Mathematik und Informatik

Prof. Dr. Xiaoyi Jiang, Institut für Informatik

Private Homepagehttps://www.uni-muenster.de/PRIA/personen/jiang.shtml
Research InterestsComputer Vision
Pattern Recognition
Current PublicationsTu P, Ye H, Shi H, Young J, Xie M, Zhao P, Zheng C, Jiang X, Chen X Phase-specific augmented reality guidance for microscopic cataract surgery using spatiotemporal fusion network. Information Fusion Vol. 113, 2025 online
Xiao J, Li Y, Tian Y, Jiang X, Wang Y, Wang S Optimising allocation of marketing resources among offline channel retailers: A bi-clustering-based model. Journal of Business Research Vol. 186, 2025 online
Zhang J, Jiang X Evolutionary training set pruning for boosting interpolation kernel machines. Pattern Recognition Applications and Methods, 2025 online
Chen J, Pi D, Jiang X, Gao F, Wang B, Chen Y EEGCiD: EEG condensation into diffusion model. IEEE Transactions on Automation Science and Engineering Vol. 22, 2025 online
Xu J, Gao J, Jiang S, Wang C, Smedby Ö, Wu Y, Jiang X, Chen X Automatic segmentation of bone graft in maxillary sinus via distance constrained network guided by prior anatomical knowledge. IEEE Journal of Biomedical and Health Informatics, 2025 online
Lixiang Xu, Jiawang Peng, Xiaoyi Jiang, Enhong Chen, Bin Luo Graph neural network based on graph kernel: A survey. Pattern Recognition Vol. 161, 2025 online
Frederik Elischberger, Xiaoyi Jiang Deep Learning meta architecture to detect spatially coherent coarse grain regions in ultrasonic data. NDT and E International Vol. 153, 2025 online
Wolff, Matthias; Eilers, Florian; Jiang, Xiaoyi CVKAN: Complex-valued Kolmogorov-Arnold Networks. Proc. of IJCNN, 2025 online
Jin Xiao, Sihan Li, Yuhang Tian, Jing Huang, Xiaoyi Jiang, Shouyang Wang Example dependent cost sensitive learning based selective deep ensemble model for customer credit scoring. Scientific Reports Vol. 15, 2025 online
Current ProjectsCRC 1450 - Z01: Interactive and computational analysis of large multiscale imaging data

The multiscale imaging strategy central to this initiative imposes novel data analysis challenges. The high complexity of the acquired data results from their nature of being volumetric, time-varying, large, multiscale, and forming cohorts). Meeting these challenges requires basic research in the fields of image analysis, machine learning, and visualization. Machine learning will be used to uncover inherent relationships between patterns at multiple scales. An interactive visual approach supports the user-centric analysis of detected features.

The deliverable of this project will be generally applicable, effective, and efficient methods supporting the overall goal of multiscale data analysis.

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SPP 2363 - Subproject: Elucidating Fingerprints – Towards a Holistic Explanatory Toolbox for Molecular Machine Learning

The central point of this proposal is the development of out-of-the-box for interpretable and Explainable Molecular Machine Learning on a structural level. Within this project broadly utilized molecular representations will be developed, adapted and used to train highly robust but accurate models (e.g. Gradient Boost algorithms). Starting from these models an open-source software pipeline will be employed to map feature importance, influence, interdependencies, as well as model confidences back to the molecular structure giving trained chemists a plain handle for molecular and reaction design. An important part of this work will involve the development of visualization based on analytic results that provide a high degree of accuracy on the one hand and are easy to understand for any scientist working in the field of molecular science on the other hand. Those tools shall be usable to investigate and improve underlaying datasets as well as for molecular design. In addition to the coloration and visualization of individual molecules, methods of statistical evaluation regarding the general influence of functional groups should be developed, so that rules for further reaction design can be derived. Finally, these rules should be used in the laboratory to validate the explanatory methods developed within the course of this proposal. By these objectives the proposal aims on fulfilling the following of the PPs general goals: “Application of state-of-the-art ML algorithms – Explainable AI”, “Development of (domain specific) molecular representations – Generally improved molecular representations” and “Prediction, understanding and interpretation of molecular properties – Improvement of current applications”. Within this scope a high focus lies on the interpretation and explanation models for quantitative yield prediction to find handles for a systematic improvement within this underdeveloped area of MML which also has defined as a major topic of this PP.

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Interdisziplinäres Lehrprogramm zu maschinellem Lernen und künstlicher Intelligenz

The aim of the project is to establish and test a graduated university-wide teaching programme on machine learning (ML) and artificial intelligence (AI). AI is taught as an interdisciplinary cross-sectional topic that has diverse application possibilities in basic research as well as in economy and society, but consequently also raises social, ethical and ecological challenges.

The modular teaching program is designed to enable students to build up their AI knowledge, apply it independently and transfer it directly to various application areas. The courses take place in a broad interdisciplinary context, i.e., students from different departments take the courses together and work together on projects.

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E-Mailxjiang at uni-muenster dot de
Phone+49 251 83-33759
Room601
Secretary   Sekretariat Steinhoff
Frau Gerlinde Steinhoff
Telefon +49 251 83-38447
Zimmer 602
AddressProf. Dr. Xiaoyi Jiang
Institut für Informatik
Fachbereich Mathematik und Informatik der Universität Münster
Einsteinstrasse 62
48149 Münster
Deutschland
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