I am a fourth-year (2020 - Present) Ph.D. student in the Department of Computer Science and Engineering at the University of Notre Dame, advised by Prof. Nitesh Chawla, ACM/IEEE/AAAI Fellow. Before that, I received my B.Ec. degree from Shanghai University of Finance and Economics.

My research interests lie broadly in machine learning and data mining, with a focus on graph neural networks. More specifically, I work on developing graph neural networks that can effectively handle data imbalance and data sparsity on graphs. Additionally, I also apply machine learning to advance interdisciplinary studies, such as AI for Chemistry and COVID-19 forecasting.

What’s New

• [2024/04] I passed my Ph.D. candidacy exam.
• [2024/03] One first-author paper on improving the predictions of chemical reaction yields using imbalanced regression was accepted at WWW’24.
• [2024/01] One first-author paper on enhancing pre-trained heterogeneous GNNs with prompt learning was accepted at WWW’24.
• [2023/12] I am serving as a co-organizer of the Learning on Graphs Conference (LoG) Local Meetup.
• [2023/06] I am joining Futurewei Technologies Inc. as a research intern this summer.
• [2023/04] One survey paper on graph molecular representation learning was accepted at IJCAI’23.
• [2023/04] One first-author survey paper on graph imbalance learning is available now. We have also compiled a reading list of related papers.
• [2023/01] One first-author paper on time series anomaly detection was accepted at IEEE Intelligent Systems.
• [2022/08] One first-author paper on COVID-19 forecasting was accepted at CIKM’22.
• [2022/08] One paper on time series anomaly detection was accepted at RE’22.
• [2022/06] I passed my Ph.D. qualification exam with GPA 3.89/4.0.
• [2022/04] One paper on recipe representation learning was accepted at IJCAI’22.

Contact

• Email: yma5 [at] nd [dot] edu
• Office: 384D Nieuwland Science Hall
• Location: University of Notre Dame, Notre Dame, IN 46556