Alex Shkolnik obtained his Ph.D. in Computational Mathematics & Engineering from Stanford University in 2015. His thesis work centered on computational methods for models used in the quantification and management of credit risk. Alex’s expertise lies in transform and Monte Carlo methods for the estimation and prediction of these risks. In particular, his ongoing focus is on the development of importance sampling techniques for complex systems encountered in finance and other research areas. Alex is currently a Postdoctoral Scholar at the Center for Risk Management Research and the Department of Statistics at UC, Berkeley. There, his research concentrates on building models for financial markets like Repo and CDS and applying modern statistical data analysis tools to identify risk factors in global equity markets.
Working Papers2018: Lisa Goldberg, Alex Papanicolaou, Alex Shkolnik, "The Dispersion Bias"
2016: Alexander D. Shkolnik, Lisa R. Goldberg and Jeffrey R. Bohn, "Identifying Broad and Narrow Financial Risk Factors with Convex Optimization"