Yu (Demi) Qin
Hello! Welcome to the space of 秦瑜, and you can call me Demi 👋
I’m a Ph.D. candidate in Computer Science at Tulane University, specializing in machine learning, topological data analysis, and advanced visualization. My dissertation, titled Metric Learning on Topological Descriptors is advised by Prof. Brian Summa and Prof. Carola Wenk.
My research falls at the intersection of machine learning (ML), visualization (VIS) and topological data analysis (TDA). I aim to push the boundaries of how we understand and interpret complex data efficiently. Using advanced visualization techniques and ML, I enhance large data analysis and explore the shapes and geometries of complex datasets, from scalar fields and images to 3D shapes and graphs. These techniques enable scalable data capture and analysis, potentially improving decisions that affect billions daily.
My work finds applications in diverse areas: enhancing medical imaging for better diagnostics, advancing climate models for precise weather forecasting and climate change analysis, and increasing efficiency in supply chain management.
news
Aug 08, 2024 | 🎉 We got the Best Paper Award at VIS 2024! I’m honored to be giving a plenary talk right after the opening ceremony. |
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Jul 29, 2024 | 🌟 Our paper “Rapid and Precise Topological Comparison with Merge Tree Neural Networks” has been accpeted by IEEE VIS with an acceptance rate of 22.26%! The paper will be published in the special issue of the IEEE TVCG jounral. Looking forward to seeing everyone in St. Pete Beach. |
Jul 15, 2024 | 🛎 I’m participating in the ICML Topological Deep Learning Challenge 2024! I’m proposing a novel topological lifting approach based on node attributes to convert a graph into a hypergraph. Check out our implementation details here. |
Apr 10, 2024 | ✨ New Paper! We introduce Merge Tree Neural Networks (MTNN), a leanred neural network model specifically designed for merge tree comparison. MTNN dramatically enhances the speed and quality of similarity computations, and speeds up the prior state-of-the-art by more than 100x on the benchmark datasets while maintaining an error rate below 0.1%, more details on [paper]. |
Dec 15, 2023 | 🛎 I’m attending NeurIPS 2023! I’m serving on the program committee for Symmetry and Geometry in Neural Representations (NeurReps) this year! |