I am a fourth-year PhD candidate at the Department of Computational Medicine and Bioinformatics at the University of Michigan. I am also a BIDS-TP trainee.
I received my bachelors in Data Science at the University of Michigan, College of Engineering CSE, and Electrical Computer Engineering at Shanghai JiaoTong University.
I joined the SOCR Lab in 2019 and am actively engaged in the SOCR TCIU project and Spacekime Analytics.

My dissertation research focuses on addressing the need for developing innovative and reproducible scientific methods to advance the field of biomedical informatics by capitalizing on the idea of complex time (kime). I am also interested in (approximate) symmetries that makes neural network sample efficient.

Relevant Links

• Yueyang's GitHub site.
• TCIU GitHub site.
• ORCID site.
• Personal Website and backup site.
• Google Scholar Profile.
• Email: petersyy at umich.edu.

Awards

• 2022-2025: Yueyang is appointed as a Michigan Biomedical Informatics and Data Science Training Program (BIDS-TP) Trainee
• May 2024: Yueyang Shen and Yupeng Zhang are awarded the 2024 annual Theory and Methods Track - Best Student Paper Award by American Statistical Association (AmStat) - Statistical Methods in Imaging (SMI) Conference. Statistical foundations of invariance and equivariance in deep artificial neural network learning was recognized as being of remarkable quality by the AmStat/SMI review committee.

Publications

• Wang, Y, Shen Y, Deng, D, Dinov, ID. (2022) Determinism, Well-posedness, and Applications of the Ultrahyperbolic Wave Equation in Spacekime, Journal of Partial Differential Equations in Applied Mathematics, DOI: 10.1016/j.padiff.2022.100280.
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• Zhang, R, Zhang, Y, Liu, Y, Guo, Y, Shen, Y, Deng, D, Qiu, Y, Dinov, ID. (2022) Kimesurface Representation and Tensor Linear Modeling of Longitudinal Data, Neural Computing and Applications Journal, DOI: 10.1007/s00521-021-06789-8.

Presentations

• (May 29-31, 2024) AmStat SMI 2024 Presentation (Best Student Award) Statistical foundations of invariance and equivariance in deep artificial neural network learning
• American Physical Society Eastern Great Lakes Session (APS/EGLS). Invariance in Deep Network Learning: Mathematical Representation, Probabilistic Symmetry, Variable Exchangeability, and Sufficient Statistics (October 2023).Slides.
  
• American Physical Society April Meeting. Numerical Methods and Analysis for Computing Forward and Inverse Laplace Transform For discrete and continuous signals (April 2023).Slides.
  
• American Physical Society Eastern Great Lakes Session (APS/EGLS). Laplace Transform and Inverse Laplace Transforms-Numerical Methods, Groups, and Clifford Algebra (October 2022).Slides.
  
• American Physical Society Eastern Great Lakes Session (APS/EGLS). Wheeler-Dewitt equation in spacekime (November 2021).Slides.
  
• Advanced Neural Network Computation (ACNN) Ultrahyperbolic Equations: WellPosedness, Visualization and Applications (September 2020).Slides.
  

Preprints

• Zhang, Y, Shen, Y, Rongqian, Z, Yuyao, L, Yunjie G, Daxuan D, Ivo D. (2023) Numerical methods for computing the discrete and continuous Laplace transforms, arxiv, DOI: 2304.13204, in submission.

Teaching and Services

• Graduate Student Instructor of BIOINF 580: Intro to Signal Process and ML in Biomedical Sciences, UM, Fall 2023
• Reviewer of Medical Physics, American Association of Physicists in Medicine, 2022-present
• Secretary and Event Chair, UM-SJTU Joint Institute Alumni Association, Ann Arbor, MI, 2022-present
• Grader for EECS476 Data Mining, UM, Winter 2021
• Teaching Assistant for VV285 Honors Mathematics II, SJTU, Shanghai, Summer 2019

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