Feiran Jia

I am a Ph.D. student in Informatics at Pennsylvania State University (PSU), working under the guidance of Prof. Anna Squicciarini and Prof. Qingyun Wu. My research is primarily focused on the Computational Game Theory, Social Network Analysis and Artificial Intelligence,

Before coming to PSU, I completed my M.S. in Computer Science at Washington University in St. Louis, where I was advised by Prof. Yevgeniy Vorobeychik and Prof. Chien-Ju Ho. I received my B.S. in Computer Science with a minor in Finance from ShanghaiTech University.

Email  /  CV  /  Google Scholar  /  Github

Research

In Computational Economics, my research applies computational approaches to tackle intricate societal challenges, with a focus on Game Theory, Social Networks, and related Ethical Issues. In the realm of Machine Learning, my work emphasizes efficiency and robustness, exploring areas such as trustworthy machine learning, AutoML, and prompt tuning.

The paper proposes RoCourseNet, a system that enhances both prediction and explanation capabilities of machine learning models, ensuring their effectiveness despite data shifts.

The paper introduces a new framework for modeling user decisions in online personal information sharing and proposes welfare-optimizing algorithms.

The project examines test-time adaptation (TTA) and discovers a security vulnerability where malicious data in the same batch can affect predictions on benign samples, resulting in the development of Distribution Invading Attack (DIA).

This research explores using GPT-4 to solve complex math problems, including a newly proposed conversational approach called MathChat.

The project introduces a novel targeted multi-objective hyperparameter optimization approach with lexicographic preference and presents LexiFlow, a randomized directed search method.

This paper develops Structured Hierarchical Games for modeling hierarchical decision-making and introduces a gradient-based method, Differential Backward Induction, to approximate game equilibria effectively.

This paper studies how peer-pressure and random noise affect consensus-building in networked systems.

The paper introduces a multi-level game-theoretic model to analyze hierarchical policy-making decisions, specifically in response to crises like COVID-19.

This paper investigates strategies to mitigate the spread of adversarial content on social networks using game theory and optimization techniques.

The website template is from here.