He is currently an Associate Professor with the Department of Mathematics and the Department of Electrical and Computer Engineering (ECE), National University of Singapore (NUS). His research interests include information theory, machine learning, and statistical signal processing.

Junwen Yang. He is a fourth-year PhD student from the Institute of Operations Research and Analytics, National University of Singapore (NUS), advised by Prof. Vincent Y. F. Tan and Prof. Yifan Feng. His research centers on online machine learning, with a particular focus on the multi-armed bandit problem.


  • Research topics with IPAL

Current or Intended projects:

My areas of focus include but are not limited to social graph, news recommendation, information propagation, diffusion cascades, fairness and ethics. I am currently working on the two projects as follows:

  1. Influence maximization with fairness for multiple sensitive attributes

The project focuses on optimizing influence maximization in a social network while addressing fairness concerns related to multiple sensitive attributes. It aims to develop algorithms and strategies that consider the impact of influence propagation while ensuring fairness across various attributes such as gender, race, age, or location. By incorporating fairness constraints into the influence maximization process, the project aims to achieve a more equitable distribution of influence in social networks. The research will explore novel approaches to balance influence maximization objectives with fairness considerations, ultimately contributing to the development of ethically aware and socially responsible influence maximization techniques.

  1. Fairness-aware personalized news recommender systems

This project focuses on developing personalized news recommender systems for social media platforms while incorporating fairness considerations. The objective is to design algorithms and models that provide users with relevant and engaging news content, tailored for scenarios under social media, where the information diffusion and propagation mechanism thereof are considered for adoption decision, while ensuring fairness in content distribution. The project aims to address the challenges of potential bias and filter bubbles by considering diverse user preferences, demographics, and perspectives. By incorporating fairness-awareness into the recommender system, the research aims to mitigate the propagation of misinformation, echo chambers, and discrimination, ultimately enhancing the overall fairness, diversity, and quality of news consumption on social media platforms.

Both all of my current projects are related to AI.


  • Collaborators¬†

Past and current collaborator: Bogdan CAUTIS