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Posts by Collection
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publications
Computing Bottleneck Distance for 2-D Interval Decomposable Modules
Published in 34th International Symposium on Computational Geometry (SoCG), 2018
Present an efficient algorithm to compute the bottleneck distance 2-parameter interval decomposable models.
Generalized Persistence Algorithm for Decomposing Multi-parameter Persistence Modules
Published in Journal of Applied and Computational Topology (JACT), 2022
Generalize the persistence algorithm to compute decompositions of multi-parameter persistence modules.
DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-based 3D Vision
Published in IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024
Presenting a massive 10K real-world scene benchmark dataset for 3D vision with 51.2 million frames.
Optimally Improving Cooperative Learning in a Social Setting
Published in 41st International Conference on Machine Learning (ICML), 2024
Algorithms for optimally fixing classifiers in a cooperative network to maximize aggregate or egalitarian accuracy.
TopInG: Topologically Interpretable Graph Learning via Persistent Rationale Filtration
Published in 42nd International Conference on Machine Learning (ICML), 2025
A topologically interpretable GNN with a novel topological discrepancy loss is proved to be uniquely optimized by ground truth.
Johnson-Lindenstrauss Lemma Beyond Euclidean Geometry
Published in Conference on Neural Information Processing Systems (NeurIPS), 2025
Extending the Johnson-Lindenstrauss lemma to non-Euclidean geometry with fine-grained error analysis based on geometric deviation.
talks
Talk 1 on Relevant Topic in Your Field
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Conference Proceeding talk 3 on Relevant Topic in Your Field
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teaching
CS 513: Design and Analysis of Algorithms
Graduate course, Rutgers University, Computer Science Department, 2025
This is a graduate-level course on the design and analysis of algorithms. We will explore fundamental techniques for designing and analyzing efficient algorithms for computationally challenging problems. The course will cover classic paradigms as well as modern algorithmic techniques. The goal is to provide students with a robust theoretical foundation and an understanding of the mathematical tools needed to tackle complex algorithmic problems in their own research.