Bio
Cheng Xin is an Assistant Professor of Computer Science at California State University, Fresno. He received his Ph.D. in Computer Science from Purdue University under the supervision of Dr. Tamal K. Dey and was a postdoctoral researcher at Rutgers University advised by Dr. Jie Gao. His research develops trustworthy and theoretically grounded AI systems by connecting topology, geometry, algorithms, and multi-agent interaction. His work spans interpretable graph learning, non-Euclidean representations, topological data analysis, AI for Science, and reliable collective learning, with publications in NeurIPS, ICML, CVPR, and SoCG.
Research Interests
Topological Machine Learning
Non-Euclidean Geometry in ML
Multiparameter Persistence
Interpretable AI
Trustworthy & Multi-Agent AI
AI for Science
Academic Appointments & Education
Assistant Professor of Computer Science
- Research on trustworthy and multi-agent AI, topological and geometric learning, and theoretical foundations for reliable learning systems
- Teaching mathematical foundations of computer science, formal languages, and automata theory
Postdoctoral Researcher, Advisor: Prof. Jie Gao
- Developing topological frameworks for interpretable AI: TopInG achieves improved prediction accuracy and interpretability on molecular benchmarks
- Led research on non-Euclidean representation learning: Neuc-MDS and Johnson-Lindenstrauss extensions with provable theoretical guarantees
- Contributing to large-scale benchmarks for 3D/video generation: DL3DV-10K dataset
- Designing algorithms for multi-agent learning in social settings
Ph.D. in Computer Science, Advisor: Prof. Tamal K. Dey
- Dissertation: Decomposition and Stability of Multiparameter Persistence Modules
Ph.D. in Computer Science, Advisor: Prof. Tamal K. Dey
- Developed generalized persistence algorithms for multiparameter persistence modules
M.S. in Computer Science, Advisor: Prof. Xiaolei Huang
- Thesis: Machine Learning Techniques for Cervigram Image Analysis
- Research Focus: Medical image analysis, machine learning applications
B.Eng. in Software Engineering
Invited Talks
"TopInG: Topologically Interpretable Graph Learning via Persistent Rationale Filtration"
Conference on Topological Data Analysis: Recent Developments and Applications, University of Missouri, November 2025
"Understanding through Shape of Data: Topological Data Analysis for Interpretable AI"
Management Science and Information Systems Department Colloquium, Rutgers University, October 2024
"Exploring Representations Beyond Euclidean Geometry"
John Hopcroft Center Seminar, Shanghai Jiao Tong University, June 2024
"Generalized persistence algorithm for decomposing multi-parameter persistence modules"
Applied Algebraic Topology Network Seminar, July 2020
"Multiparameter Persistence and Its Applications"
Theory Seminar, Department of Computer Science, Rutgers University, November 2023
Teaching Experience
Instructor, Foundations of Computer Science and Introduction to Formal Languages and Automata, Fresno State, Fall 2026
Lecturer, Design and Analysis of Algorithms (graduate course, 45 students), 2025 Fall
Teaching Assistant, Data Structures and Algorithms (undergraduate, 200 students), 2023 Spring
Teaching Assistant, Computational Geometry (graduate, 30 students), 2020 Fall
Professional Service
Area Chair, TAG-DS Workshop, 2026
Reviewer, ICML, ICLR, NeurIPS, SoCG
Publications
Machine Learning (NeurIPS, ICML, CVPR, TMLR)
ICML 2025 C. Xin, F. Xu, X. Ding, J. Gao, J. Ding. "TopInG: Topologically Interpretable Learning via Persistent Rationale Filtration"
NeurIPS 2025 C. Deng, J. Gao, K. Lu, F. Luo, C. Xin†. "Johnson-Lindenstrauss Lemma Beyond Euclidean Geometry"
NeurIPS 2024 C. Deng, J. Gao, K. Lu, F. Luo, H. Sun, C. Xin†. "Neuc-MDS: Non-Euclidean Multidimensional Scaling Through Bilinear Forms"
ICML 2024 S. Haddadan, C. Xin, J. Gao. "Optimally Improving Cooperative Learning in a Social Setting"
CVPR 2024 L. Ling, ..., C. Xin, et al. "DL3DV-10K: A Large-Scale Scene Dataset for Deep Learning-Based 3D Vision"
TMLR 2024 S. Zhang, C. Xin, T. K. Dey. "Expressive Higher-Order Link Prediction through Hypergraph Symmetry Breaking"
ICML-W 2023 C. Xin, S. Mukherjee, S. N. Samaga, T. K. Dey. "GRIL: A 2-parameter Persistence Based Vectorization for Machine Learning"
Computational Geometry & Topology (SoCG, JACT)
SoCG 2026 S. Mukherjee, S. N. Samaga, C. Xin, S. Oudot, T. K. Dey. "D-GRIL: End-to-End Topological Learning with 2-parameter Persistence"
SoCG 2026 C. Deng, J. Gao, K. Lu, F. Luo, C. Xin. "Locality Sensitive Hashing in Hyperbolic Space"
JACT 2022 T. K. Dey, C. Xin†. "Generalized persistence algorithm for decomposing multiparameter persistence modules"
arXiv 2021 T. K. Dey, C. Xin†. "Rectangular Approximation and Stability of 2-parameter Persistence Modules"
SoCG 2018 T. K. Dey, C. Xin†. "Computing Bottleneck Distance for 2-D Interval Decomposable Modules"
Medical Imaging & Pattern Recognition
PR 2017 T. Xu, H. Zhang, C. Xin, et al. "Multi-feature based benchmark for cervical dysplasia classification evaluation"
MLMI 2015 T. Xu, C. Xin* et al. "A New Image Data Set and Benchmark for Cervical Dysplasia Classification Evaluation"
† authors alphabetically ordered · * co-first author
Industrial Experience
Big Data Group · Redwood City, CA
- Large-scale machine learning on Spark
- Graph learning on relational database, attributes evaluation and selection, dataset compression
AWS Infrastructure Group · Seattle, WA
- Data management system for network messages supporting receiving, parsing, storing, and retrieving
Skills
Python
PyTorch
Spark
Keras
Java
C
C++
MATLAB
R
References
Confidential recommendation letters available via Interfolio Dossier Delivery.
Dr. Tamal K. Dey
Professor, Computer Science, Purdue University
tamaldey@purdue.edu
Dr. Jie Gao
Professor, Computer Science, Rutgers University
jg1555@cs.rutgers.edu
Dr. Feng Luo
Professor, Mathematics, Rutgers University
fluo@math.rutgers.edu
v.2026-09-01