We are delighted to invite you to submit a full application for funding from the AWS Impact Computing Project at the Harvard Data Science Initiative, using the submission protocol described below. The proposal is due on Friday, November 14th, 2025. Funding of up to $125,000 total direct costs (maximum 18-month term) will be made available for each of a small number of faculty-led projects.
Application Instructions
Please submit the three documents (see below) to datascience@harvard.edu with a copy to lawrence_weissbach@harvard.edu. We will acknowledge receipt within 24 hours. If you do not receive an acknowledgement, please be in touch with Lawrence as soon as possible.
This document contains information about the following:
- Important dates
- Proposal development and submission instructions
- Restrictions on personnel
- Description of Research Areas of Interest
- Budget
Please read this solicitation to (i) determine if this funding mechanism is a good fit with your research objectives; and (ii)learn more about the AWS Impact Computing Project at the HDSI.
Please be in touch with any questions, directing them to Lawrence Weissbach (lawrence_weissbach@harvard.edu).
The AWS Impact Computing Project at the Harvard Data Science Initiative (HDSI) is an alliance with Amazon Web Services (AWS) aimed at reimagining data science and high-performance computing to identify potential solutions for society’s most complex challenges.
This collaboration supports faculty-led research projects across Harvard. Awards to faculty are made under a master sponsored research agreement facilitated by the HDSI and Harvard’s Office for Technology Development.
Request for Proposal
The HDSI seeks research proposals from faculty interested in pursuing research that leverages data science tools and high-performance computing for achieving actionable insights into the causes and solutions to climate change, food (in)security, biomedical/clinical sciences, and social determinants of health.
Importantly, this is a targeted RFP that focuses on two Central Themes; proposals that do not fit within either of these themes will not be considered for funding:
a. Intensive computational data science: Research that employs computationally intensive technology (preference for large scale high performance computing as compared to laptop compatible) to accelerate the impact of science. Examples under this theme include: transformative algorithm or a computationally intensive, high-impact project that requires the horsepower of AWS high performance cloud computing resources for development. The proposal should address the following question: Describe how the output (i.e., most likely an artifact) will (i) be dependent on high performance computing and (ii) be transformative to society, even if not immediate, and how it can be deployed for convenient open access, allowing for further innovation?
Essentially, we are prioritizing (i) high risk-high reward intensive computationally based studies and (ii) user access.
b. Model building to streamline computational analysis: Will a new model be built (e.g., molecular modeling) to not only achieve broad and substantial impact, but introduce streamlined processes (i.e., “shortcuts”) that significantly reduce the computational resourcing needs?
Funding of up to $125,000 total direct costs (maximum 18-month term) will be made available for each of a small number of faculty-led projects. HDSI encourages research proposals seeking “seed” funding, i.e., having smaller budgets and shorter duration, and will look to fund a mixture of project sizes. The deadline to submit is Friday, November 14th, 2025.
Eligibility
This program is open to individuals who (i) hold a faculty appointment at a Harvard school; and (ii) have principal investigator rights at that school. (Please note: Harvard Medical School faculty must hold a faculty appointment with PI rights in one of HMS’s Quad-based, preclinical departments). Personnel (e.g., co-PIs; co-investigators; postdocs, etc.) who are not affiliated with Harvard are not permitted to receive funding.
Faculty may submit multiple applications.
Application Process and Requirements
Informational office hours: For any interested applicants who have questions that are not answered in these RFP instructions, please contact Lawrence Weissbach. If enough applicants have questions, HDSI will consider hosting an informational webinar.
Research Computing consultations: To clarify the computational and/or high-performance cloud computing services you will require, applicants should consider consulting with staff from the Research Computing office to assess project needs for data science tools and high-performance computing, i.e., evaluating Harvard and AWS data science services and computational capacity deemed essential for performing the proposed studies. Please contact Lawrence Weissbach if this is of interest.
Evaluation
Submissions will be evaluated on adherence to one or both of the two Central Themes noted above as well as the questions being asked in terms of (i) potential innovation; (ii) scientific merit; (iii) potential feasibility of translating expected research findings and insights into real world applications and societal impact; and (iv) the complexity and scale of novel computing or analysis needs.
Importantly, proposed research studies are expected to:
– Lead to one or more of the following data science-related outputs for open access and adoption by varied user groups: new methodological/predictive approaches or analysis, or artifacts (see below):
– A key attribute that AWS will expect within the research plan is a detailed description of the anticipated output/artifact. This could include, for example, software, data cube, dashboard, database, workflow, platform, web portal, foundational models, algorithms, and/or large language models (LLMs).
Note: Contingent upon availability, AWS may provide a finite amount of AWS Cloud Compute Promotional Credits to offset computational costs anticipated for your proposed research.
Research Areas of Interest
Climate Change/Food Security
Projects are encouraged that focus on FAO hot-spots and Global Food Security Index weak performers. Please provide a description of implications for real-world solutions that may arise from the research, for example scalable policy solutions, food supply chain improvement, improved climate resilience of crops, better irrigation systems, improved processing and storage solutions, or development of early warning systems for farmers, researchers, and policy makers. These examples are illustrative, and we encourage novel and ambitious proposals on additional topics.
The adverse impact of climate change is escalating in rapid fashion, and every facet of society and the environment is being impacted. The cascading effect, triggered by the warming of our planet, has become a global crisis with far-reaching consequences for our health and survival.We seek research that will contribute to our understanding and/or mitigation of climate change. Examples include developing data, data science methodologies, and their tailored applications for: 1) identifying key drivers of climate change, 2) developing effective climate change mitigation strategies; 3) addressing inequities, vulnerabilities; 4) informing adaptation efforts; and 5) quantifying current and future impacts. These are illustrative only and we welcome research plans that propose areas of inquiry that fall outside those described above.
Biomedical/Clinical Sciences
The development of open-source tools that will identify causes of disease, predictive clinical modeling, improving the accuracy, speed, and practicality of disease diagnosis (e.g., improving point-of-care diagnosis and treatment, more sensitive and accurate imaging), and transforming personalized health are also relevant.
Some examples of relevant research areas are (i) disease prevention; (ii) disease biology/pathophysiology/pathogenesis, including disease models, libraries (e.g., DNA-encoded libraries), gene and protein expression, and immunology; (iii) new medicines (e.g., small molecules, biologicals, aptamers, gene therapy) for treating disease (e.g., improved screening protocols, structure/function studies, predictive toxicity, mechanism of action, disease tissue target selectivity, etc.), (iv) disease diagnosis (accuracy, speed, practicality); (v) identifying and validating new drug targets, including via CRSIPR and gene editing; (vi) precision/personalized medicine; (vii) approaches to streamline and increase the success of drug development and clinical trials, including statistical analysis and patient stratification (e.g., based on mutational analysis); predictive modeling; (viii) clinical decision-making tools/nanotechnology, including imaging, pathological analysis, and advanced biochemical/molecular analysis (ix)bioinformatics; (x) developing rationales for drug combinations and/or drug repurposing/repositioning; and (xi) extracting value from real-world data (RWD) (e.g., clinical trial study design, drug treatment responses).
Addressing global diseases will be prioritized. Examples include those caused by genomic abnormalities (DNA mutations, translocations, etc.) and include diseases with high mortality or severe impact on quality of life such as cancer, neurodegenerative disease, aging (balance, mobility, auditory/ocular issues, etc.), heart disease, diabetes, dementia, autoimmune disease, and mental health. This list is illustrative only, and studies on other disorders are encouraged. Topics such as environmental-related illness, nutrition, or addiction are not covered by this topic area.
The overarching goal should be to generate actionable insights into causes, detection, and/or treatment of widespread human illness that can address these complex and often poorly understood health-related afflictions that adversely impact large swaths of the global human population.
Social Determinants of Health
AWS/HDSI looks to support faculty-led research projects that contribute to our understanding of SDOH by examining social and environmental drivers of adverse health outcomes and health inequities using population level data. Study designs can include but are not limited to observational studies, panel studies, intervention studies, cohort studies, etc. Researchers might leverage different types of data (e.g., electronic medical records, claims data, etc.) and can include environmental factors such as toxicant exposure and the physical built environment as well as social factors such as living conditions, socioeconomic status, and social inequalities. Funding is also available for studies that examine factors of health equity within and across populations.
Important Information
Friday, November 14th, 2025: Full application due.
Before sending the full application to HDSI, please submit your research proposal with budget through GMAS prior to submitting to HDSI, to ensure that all required approvals are obtained and the budget, including the IDC rate, are confirmed. Please work with your grants manager or assigned Sponsored Programs officer on this. Tentatively, please use January 1st, 2026, as the earliest date for funding disbursement.
Note: When submitting your GMAS application, please add Kevin Doyle, the HDSI Assistant Director of Programs & Operations, as an “Interfaculty Involvement Dept. Administrator” and Lawrence Weissbach, HDSI Scientific Director, as an “Observer”.
Proposal submissions should include the following three documents:
(i) A copy of the GMAS approval notice
(ii) A WORD version of the research proposal
(iii) The budget as an Excel spreadsheet. The budget template described below must be used.
December, 2025: Edits and/or funding decisions communicated to research teams
Proposal Support
Please be in touch at any time during the development process. Please direct all questions to Lawrence Weissbach at: lawrence_weissbach@harvard.edu