Can Chen

Office: East Hall 1836
Email: canc at umich dot edu

About Me

I am a Ph.D. candidate in the Applied & Interdisciplinary Mathematics program and a Master's student in the Electrical & Computer Engineering program, at University of Michigan. I am also a member of (Cell & Genome) Reprograming Lab, at University of Michigan, Medical School. 

I am interested in control theory, numerical analysis, tensor algebra, network theory, data analysis and biological modeling. Currently, I am working with Prof. Anthony Bloch, Prof. Indika Rajapakse, and Dr. Amit Surana, on data-guided control of multiway dynamical systems. 

Here is my CV


I received my B.S., Summa Cum Laude, in Mathematics with minor in Statistics, at the University of California, Irvine in 2016. 

Thesis: Two Branched Cell Lineages for Proliferative Control, supervised by Prof. John Lowengrub


  1. Multilinear Time Invariant System Theory, Can Chen, Amit Surana, Anthony Bloch, Indika Rajapakse, Proceedings of SIAM Conference on Control and its Applications (2019), pp. 118-125. [Article][Arxiv]
  2. Multilinear Control Systems Theory, Can Chen, Amit Surana, Anthony Bloch, Indika Rajapakse (preprint). [Arxiv]
  3. Data-Driven Model Reduction for Multilinear Control Systems via Tensor Trains, Can Chen, Amit Surana, Anthony Bloch, Indika Rajapakse (preprint)[Arxiv]
  4. Tensor Entropy for Uniform Hypergraphs, Can Chen, Indika Rajapakse, IEEE Transactions on Network Science and Engineering (early access). [Article] [Arxiv]
  5. Functional Organization of the Maternal and Paternal Human 4D Nucleome, Stephen Lindsly, Wenlong Jia, Haiming Chen, Sijia Liu, Scott Ronquist, Can Chen, et al. (preprint). [BioRxiv]
  6. Controllability of Uniform Hypergraphs, Can Chen, Anthony Bloch, Indika Rajapakse (preprint). [Arxiv]


  1. DMD Based Control of Multiway Dynamical Systems, SIAM conference on Applications on Dynamical Systems, Snowbird, Utah, May 22, 2019. [Drive]
  2. Multilinear Time Invariant System Theory, SIAM conference on Control and its Application, Chengdu, China, June 21, 2019. [Drive]

Course Projects 

  1. Asymptotically Efficient Adaptive Allocation Rules, Can Chen, Bo Lu, Jiaxin Liang, Henry Oskar Singer, EECS 558 Stochastic Control, Fall 2017. [Drive]
  2. A Review of Control System Analysis and Design Via the ''Second Method" of Lyapunov: I - Continuous Time Systems, Can Chen, EECS 562 Nonlinear Control, Winter 2018. [Drive]
  3. Manifold Learning in Differentiating Cancer Cells, Can Chen, Yuting Liu, Peter Paquet, James Connolly, EECS 545 Machine Learning, Fall 2018. [Drive]
  4. A Survey of Reinforcement Learning Methods in Stock Trading Games, Can Chen, Xinyue Yang, Zeyuan Zhu, Royce Hwang, Peter Paquet, EECS 598 Special Topic: Deep Learning, Winter 2019. [Drive]


  1. Math 105: Data, Functions and Graphs (Fall 2016, Winter 2017)
  2. Math 115: Calculus I (Fall 2017, Winter 2018, Fall 2018, Fall 2019)
  3. Math 216: Introduction to Differential Equations (Winter 2020)