Office: East Hall 1836
Email: canc at umich dot edu
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.
- 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]
- Multilinear Control Systems Theory, Can Chen, Amit Surana, Anthony Bloch, Indika Rajapakse (preprint). [Arxiv]
- Data-Driven Model Reduction for Multilinear Control Systems via Tensor Trains, Can Chen, Amit Surana, Anthony Bloch, Indika Rajapakse (preprint). [Arxiv]
- Tensor Entropy for Uniform Hypergraphs, Can Chen, Indika Rajapakse, IEEE Transactions on Network Science and Engineering (early access). [Article] [Arxiv]
- 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]
- Controllability of Uniform Hypergraphs, Can Chen, Anthony Bloch, Indika Rajapakse (preprint). [Arxiv]
- DMD Based Control of Multiway Dynamical Systems, SIAM conference on Applications on Dynamical Systems, Snowbird, Utah, May 22, 2019. [Drive]
- Multilinear Time Invariant System Theory, SIAM conference on Control and its Application, Chengdu, China, June 21, 2019. [Drive]
- Asymptotically Efficient Adaptive Allocation Rules, Can Chen, Bo Lu, Jiaxin Liang, Henry Oskar Singer, EECS 558 Stochastic Control, Fall 2017. [Drive]
- 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]
- Manifold Learning in Differentiating Cancer Cells, Can Chen, Yuting Liu, Peter Paquet, James Connolly, EECS 545 Machine Learning, Fall 2018. [Drive]
- 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]
- Math 105: Data, Functions and Graphs (Fall 2016, Winter 2017)
- Math 115: Calculus I (Fall 2017, Winter 2018, Fall 2018, Fall 2019)
- Math 216: Introduction to Differential Equations (Winter 2020)