Junchi Yu

I am a 5th-year Ph.D. candidate at the Insititute of Automation, Chinese Academy of Sciences, advised by Prof. Ran He.

Previouly, I was a research intern at Tencent AI LAB, working with Dr. Tingyang Xu, Dr. Yu Rong, Dr. Yatao Bian and Prof. Junzhou Huang. I was a visiting student at the Department of Computer Science, Yale University, working with Prof. Rex Ying.

I aim to develop Trustworthy Graph Learning (TwGL) methods with good interpretability and transferability, and explore their biochemistry applications. Recently I also try to extend the success in graph learning to complex reasoning with Large Language Models (LLMs).

Improve the complex reasoning ability of LLMs in the graph, text, and 3D world by analogical reasoning.

A Robust and Interpretable Rumor Detection Method by Exploiting Diverse Counterfactual Evidences.

Practical Label-invariant Augmentation Framework for Graph Out-of-Distribution Generalization By Discovery of Diverse Label-invariant Subgraphs.

VAE-based Molecule Optimization Framework with Limited Structural Modification to the Input Molecules.

A variational framework of our prior GIB with improved stability and training efficiency.

An extended publication of GIB with more theoretic analysis on IB-Subgraph properties and the GIB optimization strategy.

An Information-theoretic framwork, namely Graph Information Bottleneck, for interpretable and robust graph learning.

A generative framework for cross-domain face recognition.

Tutorial:
Trustworthy Graph Learning: Reliability, Explainability, and Privacy Protection @ KDD 2022,
Advanced Deep Graph Learning: Deeper, Faster, Robuster, and Unsupervised @ WWW 2021.

Reviewer:
Journals: IEEE Transactions on Pattern Analysis and Machine Intelligence, Pattern Recognition.
Conferences: ICML, NeurIPS, ICLR, CVPR, Ph.D. Consortium@KDD.