Programme Thesis
The NSF Trailblazer Engineering Impact Award (TRAILBLAZER) supports individual investigators pursuing bold, novel research projects that address national needs, strengthen U.S. leadership, and catalyze new industries or capabilities in priority areas such as AI, bioengineering, quantum engineering, robotics, and nuclear engineering. It exists to fund high-risk, high-reward research directions that are distinct from the investigator's prior work, with an emphasis on creativity and innovation.
Selection Criteria
- Novelty and creativity of the proposed research direction, distinct from the PI's previous or current work
- Potential to address a national need or grand challenge (e.g., opioid crisis, AI, bioengineering)
- Potential to catalyze new industries or capabilities that increase U.S. leadership
- PI's track record of innovation and creativity (not necessarily in the same field)
- Feasibility and soundness of the research plan within the award scope
- Broader impacts, including education, workforce development, and societal benefits
- Budget justification and appropriateness
- Letters of collaboration and institutional support
Past Winners / Cohort Profiles
The TRAILBLAZER program is relatively new (first awards in FY 2024). Past winners include individual investigators from U.S. academic institutions proposing high-risk, transformative engineering projects. Examples include projects in AI-driven bioengineering, quantum sensing, and robotics for national security. Archetypes: mid-career to senior faculty with a history of unconventional ideas, often pivoting from established research to a new direction. No specific named winners are listed on the program page.
Ideal Candidate Fingerprint
A U.S.-based academic investigator (typically a faculty member at a U.S. university or nonprofit) with a strong record of innovation and creativity, proposing a bold, novel research project that is clearly distinct from their prior work. The project should address a national need or grand challenge, leverage cutting-edge engineering or computational methods, and have potential for transformative impact. The PI should be able to demonstrate a track record of thinking outside the box and executing high-risk ideas.
Recommended Framing
Eniola should partner with a U.S. academic host institution (e.g., University of Michigan, Princeton, NYU) where a collaborator like Kent Berridge or Nathaniel Daw can serve as co-PI or letter writer. Frame the CCT model as a bold, AI-driven bioengineering approach to addiction treatment that directly addresses the U.S. opioid crisis (a national need). Emphasize that this research direction is distinct from Eniola's prior pharmacy and bioinformatics work, leveraging his computational neuroscience and Bayesian modeling skills to create a new class of pharmacological interventions. Highlight his independent track record, preprints, and endorsements from leading neuroscientists as evidence of innovation and creativity.
Watch Out
Eniola is not a U.S. academic and does not have a U.S. institutional affiliation, which is a requirement for NSF grants (PI must be at a U.S. institution). He must secure a U.S. host institution willing to submit the proposal. Additionally, he is not yet enrolled in a PhD program, which may raise concerns about his ability to lead a major research project. The program is for individual investigators, so a co-PI arrangement may be necessary. The budget must be managed by the U.S. institution.