Our main research theme centers on the intersection between business analytics (in other words, machine learning, data science, data mining, artificial intelligence) and decision making in business, especially in technology management. The interactions between the two key elements, analytics and decision making, are bi-directional. From analytics to decision making, we propose and test applications of novel machine learning techniques to improve decision makings in diverse management contexts, such as technology forecasting and technology strategy, and in various industries like IT manufacturing, energy, shipbuilding, oil refinery, etc. In the other direction, from decision making to analytics, we try to fill in the gaps between analytics and decision making by incorporating theoretical findings from management research into advances in machine learning methodologies. Our research interest further extends to the coevolution of business and machines: how AI technologies affect humans in their workplace, organizations, and industries? how users and firms adopt AI technologies?