Our Computing Expertise
App Research
Apps are ubiquitous and tightly integrated into our daily lives. They help facilitate decision making and communication in our work and play. Apps often serve as the front-end to other devices and systems that gather data within their respective environments and help us live healthier lives, or better understand and respond to complex problems, such as the decline of honey bee populations.
Data Science / Machine Learning
In recent years, many applications have become more helpful, personal, and informative. At the core of many of these applications are the techniques of Data Science. The techniques are used to learn from the data that is collected and archived by the host organizations. The new knowledge can be applied to many areas.
A.I. / Machine Learning
A few hundred years ago only a privileged few had access to education. The general people had to depend on these chosen few to guide them to the right path. Then, we democratized education, allowing everyone to access literacy and enlightenment. This democratization helped the human race to prosper exponentially in every field of knowledge. Andrew Ng, a co-founder of Google Brain, said that, right now, Artificial Intelligence and Machine Learning (AI & ML) are almost what education was two hundred years ago. A crystal ball that only a chosen few understand and take advantage of, and everyone else depends on this chosen few to help the rest make an informed decision. However, what if we democratize AI? What if we allow AI & ML to be pervasive, not just limited to the big tech, but also helping the small businesses benefit from its enormous capabilities?
Bioinformatics & HPC
The GVSU Applied Computing Institute's Bioinformatics and High-Performance Computing team is dedicated to advancing the field of computational biology through cutting-edge research and innovation. Our mission is to develop novel computational tools and techniques to analyze large-scale biological data sets and solve critical challenges in healthcare, agriculture, and environmental sciences.
Currently, Dr. DeBruine's lab is developing fast Non-negative Matrix Factorization algorithms for applications in bioinformatics and beyond. Special interests include single-cell experiment dimensional reduction and integration, hybrid NNLS/graph-based learning, genome/phenome meta-association, and novel approaches to model regularization.