个人资料
- 部门： 统计与数据科学学院
- 性别：
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- 学位： 博士
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- 联系电话：
- 电子邮箱： yaozhang@nankai.edu.cn
- 办公地址： 范孙楼350
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教育经历
张瑶，南开大学统计与数据科学学院，讲师。她先后于南开大学获得学士与博士学位，期间曾赴新加坡国立大学NExT研究中心进行访问学习。她的研究聚焦于自然语言处理、知识推理和对话系统等统计学与数据科学的前沿领域。目前，她主持一项国家自然科学基金项目，曾荣获天津市科技进步二等奖，并已在ACL、SIGIR、AAAI、EMNLP等国际顶级会议及期刊上发表了十余篇高水平论文。 课题组正在招收对科研充满热情的同学，欢迎将你的简历和想法发送至：yaozhang@nankai.edu.cn 个人主页：https://yaozhangnk.github.io/
工作经历
2022年9月至今 南开大学统计与数据科学学院，讲师。
个人简介
张瑶，南开大学统计与数据科学学院，讲师。她先后于南开大学获得学士与博士学位，期间曾赴新加坡国立大学NExT研究中心进行访问学习。她的研究聚焦于自然语言处理、知识推理和对话系统等统计学与数据科学的前沿领域。目前，她主持一项国家自然科学基金项目，曾荣获天津市科技进步二等奖，并已在ACL、SIGIR、AAAI、EMNLP等国际顶级会议及期刊上发表了十余篇高水平论文。 课题组正在招收对科研充满热情的同学，欢迎将你的简历和想法发送至：yaozhang@nankai.edu.cn 个人主页：https://yaozhangnk.github.io/
研究领域
My primary research interests lie in the area of Statistics and Data Science, with a focus on the following topics:
Knowledge-Driven Complex Reasoning My research focuses on deep, multi-step reasoning. Building on foundational work in logical reasoning over structured knowledge, I now investigate how models can perform self-correction and dynamic evolution to address noisy, open-domain challenges. Trustworthy AI for Improving Doctor-Patient Communication My research in trustworthy AI aims to improve doctor-patient communication via medical NLP. I develop tools to navigate the ambiguities of clinical dialogues and accurately capture patient intent, fostering clearer and more empathetic interactions to strengthen the therapeutic relationship. Controllability in Multimodal Generation My research focuses on achieving fine-grained control in multimodal generation. I aim for precise command over the style, logic, and factuality of outputs, building on methods that generate structured content from visual data to create reliable AI partners for creative and educational applications.
论文著作
Listening to Patients: Detecting and Mitigating Patient Misreport in Medical Dialogue System. In ACL, 2025. (CCF A) Generating Questions, Answers, and Distractors for Videos: Exploring Semantic Uncertainty of Object Motions. In ACL, 2025. (CCF A) SCOP: Evaluating the Comprehension Process of Large Language Models from a Cognitive View. In ACL, 2025. (CCF A) Enhancing Cross-Lingual Dialogue Summarization through Interpretable Chain-of-Thought. In DASFAA, 2025. (CCF B) Can We Learn Question, Answer, and Distractors All from an Image? A New Task for Multiple-choice Visual Question Answering. In LREC-COLING, 2024, pp. 2852--2863. (CCF B) Exploring Union and Intersection of Visual Regions for Generating Questions, Answers, and Distractors. In EMNLP, 2024, pp.1479--1489. (CCF B) Well Begun Is Half Done: Generator-agnostic Knowledge Pre-selection for Knowledge-grounded Dialogue. In EMNLP, 2023. (CCF B) Fact-Tree Reasoning for N-ary Question Answering over Knowledge Graphs. In ACL, 2022. (CCF A) Interacting with Non-Cooperative User: A New Paradigm for Proactive Dialogue Policy. In SIGIR, 2022. (CCF A) Modeling Temporal-Modal Entity Graph for Procedural Multimodal Machine Comprehension. In ACL, 2022. (CCF A) Generalized Relation Learning with Semantic Correlation Awareness for Link Prediction. In AAAI, 2021, pp.4679-4687. (CCF A) GMH: A General Multi-hop Reasoning Model for KG Completion. In EMNLP, 2021, pp.3437-3446. (CCF B) TRFR: A ternary relation link prediction framework on Knowledge graphs. In Ad Hoc Networks, 2021, vol.113, pp.102402. (SCI II) Spatiotemporal-aware region recommendation with deep metric learning. In DASFAA, 2019, pp.491-494. (CCF B)
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