Private Homepage | https://www.uni-muenster.de/PRIA/personen/jiang.shtml |
Research Interests | Computer Vision Pattern Recognition |
Current Publications | • Tu 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 • Xiao J, Zhong Y, Jia Y, Wang Y, Jiang X, Wang S A novel deep ensemble model for imbalanced credit scoring in internet finance. International Journal of Forecasting Vol. 40 (1), 2024 online • Xiao J, Wen Z, Jiang X, Yu L, Wang S Three-stage research framework to assess and predict the financial risk of SMEs based on hybrid method. Decision Support Systems Vol. 177, 2024 online • Steinhorst, Phil; Duhme, Christof; Jiang, Xiaoyi; Vahrenhold, Jan Recognizing Patterns in Productive Failure. Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1, 2024 online • Chen J, Pi D, Jiang X, Xu Y, Chen Y, Wang X Denosieformer: A transformer based approach for single-channel EEG artifact removal. IEEE Transactions on Instrumentation and Measurement Vol. 73, 2024 online • Eminaga o, Saad F, Tian Z, Wolffgang U, Karakiewicz P, Ouellet V, Azzi F, Spieker T, Helmke B, Graefen M, Jiang X, Xing L, Witt J, Trudel D, Leyh-Bannurah SM Artificial intelligence unravels interpretable malignancy grades of prostate cancer on histology images. npj Imaging Vol. 2, 2024 online • Zhang Q, Jiang X Classification performance boosting for interpolation kernel machines by training set pruning using genetic algorithm. Prof. of ICPRAM, 2024 online • Hegselmann S, Shen Z, Gierse F, Agrawal M, Sontag D, Jiang X A data-centric approach to generate faithful and high quality patient summaries with large language models. Conference on Health, Inference, and Learning (CHIL), 2024 online |
Current Projects | • 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. • 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. online• CRC 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. online • Al-based Medical Image Analysis and AR-based Surgical Navigation for Craniomaxillofacial Surgery online • Ultra-layered perception with brain-inspired information processing for vehicle collision avoidance Autonomous vehicles, although in its early stage, have demonstrated huge potential in shaping future life styles to many of us. However, to be accepted by ordinary users, autonomous vehicles have a critical issue to solve – this is trustworthy collision detection. No one likes an autonomous car that is doomed to a collision accident once every few years or months. In the real world, collision does happen at every second - more than 1.3 million people are killed by road accidents every single year. The current approaches for vehicle collision detection such as vehicle to vehicle communication, radar, laser based Lidar and GPS are far from acceptable in terms of reliability, cost, energy consumption and size. For example, radar is too sensitive to metallic material, Lidar is too expensive and it does not work well on absorbing/reflective surfaces, GPS based methods are difficult in cities with high buildings, vehicle to vehicle communication cannot detect pedestrians or any objects unconnected, segmentation based vision methods are too computing power thirsty to be miniaturized, and normal vision sensors cannot cope with fog, rain and dim environment at night. To save people’s lives and to make autonomous vehicles safer to serve human society, a new type of trustworthy, robust, low cost, and low energy consumption vehicle collision detection and avoidance systems are badly needed.This consortium proposes an innovative solution with brain-inspired multiple layered and multiple modalities information processing for trustworthy vehicle collision detection. It takes the advantages of low cost spatial-temporal and parallel computing capacity of bio-inspired visual neural systems and multiple modalities data inputs in extracting potential collision cues at complex weather and lighting conditions. online | xjiang at uni-muenster dot de |
Phone | +49 251 83-33759 |
FAX | +49 251 83-33755 |
Room | 601 |
Secretary | Sekretariat Steinhoff Frau Gerlinde Steinhoff Telefon +49 251 83-38447 Fax +49 251 83-33755 Zimmer 602 |
Address | Prof. Dr. Xiaoyi Jiang Institut für Informatik Fachbereich Mathematik und Informatik der Universität Münster Einsteinstrasse 62 48149 Münster Deutschland |
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