I look forward to contributing to the continued success and the mission of the INFORMS Data Mining Section, in promoting and disseminating research and applications among professionals interested in theories, methodologies, and applications in data mining and knowledge discovery. In the following, I briefly outline my vision and goals in terms of data mining research and education.
Nowadays, large volumes of data are collected in many interdisciplinary areas such as climate sciences, social sciences, epidemiology, mobile health, transportation, and so on. Accordingly, data-driven research in business analytics has been a trending area for investigating fundamental research questions and enabling cutting-edge applications for our industry and society. INFORMS has unique advantages in supporting and promoting such research endeavors, with diverse expertise of its members and strong outreach capacities to engage researchers, educators, and practitioners. As a member of INFORMS and the Data Mining Section, I will continue the commitment on advancing and promoting data mining innovations, which have both immediate and long-term impacts to our society by improving efficiency, reducing cost, and increasing satisfaction and fairness.
Data mining skills and experiences are critical to career success of our undergraduate and graduate students in programs such as business analytics, data sciences, and information technology. I am committed to bring data-driven research and techniques to the business programs and communities, by covering the state-of-the-art data mining topics in classes and designing modern data science curriculums. In particular, INFORMS and the Data Mining Section are well positioned to bring the data science way of thinking and practice to executives and administrators in business worlds. Specifically, the community can organize and offer series of tutorials and seminar talks for data-intensive research and professions. Examples of such series topics include predictive modeling, deep neural networks, dynamic decision optimization, reinforcement learning, and diversity, equity, and inclusion issues in data-driven research and applications.