Yan Zheng

Principal Research Scientist, Fundamental AI

Research Areas: Representation Learning, Time Series Analysis, Recommendation, Visualization, Large Language Model with Transaction Data

Biography

Dr. Yan Zheng joined Visa Research in 2017 as a Staff Research Scientist, advancing to Senior Staff Research Scientist in 2019 and Principal Research Scientist in 2022. She earned her Ph.D. in Computer Science from the University of Utah in 2017.

Dr. Zheng's research spans Representation Learning, Time Series Analysis, Recommendation Systems, Visualization, and Model Explanations. She has authored numerous published papers and holds several patents, with notable contributions to conferences such as ICLR, ICDM, SDM, KDD, PacificVis and SIGIR.

Her paper “Denoising Self-Attentive Sequential Recommendation” won Best Paper at Recommender Systems 2023, while “How Does Attention Work in Vision Transformers? A Visual Analytics Attempt” received an Honorable Mention at PacificVis 2023. Additionally, her paper “EmbeddingTree: Hierarchical Exploration of Entity Features in Embedding” won the Best Visualization Notes Award at PacificVis 2023.

Dr. Zheng has also made significant practical contributions, notably in the development of Visa Deep Authorization (VDA), which enhances fraud detection and customer benefits. VDA's success is projected to generate approximately $100 million revenue over the next five years and has been featured in various publications.

Conferences

Zheng, Y., Ou, Y., Lex, A., & Phillips, J. M. (2019). Visualization of Big Spatial Data using Coresets for Kernel Density Estimates. Visual Data Science.
Zheng, Y., & Phillips, J. M. (2017). Coresets for Kernel Regression. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
Phillips, J. M., & Zheng, Y. (2015). Subsampling in Smoothed Range Spaces. Algorithmic Learning Theory.
Zheng, Y., & Phillips, J. M. (2015). L∞ Error and Bandwidth Selection for Kernel Density Estimates of Large Data. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
Phillips, J., Wang, B., & Zheng, Y. (2015). Geometric Inference on Kernel Density Estimates. International Symposium on Computational Geometry.
Zheng, Y., Jestes, J., Phillips, J., & Li, F. (2013). Quality and Efficiency in Kernel Density Estimates for Large Data. ACM SIGMOD International Conference on Management of Data.
Yang, J., Shi, G., Zheng, Y., & Wang, Q. (2009). The research on offline palmprint identification. Computer Science and Information Engineering.
Zheng, Y., Shi, G., Wang, Q., & Zhang, L. (2008). Location of Special Areas on Palmprint. Congress on Image and Signal Processing.
Zheng, Y., Shi, G., Zhang, L., Wang, Q., & Zhao, Y. (2007). Research on Offline Palmprint Image Enhancement. IEEE International Conference on Image Processing.
Zheng, Y., Shi, G., Wang, Q., & Zhang, L. (2007). Palmprint Image Quality Measures in Minutiae-based Recognition. International Workshop on Systems, Signals and Image Processing. EURASIP Conference Focused on Speech and Image Processing, Multimedia Communications and Services.
Zheng, Y., Liu, Y., Shi, G., Li, J., & Wang, Q. (2007). Segmentation of Offline Palmprint. IEEE International Conference on Signal-Image Technologies and Internet-Based System.
Liu, Y., Zheng, Y., Shi, G., & Wang, Q. (2007). Research on Skeletonization of Palmprint Image. IEEE International Conference on Signal-Image Technologies and Internet-Based System.

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