Causal Representation Learning

Speaker: Dr. Guangyi Chen
Postdoctoral Research Fellow
Carnegie Mellon University (CMU)

Title: Causal Representation Learning

Date: Thursday, 13 November 2025

Time: 11:00am - 12:00noon

Venue: Room 2303 (via lift 17/18), HKUST

Abstract:

Traditional deep learning methods heavily rely on statistical correlations, often at the expense of generalization, robustness, and interpretability. In contrast, classical causal discovery techniques are well-suited for identifying causal relationships in structured tabular data but face significant challenges when applied to unstructured, high-dimensional inputs such as images and videos. Causal representation learning bridges this gap by uncovering the latent causal structure underlying observations. In this talk, we introduce the foundational principles of causal representation learning and its growing importance in trustworthy AI systems. Specifically, we discuss two central research questions:

  1. Under what theoretical conditions can causal factors be identified from observed unstructured data?
  2. How can learned causal representations improve the transferability, transparency, controllability, and attribution of AI systems in real-world applications?

Biography:

Guangyi Chen is a postdoctoral research fellow at Carnegie Mellon University (CMU) and a research scientist at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI). He currently co-leads the Causal Learning and Reasoning (CLeaR) Group with Prof. Kun Zhang. Prior to that, he received both his Ph.D. and B.S. degrees from Tsinghua University. His research interests include causality, representation learning, and visual understanding. A central focus of his work is to develop principled and practical methods for learning meaningful representations from visual data that support understanding, generation, and reasoning. He has published over 50 papers in top-tier machine learning and computer vision conferences, including NeurIPS, CVPR, ICLR, and so on, with several recognized as highlights or oral presentations. He also co-organized the Causal Representation Learning workshops at NeurIPS 2024 and ICDM 2024.