Justin Sybrandt is a Ph.D. Candidate at Clemson University studying large-scale text mining applications. A majority of his work focuses on hypothesis generation in medicine. Justin works in the Algorithms and Computational Science Lab overseen by his advisor Ilya Safro.
Ph.D. Candidate in Machine Learning
BS in Computer Science, 2016
Grove City College
I have the chance to present my work at the Google Ph.D. Intern Research Conference (PIRC). This poster represents all of the work we have added to the Moliere project since our original paper last year.
Today in a class, we were asked to write an iterative solver for numerical equations. Now, many students in the class did not have an optimization background, so for the benefit of everyone, I want to share a simple overview of this exercise and how to go about solving it.
The problem was stated as follows:
$$ M(a) = 2\times a + 14$$ $$ G(b) = b - 2 $$
And our goal was to find some solution $x$ such that $M(x) = G(x)$. Additionally, we were supposed to do so iteratively, so just solving the system of equations was out of the question. This is because our next exercise would have a different $M$ and $G$, so our code should be able to support whatever.
For the sake of generalization, my solution here will assume only the $M$ and $G$ are continuous, but I will not assume we know their derivatives. Additionally, I will be writing my code in python, simply because I find that it is easier for anybody to understand. Knowledge of python, hopefully, won’t be necessary. But first, lets go over some aspects of the problem…
We discover potential connections within existing scientific literature. Currently, we are preparing MOLIERE for large-scale public usage.
We classify bridge health using Support Vector Regression and other Machine Learning Techniques. In partnership with Clemson Civil Engineers.
An introductory video series for people absolutly new to programming. Learn the basics of programming!
Distsync is a parallel storage system syncronization utility which leverages cluster computing capabilities to unify large out-of-sync distributed file systems.