Engineering East

Friday, October 18 at 5:00pm to Saturday, October 19 at 5:00pm.



Three top teams receive up to $10,000 in cash prizes. Judges will determine the prize amounts.





Data engineering workflows, which leverage database infrastructure, computational capacity, and analysis technologies, are critical for the handling of and knowledge generation from contemporary massive datasets. However, data quality, size, structure, sensitivity, and distribution as well as security, computational capacity, and efficiency of storage and transmission are key factors that must be considered when deploying data engineering solutions for such datasets. Datasets with patient’s health information, which are often characterized as enormous, unstructured, biased, sparse, and consisting of private and sensitive information, represent an ideal testbed for designing such solutions. Using the All of Us dataset, a massive patient health information dataset for which Florida Atlantic University (FAU) has privileged access, this hackathon challenges participants to conceptualize and develop new guidelines, tools, methods, protocols, and best practices to facilitate precision medicine. What issues of fairness might be encountered when developing predictive models given the demographic distribution within the dataset, and how might these pitfalls be overcome? What are the limitations and solutions for tackling the uneven availability of different data sources for study participants in the dataset? How might we reach the goal of individualized healthcare?



Students in teams of three to five will select a data or computational hurdle within the All of Us dataset and devise a solution to it. Teams may consider open-source systems and develop a software prototype as a part of their solution. Teams may present a solution that consists of a design and architecture. Teams are expected to perform the needed research and may interview stakeholders to gain a better understanding of the problem. A properly considered problem will lead to a more meaningful solution.



Teams should explore the All of Us webpage from the National Institutes of Health as well as read the recent journal publication describing the All of Us Research Program, and reflect upon the mechanisms that the research consortium facilitates data extraction and analysis for the public and for permitted research institutions, such as FAU. Other key resources are:

· Description of the current All of Us Research Program data snapshot

· Explanation of the diverse All of Us Research Program data sources

· Illustration of and registration portal for the All of Us Research Program researcher workbench



Pre-hackathon workshops in EE (Engineering East) room 106 from 4:00pm to 6:00pm on September 27, October 4, and October 11 come with free hackathon fuel—pizza!



Teams will participate in pre-hackathon workshops where leading researchers and practitioners from leading healthcare companies and other partners will present challenges and opportunities in developing engineering solutions for interrogating massive datasets. These talks are scheduled from 4pm to 6pm on September 27, October 4, and October 11, and will help teams generate ideas and develop solutions for the hackathon.



Kickoff meeting on September 27, 4:00 – 6:00 pm in EE 106

Speakers: Michael DeGiorgio and Vishnu Chinta



Teams will be evaluated based on:

· Insights: What did we learn about the problem and solution?

· Alignment: How aligned is the solution with the goals of personalized healthcare?

· Impact: How broad is the impact of the proposed solution?

· Usability: Is the solution easy to use and adopt?

· Generalizability and Scalability: Can the tool be used for different AI systems and at scale?



For industry sponsorship opportunities, please contact:



Michael Miller

Director of Development

College of Engineering and Computer Science

777 Glades Road, EE308

Boca Raton, FL 33431-0991 Phone: 561-297-2083 Cell: 805-448-5430 E-mail: millermichael@fau.edu

Website: eng.fau.edu – transcendtomorrow.fau.edu

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