报告题目：Some Optimization Challenges and Opportunities in Financial Engineering
Optimization models and methodologies have been widely and successfully used in financial engineering. In his seminal
work in 1950s, Markowitcz introduced the mean-variance model, which opened an era of modern portfolio theory. Though there is a
rich literature on the study of the mean-variance model and its variants, there exists a long-standing issue that the solution to the
mean-variance model is very sparse and leads to the so-called idiosyncratic risk. Moreover, as a consequence of the recent financial crisis,
numerous optimization models have been proposed to estimate and control the risk in financial market and these problems are usually
non-convex with mixed integer constraints.
In this talk, we present some recent advance in our research in portfolio selection, asset deleveraging and systemic risk estimate.
Jiming Peng received his PhD degree in operations research in 2001 from Delft University of Technology, the Netherlands. He is at present an assistant professor in the department of industrial engineering, University of Houston. Previously he worked in McMaster University in Canada, and University of Illinois at Urbana-Champaign.
His research interest covers several branches in optimization, with a recent focus on the development of effective algorithms for large-scale non-convex and mixed integer programming, with applications in financial engineering and big data. He has published a research monograph and about sixty peer-reviewed papers. He and his student have received numerous awards for their research contribution in optimization and financial engineering including Stieljes prize in Holland (2001), finalist of Tucker prize (2003), primer research excellence award from Ontario (2003), first runner-up for the annual Morgan Stanley Prize for Excellence in Financial Market (2012), best research paper award in financial service, Informs (2013).