Here are two research papers I wrote with my then-PhD-student Dr. Elcin Cetinkaya that might be of interest to my operations research readers. One is on a data-driven approximation algorithm to portfolio management with quantile constraints. While the algorithm is very simple, it performs very well in practice. (Calafiore considers a similar problem from the perspective of finding the minimum sample size that will provide certain probabilistic guarantees to the manager, in a setting slightly more general than ours.) Our paper is the latest version of the report based on our ISMP presentation back in 2012. The other is on new product launch. The key contribution of that paper is to incorporate robust optimization to the parameters of the Bass diffusion process driving innovation adoption, in a context where the decision maker seeks to maximize his Net Present Value. For tractability purposes we enforce that the uncertain parameters must take either their nominal, best-case or worst-case values. While this requires the use of integer variables in the uncertainty set, which precludes an immediate use of strong duality as is traditionally done in classical robust optimization, we show that a total unimodularity property of a parametrized version of the problem allows us to obtain tractable reformulations of the master problem.