I am interested in machine learning, network biology that is applied to clinical outcome prediction. The current machine learning field is dominated by gradient descent-based methods; I am more interested in developing the branch of techniques with matrix manipulation to complement the strength of gradient descent. Most of my methods involve a strong component of matrix decomposition, multiplication, or transformation. I consider the gradient descent-derived approach in machine learning today and the matrix-derived approach are the natural descendants of two branches of modern math: calculus and linear algebra, which together will deliver the most powerful and accurate prediction algorithms.
I enjoy working with kids and found it a rewarding process to build long-term relationship with a smart student and a sweet family. I have coached high school students who placed first in the Southeast Michigan Science Fair, or entered Intel STS finalist.
I wrote the 1st place solutions for 10 DREAM challenges, spanning wide topics of machine learning: HERE. I encourage U-mich students to take advantage of the courses I am teaching to learn them.