Dear Review Volunteer, 

This survey is being conducted to ensure a high-quality review process for proposals submitted to the ExpandAI program. Volunteers recruited will have the opportunity for direct engagement, and better acquaintance with the ExpandAI program. Questions with an * require an answer.

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* Please enter your contact Information:

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* Please enter your email address:

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* Please enter your institution name :

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* If you have indicated ‘Other’, or ‘MSI Other’, please specify here:

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* Job title: (e.g., Assistant Professor, Research Scientist, etc.)

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* Please enter your homepage URL if available, otherwise leave blank:

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* Are you an investigator or senior personnel on an ExpandAI award? 

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* If you answered "yes" in the above question, please specify the project title:   

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* Are you an investigator or senior personnel on an AI Institutes award?

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* If you answered "yes" in the above question, please specify the project title:   

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* Do you plan to submit a proposal to the ExpandAI Capacity Building Pilots (CAP) track in the next year?

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* Do you plan to submit a proposal to the ExpandAI Partnerships (PARTNER) track in the next year?

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* NSF may also recruit you to review proposals in a “review panel”, meaning that you would be assigned several proposals to review and would meet with other reviewers and NSF staff virtually. Panels would make funding recommendations to the agencies. This review opportunity will be infrequent. No reviewer would be asked to serve on a panel in the program more than once in a year.  Would you like to be contacted about opportunities to serve on an ExpandAI panel?

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* Please indicate the areas in which you are willing and qualified to review proposals.

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* Please provide a brief paragraph (3-4 sentences) summarizing your broad areas of disciplinary expertise as they relate to the ExpandAI program and your potential service as a reviewer. A sample paragraph is provided below as a guide to the types of content that should be included.

Sample Faculty Expertise: Jane Doe holds a Ph.D. in Computer Science and has served as a faculty member in the Electrical Engineering & Computer Science department for ten years. Dr. Doe has managed several NSF-funded projects with a focus on data analytics and machine learning. She has published numerous conference papers and journal articles on the application of machine learning to the analysis of data in domains such as gene expression, biomedical implant design and emotions behind social media content.

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