About

I am a PhD Computing Research Student (started April 2022) at the Computational Logic and Argumentation group at Department of Computing, Imperial College London, supervised by Professor Francesca Toni and Dr Francesco Leofante, funded by the Interactive Explainable AI project. I’m also a TA at the Department of Computing and Imperial College Business School, helping with several AI / ML and programming courses.

From June to December 2024, I’m doing an internship as an AI Research Associate at J.P. Morgan Explainable AI Center of Excellence, working with Dr Tom Bewley and Dr Saumitra Mishra on interpretability for large language models.

My research interest spans the spectrum of AI trustworthiness, including its explainability, robustness, fairness, privacy, etc. I’m focusing on counterfactual explanations (algorithmic recourse), robustness of explanations, and predictive multiplicity. I’m also involved in some applied projects like explainable graph neural networks for city planning and deep learning for Formula 1 race strategy.

Previously, I completed my MSc Computing degree from Imperial in 2021. I received two BEng degrees (2020) from the University of Edinburgh and South China University of Technology.

Research

2024

• Interval Abstractions for Robust Counterfactual Explanations
  J. Jiang, F. Leofante, A. Rago, F. Toni.
  Artificial Intelligence (AIJ), 2024.

• Robust Counterfactual Explanations in Machine Learning: A Survey
  J. Jiang, F. Leofante, A. Rago, F. Toni.
  The 33rd International Joint Conference on Artificial Intelligence (IJCAI), Survey Track, 2024.

• Recourse under Model Multiplicity via Argumentative Ensembling
  J. Jiang, F. Leofante, A. Rago, F. Toni.
  The 23rd International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2024.
  Nominated for Best Student Paper Award at AAMAS 2024, runner-up.

• Contestable AI needs Computational Argumentation
  F. Leofante, H. Ayoobi, A. Dejl, G. Freedman, D. Gorur, J. Jiang, G. Paulino-Passos, A. Rago, A. Rapberger, F. Russo, X. Yin, D. Zhang, F. Toni.
  The 21st International Conference on Principles of Knowledge Representation and Reasoning (KR), 2024.

• Heterogeneous Graph Neural Networks with Post-hoc Explanations for Multi-modal and Explainable Land Use Inference
  X. Zhai, J. Jiang, A. Dejl, A. Rago, F. Guo, F. Toni, A. Sivakumar.
  Preprint, 2024.

2023

• Provably Robust and Plausible Counterfactual Explanations for Neural Networks via Robust Optimisation
  J. Jiang, J. Lan, F. Leofante, A. Rago, F. Toni.
  The 15th Asian Conference on Machine Learning (ACML), 2023.

• Formalising the Robustness of Counterfactual Explanations for Neural Networks
  J. Jiang*, F. Leofante*, A. Rago, F. Toni.
  The 37th AAAI Conference on Artificial Intelligence (AAAI), 2023.

_{*equal contributions}