Wesley is a faculty member at the Global Innovation Exchange and a Program Operations Specialist at the University of Washington. He is a Data Science Advisor for the Pfaendtner Research Group and partner at MFG Analytic. At MFG, he works with manufacturing clients in the U.S. and Europe to optimize their production processes using cloud-based tools. He received his Ph.D. in Chemical Engineering Data Science from the University of Washington, where his research was funded through a National Science Foundation Traineeship in the Data Sciences. He received his B.S. in Chemical Engineering from the University of Texas at Austin.
Wesley has done extensive work in surrogate modeling and machine learning. In his doctoral research, he performed computational design of liquid materials using a combination of machine learning models and all-atom molecular dynamics simulations. An outcome of this research was open-source python software that generated an adaptive learning and design framework for the creation of liquid materials. While at the UW, he worked in cross-disciplinary teams to create machine learning models for atomic force microscopy image analysis, climate sentiment natural language processing tasks, and quantitative structure-property relationship models of chemical compounds. After graduating from the UW, Wesley joined a Houston-based startup, MFG Analytic, to deliver software solutions based heavily on his expertise in chemical engineering and data analytics. In the past two years, he has traveled to clients based in England, Luxembourg, the Netherlands, and across the continental U.S. to codify and improve their processes.
In his consulting practice, Wesley saw a need for a paradigm shift in the way consulting is traditionally done. Rather than simply hiring a consulting team to deliver a software product, organizations could streamline their workflows and increase profit margins by training their in-house employees to better understand and utilize the data the organization is generating. In this course series, Wesley distills what he has learned in the past seven years as a chemical engineering data scientist to the most salient, practical information applicable to the working professional.
The course will cover relevant topics in the data science toolkit through the lens of surrogate modeling – creating models of people, processes, and equipment that are heavily featured in the world of engineering. The curricula will cover exploratory data analysis, supervised and unsupervised machine learning models, neural networks, and data dashboarding. Sticking true to the consulting model, an important and differentiating feature of these courses, is the opportunity for students to bring in their own datasets and gain accreditation creating algorithmic solutions centered around this data. The result is that students will come out of the course series with a suite of tools and a working example of a solution they have created, to propel them forward in their data-centric thinking and become data advocates and leaders within their organization.