If you are from admission committee, here is a FAQ for you.
Hi, I’m a final-year undergraduate student. I am interested in understanding interaction and connection (between entities like agents, computers and humans). Currently, I collaborate with Prof. Xiang Anthony Chen and Sherry Wu. Previously, I worked with Prof. Yujun Yan at Dartmouth College, focusing on Network learning and relations modeling. I was a member of the Microsoft Research with Dr. Jiang Bian, working on LLM Agents.
I care about promoting open, collaborative, and reproducible research. I do not limit myself to specific techniques but instead always look for better solutions and good problems. My goal is to be a great professor for students in places like here. Yes, I love teaching and sharing knowledge.
In my spare time, I have keen interests in visualization communication design and VI system. Everything should be able to be expressed or explained in vivid and simple ways. Anime, movies, and music are also my favorites. I have no interest in becoming a “successful” but boring guy, interests and passion is the key to a happy life. I also have plans to make one trillion dollars, email me for details😉.
Undergraduate in Artificial Intelligence, 21.09 - 25.06
South China University of Technology
Working with Prof. Anthony Chen from UCLA and Prof. Sherry Wu from CMU. Investigate the over-reliance issues on LLM by designed large-scale user study, with quantization, statistics methods, customized webs and browser extension.
One paper submitted to CSCW 2025.
Working with Dr. Jian Bian, Microsoft Research Asia. Research on Automatic Research and Development (R&D) and Quantitative Finance Strategy. Imagine a world where the R&D process is fully automated, and the LLM can automatic generate analysis results of every proposed ideas and propose new ideas. Project we led is open-sourced in: Github/RD-Agent. I also won the highest honor “Star of Tomorrow” awarded by MSRA.
One paper accepted by ICLR 2024 AGI Workshop.
Advised by Prof. Danyang Wu. Research on the graph neural network, geometric representation learning and their applications for RNA, protein, and brain networks.
Two paper accepted by WWW 2024, two papers pending.
Responsibilities for CTR/CVR prediction and Smart Bidding. Project of the Year awarded. Achievements include:
Propose a cross-view approximation on Grassman manifold (CAGM) model to address inconsistencies within multiview adjacency matrices, feature matrices, and cross-view combinations from the two sources.
Propose an explainable stock earning framework via news factor analyzing model. Formalizing news into semantic graph and learn embedding of it as a more expressible factor. Stock earning foresting and explainable earning module are built via aggregating and utilizing news factor and numeric stock factor, achieving SOTA performance on A-stock dataset. A detailed report will be generated with the power of LLM.
Propose a simple but effective multigraph convolution networks, focusing on exacting edge-level and subgraph-level credible matrix to help cross-view interaction.
Propose an unsupervised framework to exact negative relationship between nodes, based on pseudo partial labels, and augment the original graph into a signed graph. Then signed graph neural networks are used for learning node embeddings, achieving state-of-the-art performance on both link prediction and node classification tasks.
I have been fortunate to work with many talented and dedicated individuals who have generously shared their time and expertise with me. I am grateful for their support and encouragement.
I also thanks friends in anime club, who always bring me joy and mental support.
Reach me by email if you have any questions.