The Department of Industrial Engineering is honored to welcome Dr. Yongjia Song as an assistant professor of industrial engineering this Fall 2018.
Dr. Song obtained his Ph.D. in Industrial Engineering from University of Wisconsin-Madison in 2013. He completed his graduate studies at the University of Wisconsin-Madison in 2012, where he earned his MS in both Operations Research and Computer Sciences. Prior to his work at Wisconsin-Madison, he earned his Bachelor of Science degree in Computational Mathematics from Peking University, Beijing, China in 2009.
Before joining Clemson, Dr. Song was an assistant professor in the Department of Statistical Sciences and Operations Research at Virginia Commonwealth University (VCU).
We encourage you in welcoming our newest faculty member to Clemson University, and in getting to know Dr. Song with our quick Q&A introduction.
What types of courses do you teach?
Dr. Song: I teach courses related to optimization and its applications in all levels: undergraduate, master, and PhD. I particularly enjoyed teaching introductory operations research courses for junior/senior undergraduate students and first-year graduate students, where I felt like I am the person who opened the door for these students to see a brand new scientific and engineering area that is so close to their daily life, but they have not been aware of or even have not heard of. I also enjoyed teaching advanced graduate level courses in optimization methodology such as integer programming, network optimization, as well as optimization under uncertainty, all of which are intimately related to my research.
What are your research interests?
Dr. Song: My research interests include optimization under uncertainty (stochastic and robust optimization), integer programming (linear and nonlinear), and applications of optimization in various engineering problems such as transportation, networks, energy, health care, etc. I focus on developing solution methodology for optimization models, especially those that are challenging to solve due to their (large) scale, and/or due to some underlying uncertainty that is involved in the model. These models are usually motivated by the need of integrated optimization among multiple interconnected decisions, and/or the need of anticipating the impact of the current decision to the future so that the decision is optimal in the long run. These situations arise from modern engineering applications such as integrated vehicle routing and service scheduling, multistage power generation planning, reliable network designs, etc.
What brought you to this field?
Dr. Song: I have been into the Three Kingdoms period in the history of China (https://en.wikipedia.org/wiki/Three_Kingdoms) since childhood. I have been particularly interested in the ancient wisdom that leads to strategies that people played against each other on and off the battlefield. Coincidently, military operation is exactly the origin of modern operations research. Interestingly, many tools and ideas that we learn today can be used to interpret and understand those ancient wisdom. Given my background in mathematics and quantitative analysis, I am passionate about the field of operations research, which I think is the “modern wisdom” that is highly desired in all kinds of engineering applications.
Dr. Song: I enjoy almost all kinds of sports: running, biking, tennis, basketball, etc. I also enjoy listening to music (even when I am working) and watching sports games. Go (Wisconsin) Badgers, Go (VCU) Rams, and of course, Go Tigers!
What are you most looking forward to about Clemson University?