Joseph Wang is a theoretical physicist with 20 years of proven research experience in modeling collective phenomena and exploration numerical simulation to make predictions in complex systems. Identifying correlations between different degrees of freedoms, connecting those to the understanding of challenging problems and making predictions are his strengths and passion. Most recently, he focused on forecasting rock properties in the oil-gas industry. Previously, he held a postdoctoral research position at Los Alamos National Laboratory affiliated with Center for Nonlinear Studies and the T4 Group of Material Science and Statistical Physics. He also held a record on publishing three well recognized papers in Nature, Science, and Nature Communications on quantum computation within three years before joining Los Alamos. His curiosity to understand nature phenomena coincides with the pursuit of pattern recognition in the data. He has realized that physics is all about a way of thinking, and less about a specific domain of knowledge. Anything learned from one area of physics can be connected to a different area. The most valuable lessons from physics, regardless what tools needed, are how to break a complex problem into smaller tangible problems, and how to gather information about a problem. Tools, such as programming languages, can always be learned on the fly. By the NYC data science training programs, Joseph wants to refine and deepen his hands-on experience in data science. Joseph has shown flexible and deep research experience in physical and mathematical science.
