Professor Kay Giesecke
Kay Giesecke is an Associate Professor of Management Science & Engineering and the Paul Pigott Faculty Scholar in the School of Engineering. He is on the faculty of Stanford's Financial Mathematics Program.
Kay's research addresses the quantification and management of financial risks, especially the risk of default (credit risk). He is particularly interested in the stochastic modeling, valuation and hedging of credit risks, the development of statistical tools to estimate and predict these risks, and the methods for solving the significant computational problems that arise in this context. His research contributions enable more effective hedging of credit risks, better risk management at financial institutions, and more accurate measurement of systemic risk in financial markets. They have won the 2011 Fama/DFA Prize for the Best Asset Pricing Paper in the Journal of Financial Economics and the 2003 Gauss Prize of the Society for Actuarial and Financial Mathematics of Germany. Kay's research group CreditLab has been funded by grants from JP Morgan, Morgan Stanley, Mizuho, Moody's, Credit Suisse and American Express.
At Stanford, Kay teaches three courses, MS&E 242H Investment Science Honors, MS&E 347 Credit Risk: Modeling and Management, and MS&E 444 Investment Practice. He is the recipient of the 2007 Stanford Graduate Teaching Award.
Kay has served as a consultant to banks, investment and risk management firms, governmental agencies, and supranational organizations in the area of risk management and derivatives valuation and hedging. He holds a U.S. patent on a method for the quantification of credit risk in the presence of incomplete information.
Kay serves on the editorial boards of Operations Research, the SIAM Journal on Financial Mathematics, the Journal of Banking and Finance, Operations Research Letters, and IIE Transactions.
For a detailed profile of Professor Kay Giesecke, please go to: https://engineering.stanford.edu/profile/giesecke
Professor Peter Glynn
Peter W. Glynn is the current Chair of the Department of Management Science and Engineering at Stanford University. He received his Ph.D in Operations Research from Stanford University in 1982. He then joined the faculty of the University of Wisconsin at Madison, where he held a joint appointment between the Industrial Engineering Department and Mathematics Research Center, and courtesy appointments in Computer Science and Mathematics. In 1987, he returned to Stanford, where he joined the Department of Operations Research. He is now the Thomas Ford Professor of Engineering in the Department of Management Science and Engineering, and also holds a courtesy appointment in the Department of Electrical Engineering. From 1999 to 2005, he served as Deputy Chair of the Department of Management Science and Engineering, and was Director of Stanford's Institute for Computational and Mathematical Engineering from 2006 until 2013. He is a Fellow of INFORMS and a Fellow of the Institute of Mathematical Statistics, has been co-winner of Best Publication Awards from the INFORMS Simulation Society in 1993 and 2008, was a co-winner of the Best (Biannual) Publication Award from the INFORMS Applied Probability Society in 2009, and was the co-winner of the John von Neumann Theory Prize from INFORMS in 2013. His research interests lie in simulation, computational probability, queuing theory, statistical inference for stochastic processes, and stochastic modeling.
For a detailed profile of Professor Peter Glynn, please go to: https://engineering.stanford.edu/profile/glynn
Professor Gerd Infanger
Gerd Infanger is a Consulting Professor of Management Science and Engineering at Stanford University. He teaches and conducts research in the area of stochastic optimization and its applications in finance. Infanger's research has concentrated on the development of the theory, algorithms and software for solving large-scale stochastic programs. His principal research goal is to make it practical to solve general large-scale stochastic programs and related control systems, with many stages looking into the future. His state-of-the art software system, called DECIS, combines decomposition and Monte Carlo sampling techniques and permits the solution of large-scale problems with numerous stochastic parameters.
Dr. Infanger is the CEO of Infanger Investment Technology, LLC, an investment advisor company founded in 1998 that manages mutual and hedge funds based on its state-of-the-art mathematical techniques. Infanger Investment Technology also provides risk optimization software based on stochastic optimization, asset allocation and equity portfolio optimization software.
For a detailed profile of Professor Gerd Infanger, please go to: https://engineering.stanford.edu/profile/infanger
Professor Samuel Wong
Samuel Po-Shing Wong received a Ph.D. in Statistics from Stanford University in 1997. He was on the faculty of various universities including University of California at Davis, The Hong Kong University of Science and Technology, and The Chinese University of Hong Kong. From January 2011 to July 2013, he was the head of research in CASH Dynamic Opportunities Investment Limited and was responsible in developing high frequency algorithm trading methodologies and corresponding risk management schemes. In June 2012, he was invited by the Institute of Mathematical Sciences at National University of Singapore to present a 4-hour lecture in “Algorithmic trading - mathematics, technology, finance and regulation”.
Other than his academic profile and his high frequency trading experience, Dr. Wong has been a business statistics consultant since 1991. He helped many companies in US as well as in Hong Kong to tackle practical issues by Statistics. In particular, he was a Basel II IRB modeler of a local bank in Hong Kong and offered risk management trainings to financial institutes such as China Development Bank, Hang Seng Bank, HSBC, Wing Hang Bank and many others. Even though he was not on the regular faculty, Dr. Wong taught data analysis in the MBA program as an Adjunct Professor in 2011-2012 for the Business School of The Hong Kong University of Science and Technology. In 2011-2012, Dr. Wong lectured Basel regulations and risk management in the Executive Leadership Programme in Global Finance of the Institute of Global Economics and Finance at The Chinese University of Hong Kong.Dr. Wong has also been serving as an external reviewer for the Financial Risk Management (FRM) examination of the Global Association in Risk Professionals (GARP) since 2006.
Professor Markus Pelger
Markus Pelger is an Assistant Professor at the Management Science & Engineering Department at Stanford University and a Reid and Polly Anderson Faculty Fellow at Stanford University.
His research interests are in statistics, financial econometrics, asset pricing and risk management. His work includes contributions in statistical factor analysis, high-frequency statistics, credit risk modeling and management compensation. He is particularly interested in how systematic risk factors and tail risk in the form of jumps influence the price of assets and the incentives of agents. For this purpose he has developed various statistical tools to estimate unknown risk factors from large dimensional data sets and from high-frequency data. He uses them empirically to predict asset prices and construct trading strategies.
Markus received his Ph.D. in Economics from the University of California, Berkeley. He is a scholar of the German National Merit Foundation and he was awarded the Fulbright Scholarship, the Institute for New Economic Thinking and the Eliot J. Swan Prize. He has two Diplomas in Mathematics and in Economics, both with highest distinction, from the University of Bonn in Germany.