I’m a second-year M.S. student in the Master of Science in Intelligent Information Systems (MIIS) program at Language Technologies Institute (LTI) School of Computer Science (SCS), Carnegie Mellon University. I conduct research with Prof. Lei Li and Prof. Maarten Sap on Retrieval-Augmented Generation.
In summer 2025, I worked at TikTok Inc. as a Machine Learning Scientist Intern, exploring reinforcement learning for multimodal video content understanding.
Previously, I earned my B.Eng. in Computer Science and Technology from the Chu Kochen Honors College, Zhejiang University. I did research on time series classification supervised by Prof. Yang Yang. I also had wonderful research experiences in CHAI lab with Prof. Chenhao Tan at UChicago about evaluating long-form multimodal summarization generated by LLMs.
I’m expected to graduate in December 2025 and am seeking full-time opportunities on MLE/RS in 2026!
Here is my resume for your reference, please feel free to reach out!
🔥 News
- 2025.05: I start Machine Learning Scientist Internship at TikTok Inc.
- 2025.03: I start a new research on time-sensitive RAG benchmarking at LTI under the guidance of Prof. Lei Li!
- 2024.09: 🎉🎉 One paper about segmented time series classification was accepted by NeurIPS 2024!
- 2024.09: I start a new research on RAG’s robustness on linguistic variations at LTI under the guidance of Prof. Maarten Sap!
- 2024.08: I start my master’s program at School of Computer Science, Carnegie Mellon University!
- 2024.07: 🎉🎉 One paper about multimodal long-form summarization was accepted by COLM 2024. My first-author paper!
📝 Publications
Out of Style: RAG’s Fragility to Linguistic Variation
Tianyu Cao*, Neel Bhandari*, Akhila Yerukola, Akari Asai, Maarten Sap
Our research reveals that linguistic variations significantly impact both retrieval and generation stages. RAG systems exhibit greater sensitivity to such variations compared to LLM-only generations, highlighting their vulnerability to error propagation due to linguistic shifts.
RARE: Retrieval-Aware Robustness Evaluation for Retrieval-Augmented Generation Systems
Yixiao Zeng, Tianyu Cao, Danqing Wang, Xinran Zhao, Zimeng Qiu, Morteza Ziyadi, Tongshuang Wu, Lei Li
RARE is a unified framework designed to automatically generate synthetic, dynamic, and time-sensitive corpora for testing Retrieval-Augmented Generation (RAG) systems using domain-specific unstructured datasets.
Characterizing Multimodal Long-form Summarization: A Case Study on Financial Reports
Tianyu Cao, Natraj Raman, Danial Dervovic, Chenhao Tan
Developed an evaluation framework for LLM-generated multimodal long-form financial report summaries, integrating textual and numeric analysis.
Con4m: Context-aware Consistency Learning Framework for Segmented Time Series Classification
Junru Chen, Tianyu Cao, Jing Xu, Jiahe Li, Zhilong Chen, Tao Xiao, Yang Yang
Con4m is a consistency learning framework, which effectively utilizes contextual information more conducive to discriminating consecutive segments in segmented TSC tasks, while harmonizing inconsistent boundary labels for training.
📖 Educations
- 2024.08 - 2025.12, Master, Language Technologies Institute (LTI), Carnegie Mellon University
- 2020.09 - 2024.06, Undergraduate, Computer Science and Technology, Chu Kochen Honors College, Zhejiang Univeristy
💻 Internships
- 2025.05 - 2025.08, TikTok Inc., San Jose, CA