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Chen Institute & Science Prize for AI-Accelerated Research

Chen Institute

Through the Tianqiao and Chrissy Chen Institute and Science Prize for AI Accelerated Research, the editors of Science and the Chen Institute will recognize innovative young researchers who apply techniques in Artificial Intelligence (AI), such as Machine Learning, Natural Language Processing, Computer Vision, or Deep Learning, to address basic research questions in the physical or life sciences.

Successful applicants will have made a fundamental advance in an empirical science that would not have been feasible without using an AI-associated technique to augment the standard repertoire of methods in that field. The results should inspire other practitioners in the discipline to likewise take advantage of AI in enabling their future research.

The prize is awarded for outstanding research performed by the applicant as described in a 1,000-word essay.

AI Based Application Success Predictor

1. ⭐⭐⭐⭐⭐ Highly innovative neuroscience idea

This is the strongest predictor.

The Chen Institute explicitly describes its Research Grant Awards as seed funding for highly innovative projects that are often considered too risky by traditional funding sources.

Therefore, a competitive proposal should offer something beyond incremental research:

New biological mechanism.
New neuroscience technology.
New model organism.
New computational approach.
New brain-machine interface.
New way of studying behavior or cognition.
New conceptual framework.

Recent 2026 awards included novel CRISPR tools in an emerging species, fiber-optic ultrasound for brain imaging, and AI-based analysis of social connection.

2. ⭐⭐⭐⭐⭐ Potential to open a genuinely new direction

The Chen seed-grant model is designed specifically to help faculty develop new directions of neuroscience research.

This makes "new direction" particularly important.

A proposal should make clear:

What new research direction will this grant allow the investigator to establish?

Strong examples include:

Existing expertise + new technology → new neuroscience question

Existing biological observation → new mechanistic research program

Existing engineering capability → new brain-science application

3. ⭐⭐⭐⭐⭐ High-risk / high-reward potential

The Chen Institute is unusually receptive to research that conventional funding agencies may consider too risky.

Its grants are explicitly intended to support projects that are too risky for more traditional funding sources and generate proof-of-concept evidence that can unlock larger-scale funding.

This means a proposal should not be artificially conservative.

The ideal profile is:

High scientific upside + credible experimental path

rather than:

Low risk + incremental result

or:

Extremely speculative idea + no credible experiment.

4. ⭐⭐⭐⭐⭐ Interdisciplinary neuroscience

Interdisciplinarity is deeply embedded in the Chen Institute model.

Its 2026 awards span:

Molecular/cellular neuroscience.
Systems neuroscience.
Social and decision neuroscience.
Brain-machine interfaces.
Engineering.
AI.
Genetics.
Imaging.
Information theory.

The Institute also describes its environment as interdisciplinary and collaborative.

Strong applications can therefore benefit from combining fields such as:

Neuroscience + AI

Neuroscience + engineering

Neuroscience + computation

Neuroscience + molecular biology

Neuroscience + imaging

Neuroscience + behavioral science

The important point is that the interdisciplinary combination should solve a problem that one discipline alone could not address effectively.

5. ⭐⭐⭐⭐⭐ Clear proof-of-concept potential

This is a particularly distinctive Chen Institute criterion.

The purpose of the seed funding is to generate preliminary/proof-of-concept data that can establish feasibility and support subsequent larger-scale research funding.

A strong proposal should therefore identify a specific result that will demonstrate:

The technology works.
The biological hypothesis is plausible.
The experimental system is feasible.
The new methodology produces useful data.
The research direction warrants larger investment.

A useful proposal question is:

What decisive experiment can this seed grant accomplish that changes the investigator's ability to pursue the larger research program?

6. ⭐⭐⭐⭐☆ Strong scientific significance

Innovation alone is not enough.

The project should address an important neuroscience problem involving:

Brain function.
Neural circuits.
Molecular/cellular mechanisms.
Cognition.
Behavior.
Neural computation.
Brain-machine interfaces.
Neurological or psychiatric conditions.
Brain development.
Neural repair.

The 2026 portfolio included projects on spinal-cord regeneration, hypothalamic circuit development, social connection, stroke recovery and brain imaging technology.

This demonstrates that Chen's scope is broad, but the underlying project must still answer a meaningful neuroscience question.

7. ⭐⭐⭐⭐☆ Technology that creates a new experimental capability

A recurring feature of Chen-funded research is the creation of new tools or experimental platforms.

Examples from recent awards include:

New CRISPR tools for an emerging model organism.
Fiber-optic ultrasound sensors for functional brain imaging.
Long-term single-neuron recording platforms.
AI-based neural-dynamics analysis.

This suggests an important success pattern:

New capability → previously inaccessible experiment → new neuroscience discovery

A proposal is stronger when the technology is not merely an engineering deliverable but enables a scientifically important experiment that could not previously be performed.

8. ⭐⭐⭐⭐☆ Strong investigator capability and appropriate environment

Because these are seed grants intended to establish new research directions, reviewers need confidence that the investigator can actually execute the proposed work.

Relevant strengths include:

Relevant publications.
Technical expertise.
Access to specialized equipment.
Appropriate laboratory infrastructure.
Collaborators with complementary expertise.
Access to model organisms or patient data.
Computational resources.

The Chen Institute's grant program operates within Caltech's interdisciplinary neuroscience environment, which is itself an important part of the funding model.

9. ⭐⭐⭐⭐☆ Potential to unlock larger external funding

This is one of the clearest strategic outcomes of Chen research grants.

The Institute states that its seed grants generate proof-of-concept data usually necessary to demonstrate feasibility and support larger-scale research funding.

Therefore, a competitive proposal should ideally show:

Chen seed funding
↓
Proof-of-concept data
↓
Publication / validation
↓
NIH or other major grant
↓
Expanded neuroscience research program

This makes the Chen award particularly valuable for researchers proposing a new direction that is not yet sufficiently mature for a large external grant.

10. ⭐⭐⭐⭐☆ Clear fit with the particular Chen program

The Chen ecosystem contains several different opportunities.

Program	Primary focus
Research Grant Awards	Faculty seed funding / new neuroscience directions
Graduate Innovator Grants	Out-of-the-box graduate-student projects
Postdoctoral Innovator Grants	Novel postdoctoral research directions
Travel Grants	Graduate student/postdoc conference and learning travel
Fellowships	Graduate and postdoctoral career development
Brain-Machine Interface Center	Neurotechnology and brain-machine interfaces
Systems Neuroscience Center	Neural systems and computation
Social & Decision Neuroscience Center	Social cognition, decision-making and related neuroscience
Molecular & Cellular Neuroscience Center	Cellular and molecular brain mechanisms

The Graduate/Postdoc Innovator program is particularly explicit: projects should be conceived and driven by trainees and not be an obvious extension of their laboratory's existing research.

Specialties

Artificial Intelligence

Biomedical Engineering

Eligibility Requirements

The editors of Science and the Chen Institute will recognize innovative young researchers who apply techniques in Artificial Intelligence (AI), such as Machine Learning, Natural Language Processing, Computer Vision, or Deep Learning, to address basic research questions in the physical or life sciences.

Sponsor Details

Sponsor Institute/Organizations: Chen Institute

Sponsor Type: Corporate/Non-Profit

Address: 1200 New York Avenue NW Washington, DC 20005

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