About me
I am currently a PhD student in Computer Science at the Cambridge Computer Laboratory, supervised by Prof. Mateja Jamnik. My research is generously supported by the Google PhD Fellowship.
My research interest primarily lies at the intersection of structured tabular data and explainable artificial intelligence (XAI). In particular, my work focuses on:
- tabular foundation models
- tabular generative modelling
- trustworthy AI solutions for scientific applications
Before joining Cambridge, I was fortunate to work as a research intern on embodied AI at MT Lab and Cola Laboratory under the supervision of Prof. Si Liu.
🙌 If you would like to discuss anything related to XAI, please feel free to reach out via: silencejiang12138 [at] gmail [dot] com.
Last updated: November, 2025
News
🎊 Awarded with “Google PhD Fellowship” in the branch of Machine Learning and ML Foundations (1st recipient of the CST at University of Cambridge)
🌟 One first author paper accepted by DeLTa & SynthData@ICLR 2025, and awarded with “Early Career Scholar Award” (only 6 recipients worldwide)
🌟 One co-first author paper accepted by NeurIPS 2024 (with an acceptance rate of 25.8%), and awarded with “NeurIPS 2024 Scholar Award”
🌟 One first author paper accepted by ICML 2024 (with an acceptance rate of 27.03%)
🎓 Pass with Distinction for MPhil in Advanced Computer Science, and awarded with “College Prize for Academic Excellence”
Selected Publications & Preprints
(2025).
How Well Does Your Tabular Generator Learn the Structure of Tabular Data?.
ICLR 2025 Workshop.
(2024).
TabEBM: A Tabular Data Augmentation Method with Distinct Class-Specific Energy-Based Models.
NeurIPS 2024.
(2024).
ProtoGate: Prototype-based Neural Networks with Global-to-local Feature Selection for Tabular Biomedical Data.
ICML 2024.
(2023).
Exploring Time Granularity on Temporal Graphs for Dynamic Link Prediction in Real-world Networks.
NeurIPS 2023 Workshop.
(2023).
Prototype-based Neural Networks for Tabular Biomedical Data.
ICML 2023 Workshop.