Speaker: Pro.Xing Qiu（University of Rochester）

Title: Statistical Methods for Differential Equation Modeling

Location: Room 4318, Building No.4, Wushan Campus

Curricula Arrangement:

Time1: Sun, Jun.23, 2019, PM:3:00-6:00

Lecture1: Introduction to functional data analysis;

Time2: Mon, Jun.24, 2019, AM:9:00-12:00, PM:3:00-6:00

Lecture2: Introduction to ordinary differential equations;

Time3: Tues, Jun.25, 2019, AM:9:00-12:00, PM:3:00-6:00

Lecture2: Introduction to ordinary differential equations;

Time4: Wed, Jun.26, 2019, AM:9:00-12:00, PM:3:00-6:00

Lecture3: Statistical methods for inverse problem;

Time5: Thus, Jun.27, 2019, AM:9:00-12:00, PM:3:00-6:00

Lecture4: Sparcity, stability, and controllability;

Time6: Fri, Jun.28, 2019, AM:9:00-12:00, PM:3:00-6:00

Lecture5: Partial differential equations and other future research opportunities.

Abstract:

Prerequisites: 

Calculus I, II (MTH 161, 162 or equivalent); Ordinary Differential Equations (MTH 163 or equivalent); Probability Theory (BST 401 or equivalent); Statistical Inference (BST 411 or equivalent).

Course Description: 

This course intends to introduce students with basic concepts of a “system” and differential equations theory with applications to modeling biological systems and processes. This course will present statistical and mathematical techniques that are required to reconstruct biological networks, analyzing medical image data, etc.

Course Aims and Objectives: 

We will train students in functional data analysis, parameter estimation and model selection, cluster analysis, and numerical methods for differential equations, so that they have a systems thinking in biomedical research with a solid systems science approach. The students are also expected to master statistical, mathematical, and computational skills that are necessary for their future research in computational biology.

Course Policies and Expectations: 

Students are expected to attend every class and finish homework and/or projects in a timely fashion. Students may bring laptops to class to assist learning