|
Ye Chen,
PhD Assistant
Professor Department
of Mathematics and Statistics Northern
Arizona University Tel.:
928-523-2758 Add.:
171 Adel Mathematics Email:
Ye.Chen@nau.edu |
|
Teaching
|
Courses |
Semester |
|
MAT316 Introduction to Linear Algebra (2 sessions) |
Spring 2018 |
|
MAT316 Introduction to Linear
Algebra MAT362 Numerical Analysis MAT485 Undergraduate Research |
Fall 2018 |
|
MAT239 Differential Equations MAT431 Introduction to Real Analysis |
Spring 2019 |
|
MAT226 Discrete Mathematics (2 sessions) MAT431 Introduction to Real Analysis |
Fall 2019 |
|
MAT362 Numerical Analysis MAT685 Graduate Research |
Spring 2020 |
|
MAT226 Discrete Mathematics (2 sessions) MAT239 Differential Equations |
Fall 2020 |
|
MAT362 Numerical Analysis MAT690 Advanced Topics in Mathematics - Network
Science |
Spring 2021 |
|
MAT226 Discrete Mathematics (2 sessions) MAT362 Numerical Analysis |
Fall 2021 |
|
MAT226 Discrete Mathematics MAT362 Numerical Analysis MAT485 Undergraduate Research STA 685 Graduate Research |
Spring 2022 |
|
MAT362 Numerical Analysis (2 sessions) MAT431 Introduction to Real Analysis MAT485 Undergraduate Research STA 685 Graduate Research |
Fall 2022 |
|
MAT362 Numerical Analysis MAT690 Advanced Topics in Mathematics - Mathematical
Foundation of Deep Learning MAT685 Graduate Research |
Spring 2023 |
|
MAT226 Discrete Mathematics MAT316 Introduction to Linear Algebra MAT362 Numerical Analysis MAT485 Undergraduate Research MAT685 Graduate Research |
Fall 2023 |
|
MAT362 Numerical Analysis MAT411 Introduction to Abstract Algebra MAT485 Undergraduate Research |
Spring 2024 |
I earned my PhD in Mathematics from West Virginia
University in 2014, then spent three years at the National Institutes of Health
(NIH) as a postdoc before joining Northern Arizona University (NAU). My
research interests span statistical inference for dynamical systems, graph
theory, network science, and the analysis and modeling of bioinformatics and
systems biology data.
My research experience includes:
·
Bayesian
Approach to inverse problems, and its application in epidemiology forecast with
a focus on diseases such as COVID-19 and flu.
·
Raw sequencing
data processing, statistical analysis, pathway analysis, network modeling for
various sequencing methods, including DNA, mRNA, miRNA, scRNA,
16s rRNA, and proteomics.
·
Hybrid
dynamical systems modeling for biomolecular networks.
·
Land
carbon cycle modeling.
·
Graph
coloring, graph connectivity.
Publication list: Google Scholar