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Location: Great Bay University, Songshan Lake, Dongguan, China Welcome to my homepage! I am Yiyan Huang, a tenure-track Assistant Professor in the School of Computing and Information Technology at Great Bay University, leading the Causal Data Science (CDS) Lab and involved in a Guangdong High-Level Young Talent Project (广东省高层次青年人才). My main research focus is Causal Inference and Data Science, at the intersection of Statistics, Operations Research, and Machine Learning. Email: huangyiyan@gbu.edu.cn WeChat: 18856958634 |
📣 Collaborations are always welcome! 📣:
本人长期关注数据科学(统计与优化)与人工智能的交叉研究,尤其是如何利用统计与优化方法提升 AI 系统的预测鲁棒性与决策一致性。欢迎相关领域的老师、同行和同学交流探讨与合作。同时,我也在招收联合培养的硕博研究生与博士后,欢迎您的推荐! I am looking for self-motivated Visiting students (访问学生), RAs (研究助理), Masters (湾大-南科大联培硕士生), and PhDs (湾大-哈工深联培博士生) to join my research team. I also have several openings for Postdoctoral (博士后) positions, with the postdoctoral certificate awarded by THU or USTC(博后出站颁发清华大学/中国科学技术大学博后联合培养证书). The salary of a postdoc ranges from 350k-400k CNY/year, and details can be found via GBU Recruitment Page.
[联系方式] Welcome to contact via Email:[huangyiyan@gbu.edu.cn] and WeChat: 18856958634.
My primary research focuses on Causal Inference and Data Science, with a multidisciplinary approach integrating Statistics, Econometrics, Operations Research, and Machine Learning. I'm also interested in applying causal inference and data science methods for solving real-world problems in fields such as Management Science, Digital Marketing, Healthcare, etc. Currently, our group is focusing on three research lines (Collaborations are always welcome!):
⭐ 1. [Theoretical] Data-driven causal decision making: Studying the contextual and non-contextual bandit problem in different settings, in order to solve the resource allocation problem in Management Science and Operations Research.
⭐ 2. [Theoretical and Experimental] Causal4ML and ML4causal: Bridging the gap between causal inference and machine learning, aiming to address trustworthiness challenges such as robust learning, distribution shifts, and uncertainty.
⭐ 3. [Experimental] Reliable/Trustworthy LLM: Using causal inference and machine learning methods to build more reliable/trustworthy Large Language Models.
主持国家自然科学基金青年项目、主持广东省地区培育项目、主持蚂蚁横向课题(因果AI决策)科研基金项目。
[2026 Spring] | Introduction to Computer Science | Undergraduate-level
[2026 Fall] | Introduction to Artificial Intelligence | Undergraduate-level
Area Chair / Reviewers for COLT、Neurips、ICML、ICLR、AAAI、AISTATS、IJCNN、ICASSP、TMLR、JMLR、TPAMI、Neural Networks、Journal of Biomedical Informatics, etc.