Chapter Dinner Thursday Meeting - Feb 20, 2020
In the scientific community, the roles of principal investigator and project manager are often performed by the same person. This presentation will discuss some of the positive and negative side-effects of combining the two roles relative to factors such as time management, team building and staff development, the technical performance of the project, and the art of sustaining a long-term research program. For context, the presentation will begin with an overview of the types of projects and research environments with which the speaker has experience. Discussion will then focus on the many aspects of project and team leadership that scientific principal investigators must provide, with emphasis on the overlap with traditional project management. Finally, the discussion will conclude with some of the speaker’s lessons learned, including reflections on experiences with projects that did have a defined project manager, and an opening for comments and input from the audience. Learning Objectives include: 1) Insight into the role of the science principal investigator; 2) Insight into the interactions between technical leadership and project management.
Sandia National Laboratories is a multi-mission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.
Dr. David Stracuzzi is a Principal Member of Technical Staff at Sandia National Laboratories and has been studying machine learning and artificial intelligence for 20 years. He currently leads several projects that apply data-driven modeling and uncertainty analysis methods to tasks related to remote sensing data, pattern-of-life data, and data related to physics-based simulations. Prior to joining Sandia in 2010, Dr. Stracuzzi was a member of the research faculty at Arizona State University working on computational cognitive architectures for developing intelligent agents.
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