Xiangchen Song (宋祥辰)
I am a PhD student in Machine Learning Department at Carnegie Mellon University, fortunately advised by Prof. Kun Zhang (CMU-CLeaR Group). Before that, I was an undergraduate student in Data Mining Group in Computer Science at University of Illinois at Urbana-Champaign, where I was advised by Prof. Jiawei Han.
research
My research lies at the intersection of Machine Learning and Causality, with a focus on interpretability through provable causal representations.
I develop causal representation learning methods for sequential data—including
time series (NeurIPS’23, NeurIPS’24, arXiv’24, ICLR’25), video (ICML’24, NeurIPS’24), and text (NeurIPS’25). More recently, I am particularly interested in extending this causal lens to study large language models through mechanistic interpretability, aiming to both understand (NeurIPS’25, MechInterp@NeurIPS’25) and control (ICML’25) their latent reasoning processes toward more reliable and principled way.
contact
The easiest way to reach me is email. My address is xiangchs [at] cs [dot] cmu [dot] edu.
news
| Sep 23, 2025 | Two papers about efficient LLM reasoning have been accepted to NeurIPS 2025 Workshop on Efficient Reasoning (ER@NeurIPS’2025)!! |
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| Sep 22, 2025 | Two papers on mechanistic interpretability have been accepted to Mechanistic Interpretability Workshop at NeurIPS 2025 (MechInterp@NeurIPS’2025)!! |
| Sep 18, 2025 | One paper “LLM Interpretability with Identifiable Temporal-Instantaneous Representation” has been accepted to The Thirty-ninth Conference on Neural Information Processing Systems (NeurIPS’2025)!! |
| May 01, 2025 | One paper “Reflection-Window Decoding: Text Generation with Selective Refinement” been accepted to The Forty-Second International Conference on Machine Learning (ICML’2025)!! |
| Jan 22, 2025 | One paper “On the Identification of Temporal Causal Representation with Instantaneous Dependence” has been accepted to The Thirteenth International Conference on Learning Representations (ICLR’2025) with oral presentation!! |