News
| Mar 2026 | 🎤 I’m giving a talk at SIAM UQ 2026 on “Constraint-Guided Conditional Diffusion for Power-Grid Generation”! |
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| Jan 2026 | 🎤 We’re giving a talk at JMM 2026 on “Topological Deep Learning for Energy Systems: From TDA Features to Higher-Order Relations”! |
| Oct 2025 | 🎤 Invited talks at AMS Fall Southeastern Meeting: “Learning Topological Signatures: TDA and ML at Scale”! |
| Jan 2025 | 🌟 Our paper “Learning production functions for supply chains with graph neural networks” has been accpeted for an oral presentation at AAAI 2025 (top 5%)! |
| Nov 2024 | 🎓 I defended my dissertation, “Metric Learning on Topological Descriptors”! Special thanks to my thesis committee and all the wonderful people who have supported me throughout my PhD. |
| Aug 2024 | 🎉 We got the Best Paper Award at VIS 2024 (top 1%)! I’m honored to be giving a plenary talk right after the opening ceremony. |
| Jul 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 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 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 2023 | 🛎 I’m attending NeurIPS 2023! I’m serving on the program committee for Symmetry and Geometry in Neural Representations (NeurReps) this year! |
| Oct 2023 | 🎤 I’m attending IEEE VIS 2023! Check out our works in TopoInVis and EnergyVis. In TopoInVis, we propose a method to visualize topological importance [slide] , and in EnergyVis, we use Topological Data Analysis (TDA) to detect extreme climate events at scale [slide]. |
| Sep 2023 | 🏆 Selected as a Grace Hopper Celebration (GHC) 2023 scholar! |