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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
About me
Cheng Xin is an Assistant Professor of Computer Science at California State University, Fresno.
Posts
Future Blog Post
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Blog Post number 4
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Blog Post number 1
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portfolio
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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.
GRIL: A 2-Parameter Persistence Based Vectorization for Machine Learning
Published in 2nd Annual Workshop on Topology, Algebra, and Geometry in Machine Learning (TAG-ML, ICML 2023 Workshop), 2023
A stable and differentiable 2-parameter persistence vectorization for machine learning; oral presentation at the ICML 2023 TAG-ML workshop.
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.
Expressive Higher-Order Link Prediction through Hypergraph Symmetry Breaking
Published in Transactions on Machine Learning Research (TMLR), 2024
A symmetry-breaking approach that improves higher-order link prediction expressivity for hypergraph representation learning.
Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms
Published in The Thirty-Eighth Annual Conference on Neural Information Processing Systems (NeurIPS 2024), 2024
Generalizing MDS to accommodate non-Euclidean and non-metric inputs via symmetric bilinear forms.
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 39th Conference on Neural Information Processing Systems (NeurIPS 2025), 2025
Extending the Johnson-Lindenstrauss lemma to non-Euclidean geometry with fine-grained error analysis based on geometric deviation.
D-GRIL: End-To-End Topological Learning with 2-Parameter Persistence
Published in 42nd International Symposium on Computational Geometry (SoCG 2026), 2026
A differentiable GRIL-based topological learning layer for end-to-end graph and bio-activity prediction.
Locality Sensitive Hashing in Hyperbolic Space
Published in 42nd International Symposium on Computational Geometry (SoCG 2026), 2026
Locality-sensitive hashing constructions and bounds for approximate nearest neighbor search in hyperbolic space.
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.
CSCI 119-02: Introduction to Formal Languages and Automata
Undergraduate course, California State University, Fresno, Department of Computer Science, 2026
Fall 2026. This theory-centered course studies formal models of computation and the proof techniques used to reason about them. It begins with strings, languages, and finite automata, then develops the relationships among automata, regular expressions, grammars, and computational models.
CSCI 60-07: Foundations of Computer Science
Undergraduate course, California State University, Fresno, Department of Computer Science, 2026
Fall 2026. This course develops the mathematical language and reasoning used throughout computer science. Topics include abstraction, logic, data models, induction, recursion, program correctness, recurrence relations, and introductory algorithm analysis.