AWS Research Computing Fund: Call for Letters of Intent

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 LOIs that propose 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 examples of research questions that are of interest. These are illustrative only and we welcome LOIs that propose areas of inquiry that fall outside those described above.

This topic emphasizes research proposals that employ data science methodologies (e.g., AI/ML, data analytics, algorithms, artifact development) to advance health and catalyze actionable insights to decrease disease burden worldwide.

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 (SDOH) are defined as societal and environmental conditions encompassing where people are born, live, learn, work, play, worship, and age that affect a wide range of health, functioning, and quality-of-life outcomes and risk. Social determinants of health can be grouped into the following general categories: Economic Stability, Education Access and Quality, Neighborhood and Built Environment, and Social and Community Context. 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.