Design and Analysis of Experiments

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The Design and Analysis of Experiments Conference (DAE 2017) will be held at UCLA on October 12-14, 2017. The goal of the conference is to bring together leading researchers of international repute, graduate students and researchers from industry and government, and provide a forum for interaction, discussion, and exchange novel research ideas in design and analysis of experiments. Funds will be used to support students and junior researchers to attend the conference. The conference will be widely announced and selected individuals and organizations will be specially contacted to broaden the diversity of the participants. Of particular note is that junior researchers who participated in earlier DAE conferences continue to speak very highly of the impact of mentorship and funding they had received from DAEs has had on their careers. Many of them have gone on to make significant contributions in the development of the theory and applications of experimental design, including a few who have received NSF career awards and several more who are now recognizable researchers in the field. Organizers of the conference have full confidence that DAE 2017 will follow the tradition in the previous years and have a significant impact on nurturing young researchers in design and analysis of experiments. Organizers will plan for mentoring activities by carefully pairing senior and junior participants in advance, provide some funding for the students and junior researchers and continue to make a serious effort to broaden participation by under-represented groups. Design and analysis of experiments are indispensable in the scientific process as they form an integral part of the discovery process in virtually all scientific disciplines. The present era of big data has brought very exciting and new areas of research for design and analysis of experiments. DAE 2017 is timely and will help advance cutting edge research ideas to tackle challenging emerging issues. Themes of DAE 2017 include internet experiments, designs for big data, computer experiments, uncertainty quantification, optimal designs in the pharmaceutical industry, computational methods in design of experiments, applications of designed studies to solve industrial and engineering problems, and designs for non-normal data. Additional information is available at the conference web site: http://www.stat.ucla.edu/~hqxu/dae2017/