Zhe Xie (谢哲)
PhD Candidate · Tsinghua University
About
I am a Ph.D. candidate in the Department of Computer Science and Technology at Tsinghua University, advised by Professor Dan Pei. My research lies at the intersection of Multimodal Large Language Models, Time Series Analysis, Anomaly Detection, and Root Cause Analysis for complex systems (AIOps).
My recent research interest is enabling LLMs to natively understand and reason over time-series data. ChatTS introduced one of the first multimodal LLM that treats time series as a first-class modality. FoundRoot presents one of the first LLM-based foundation models for root cause analysis, deployed in production.
Prior to Tsinghua, I received my B.S. in Computer Science and Technology from Shanghai Jiao Tong University (GPA 3.98/4.3, Rank 4/147) in 2022, where I was a member of the Zhiyuan Honors Engineering Program (致远工科荣誉计划). I have held research internships at ByteDance and eBay, where several of my algorithms have been deployed in production systems.
News
Paper accepted to ICLR 2026: "AutoDA-Timeseries: Automated Data Augmentation for Time Series."
Paper accepted to ICSE 2026: "FoundRoot: Towards Foundation Model for Root Cause Analysis via Structured Deep Thinking."
Paper accepted to VLDB 2025: "ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning."
Joined ByteDance as a Research Intern, focusing on time series MLLMs.
Paper accepted to KDD 2024: "Microservice Root Cause Analysis with Limited Observability through Intervention Recognition in the Latent Space."
Selected Publications
† denotes equal contribution. For a complete list of publications, see my Google Scholar profile.
ChatTS: Aligning Time Series with LLMs via Synthetic Data for Enhanced Understanding and Reasoning
One of the first MLLM natively supporting time-series modality for Q&A.
GitHub Stars: 430+; Hugginface Stars: 140+; Citations: 70+; Model Downloads: 19,000+ (Mar. 2026).
FoundRoot: Towards Foundation Model for Root Cause Analysis via Structured Deep Thinking
We use RL to build one of the first LLM-based foundation models for root cause analysis, which has been deployed online.
Microservice Root Cause Analysis with Limited Observability through Intervention Recognition in the Latent Space
Multi-level root cause analysis under limited observability; algorithm deployed at eBay.
From Point-wise to Group-wise: A Fast and Accurate Microservice Trace Anomaly Detection Approach
First group-wise anomaly detection concept for traces; 20x speed improvement via graph algorithm.
Unsupervised Anomaly Detection on Microservice Traces through Graph VAE
Models traces as graphs for more accurate anomaly detection. Citations: 50+ (Mar. 2026).
Adversarial and Contrastive Variational Autoencoder for Sequential Recommendation
VAE for sequential recommendation. Citations: 140+ (Mar. 2026).
AutoDA-Timeseries: Automated Data Augmentation for Time Series
Experience
Research Internships
Time Series Multimodal Large Language Models
· ChatTS: One of the first LLM-based foundation models for time series multimodal analysis.
· FoundRoot: One of the first RL-based LLM foundation models for root cause analysis.
· ThinkTime (under review): Achieving "Thinking with Time Series" with interleaved deep thinking of time series and Python tool use in LLM.
Corpus Construction and Fine-tuning for Customer Service Dialogue Models
Results deployed in production
AIOps
3 first-author CCF-A papers; results deployed in engineering
Education
Ph.D. in Computer Science and Technology
Advisor: Prof. Dan Pei · Research: Multimodal LLM, Anomaly Detection, Root Cause Analysis
B.S. in Computer Science and Technology
GPA: 3.98/4.3 · Rank: 4/147 · Zhiyuan Honors Engineering Program (致远工科荣誉计划)