Aaron W. Chen, PhD
About Aaron W. Chen, PhD
Organizations everywhere are at an extremely interesting crossroads between data usage and development and technological innovation! Many places are trying to switch their data infrastructure off of legacy systems and incorporate open source stack friendly data warehouses, data lakes, and processes and tools. However, it can be difficult to find people who can combine subject matter expertise, industry experience, and institutional knowledge with technology stacks using data science, machine learning (ML), and artificial intelligence (AI). Data engineers/scientists, research engineers/scientists, and machine learning engineers at this junction can be powerful force multipliers for organizations. We can integrate with matrix teams combining subject matter experts, business stakeholders, and technology infrastructure engineers to accelerate impactful decision making. As such, we tend to learn a lot from various fields and reapply best practices to solve problems. As a data scientist, I seek to pragmatically apply technological advances to complex, real world problems so that I can deliver practical solutions. For example, if someone wanted a way to classify text reports, a natural language processing (NLP) classifier tool might be able to use an expensive deep learning neural network, but maybe it would work out fine with a logistic regressor that is both simpler to deploy and understand. I am also interested in implementing responsible and ethical AI practices with robust and understandable MLOps. This stems from my Chemical Engineering (with biomedical application) experience where, especially during my Ph D, reliable and repeatable science were key to getting things done and learning new things. Why data science? Since I was a kid, I've always been curious about how things happen in the world and learning more. AKA a geek! I indulge my analytical curiosity to look at things like photography, cooking, biotechnology, materials science, and computers and incorporate information from disparate fields to advance the efforts of myself and others. Like describing cooking as chemical processes or utilizing machine learning, computer vision, and computer analysis in colloid and interface science to aid in workflow and research publication.