Program
Goals
The overall goal of this NRT program is to train scientists with an interdisciplinary background and conceptual framework to synthesize two divergent fields - plant science and computational/data science - and to integrate knowledge at multiple levels of biological organization. At the end of the program the trainees should be competent in the 5 areas. Detail list of areas is found here.
Contents
Our program has 5 components. All components will be open to non-trainees interested in computational plant science. Details about the components is found here.
Professional Development
Professional development preparing Trainees for a broad career path is a central component of the IMPACTS program. Trainees will participate in the following professional development activities aiming at bolstering the Trainee’s communication, leadership, management, mentoring, and teaching skills.
For more details click here.
- Communication
- Leadership and Management
- Teaching
- Mentoring
- Career Advancement
Organization
| Committee | Description | Members |
|
Curriculum |
Design, implement curriculum, put together course form, and work with departments/programs to clear obstacles |
Dan Chitwood, Jyothi Kumar Tammy Long, Shinhan Shiu, Addie Thompson, Robert VanBuren |
|
Evaluation |
Design evaluation plan, oversee evaluation data gathering, interact with external evaluator |
Jyothi Kumar Tammy Long, Shinhan Shiu |
|
Executive committee |
Work with director/co-directors on program management, review applications, delegate project report items among subcommittees |
Karen Cichy, Patrick Edger, Jyothi Kumar, Tammy Long, Daniel Morris, Shinhan Shiu, Jianrong Wang, Yuying Xie |
|
Recruitment |
Interface with degree programs on advertising the NRT, attend major URM conferences in campus to recruit students |
Erich Grotewold, Ning Jiang, Kevin Liu |
|
Symposium or Retreat |
Work with trainees to identify symposium topic, invited speakers, identify date, venue, and other arrangements |
Trainees: Seth Hunt, Mckena Lipham |
Reports & Logic Model