Guanghui Yu

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 . Open to job opportunities.

Education

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

Publication

• Kasumba, Robert, 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.

• Yu, Guanghui, Wei Tang, Saumik Narayanan, and Chien-Ju Ho. "Encoding Human Behavior in Information Design through Deep Learning." Advances in Neural Information Processing Systems 36. 2023.

• Narayanan, Saumik, Guanghui Yu, Chien-Ju Ho, and Ming Yin. "How Does Value Similarity Affect Human Reliance in AI-Assisted Ethical Decision Making?" In ACM Conference on AI, Ethics, and Society. 2023.

• Yu, Guanghui, and Chien-Ju Ho. "Environment Design for Biased Decision Makers." In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI). 2022.

• Narayanan, Saumik, Guanghui Yu, Wei Tang, Chien-Ju Ho, and Ming Yin. "How Does Predictive Information Affect Human Ethical Preferences?" In ACM Conference on AI, Ethics, and Society. 2022.

• Ding, Jingtao, Guanghui Yu, Yong Li, Xiangnan He, and Depeng Jin. "Improving implicit recommender systems with auxiliary data." ACM Transactions on Information Systems (TOIS) 38. 2020.

• Ding, Jingtao, 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." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies. 2019.

• Ding, Jingtao, Guanghui Yu, Xiangnan He, Fuli Feng, Yong Li, and Depeng Jin. "Sampler design for bayesian personalized ranking by leveraging view data." IEEE transactions on knowledge and data engineering 33. 2019.

• Ding, Jingtao, Guanghui Yu, Xiangnan He, Yuhan Quan, Yong Li, Tat-Seng Chua, Depeng Jin, and Jiajie Yu. "Improving Implicit Recommender Systems with View Data." In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI). 2018.

• Ding, Jingtao, Fuli Feng, Xiangnan He, Guanghui Yu, Yong Li, and Depeng Jin. "An improved sampler for bayesian personalized ranking by leveraging view data." In Companion Proceedings of the The Web Conference 2018. 2018.

Work Experience

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