News

Jan 19, 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 04, 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 08, 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 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!
Oct 15, 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 15, 2023 🏆 Selected as a Grace Hopper Celebration (GHC) 2023 scholar!