Talk Title Bridging Online and Offline Learning Towards Improved Data-Driven Operations Management Abstract Modern organizations’ operations require increasing use of machine learning tools. While supervised machine learning traditionally excels at making predictions based on i.i.d. offline data, many modern operational tasks require making sequential decisions based on data collected online. Bridging the gap between offline…

Talk Title: Provably Good Region Partitioning for On-Time Last-Mile Delivery Abstract: People expect faster delivery of goods ranging from groceries to medicines. Managing on-time delivery systems is challenging because of the underlying uncertainties and combinatorial nature of the routing decision. In practice, the efficiency of such systems also hinges on the driver's familiarity with the…

Talk Title Benefits of Randomization in First Order Algorithms for Min-Max Optimization Abstract Modern data science applications require solving high dimensional optimization problems with large number of data points. Min-max optimization provides a unified framework for many problems in this context ranging from empirical risk minimization and distributionally robust optimization in machine learning to medical…