I'm a PhD candidate in Computer Science & Engineering, Washington University in St. Louis, advised by Chien-Ju Ho. Prior to my PhD, I got bachelor degree in electronic engineering from Tsinghua University. My research focus on machine learning and human computer interactions, aiming to understand and model human decision-making process, and
design AI algorithms to change their decisions. For more information, please see my CV and Google scholar page, or email guanghuiyu@wustl.edu .
Washington University in St. Louis 2019.8-Current
Pursuing PhD degree in Computer Engineering
Tsinghua University 2015.9-2019.6
Bachelor of Engineering in Electronic Information Science and Technology
Robert Kasumba, Guanghui Yu, Chien-Ju Ho, Sarah Keren, and William Yeoh. "Data-Driven Goal Recognition Design for General Behavioral Agents."
arXiv preprint arXiv:2404.03054. 2024.
Guanghui Yu, Wei Tang, Saumik Narayanan, and Chien-Ju Ho. "Encoding Human Behavior in Information Design through Deep Learning."
NeurIPS 2023: Advances in Neural Information Processing Systems 36. 2023.
Saumik Narayanan, Guanghui Yu, Chien-Ju Ho, and Ming Yin. "How Does Value Similarity Affect Human Reliance in AI-Assisted Ethical Decision Making?"
AIES 2023" In ACM Conference on AI, Ethics, and Society. 2023.
Guanghui Yu, and Chien-Ju Ho. "Environment Design for Biased Decision Makers."
IJCAI 2022: In Proceedings of the International Joint Conference on Artificial Intelligence. 2022.
Saumik Narayanan, Guanghui Yu, Wei Tang, Chien-Ju Ho, and Ming Yin. "How Does Predictive Information Affect Human Ethical Preferences?"
AIES 2022:In ACM Conference on AI, Ethics, and Society. 2022.
Jingtao Ding, Guanghui Yu, Yong Li, Xiangnan He, and Depeng Jin. "Improving implicit recommender systems with auxiliary data."
TOIS 2020: ACM Transactions on Information Systems 38. 2020.
Jingtao Ding, Guanghui Yu, Yong Li, Depeng Jin, and Hui Gao. "Learning from hometown and current city: Cross-city POI recommendation via interest drift and transfer learning."
IMWUT 2019: Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2019.
Jingtao Ding, Guanghui Yu, Xiangnan He, Fuli Feng, Yong Li, and Depeng Jin. "Sampler design for bayesian personalized ranking by leveraging view data."
IMWUT 2019: IEEE transactions on knowledge and data engineering 33. 2019.
Jingtao Ding, Guanghui Yu, Xiangnan He, Yuhan Quan, Yong Li, Tat-Seng Chua, Depeng Jin, and Jiajie Yu. "Improving Implicit Recommender Systems with View Data."
IJCAI 2018: In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI). 2018.
Jingtao Ding, Fuli Feng, Xiangnan He, Guanghui Yu, Yong Li, and Depeng Jin. "An improved sampler for bayesian personalized ranking by leveraging view data."
WWW 2018: In Companion Proceedings of the The Web Conference 2018. 2018.
Machine Learning Engineer Intern, Apple, Cupertino 2023.3-2023.9
Developed machine learning models for user relevance inference
Data Scientist Intern, Wayfair, Boston 2022.6-2022.8
Deployed time series forecasting model to understand customer behavior and predict customer needs
Data Scientist Intern, Tencent, Beijing 2018.8-2018.10
Processed WeChat check-in behavior data, and built distributed matrix factorization model to provide POI recommendation