Information and collaboration sessionMay 27, 2015

Prof. Omprakash Gnawali (University of Houston)

on behalf of the entire organizing team

Theme of this Hackathon

• Role of technology and technologists during disaster relief and post-disaster rebuilding, recovery, and reconstruction

• Relief = Immediate• Rebuild = Long term

Hackathon

Compress your learning and execution in a short 12 or 24 hour session

Demo or working prototype is key to learning

Example: Haiti Hackathon after Earthquake

• Open Street Map• Ushahidi• Crowdsource Reporting• Conjunction with Call Center• Real time view of crisis spots and resources to

guide connection

Source: http://techpresident.com/news/wegov/23477/techies-gather-port-au-prince-haitis-first-hackathon

Example: Atlanta Govathon

• Polls for Government: Each person has a voice

• Adopt a school: Crowdshare the cost• SkillMatch: Match employees and

employers with specific skills• Many more …Source: http://govathon.com/projects.html

Ideas from hack4nepal• https://geekli.st/hackathon/Nepal?tab=projects

Take Aways• Meet your colleagues: This is our

community working together to make a better Nepal

• Review the example projects for inspiration

• Make diverse teams

Logistics Reminders

• Remember to form your teams• Remember to research and read

about project ideas• Come to the Hackathon 15 minutes

early• Bring your sleeping bags (OR NOT)

Team Forming• Try to have a diverse team.• Registrant statistics:o42% Frontend Developerso23% Backend Developerso11% Mobile Developerso10% Designerso13% Other (Hacker, Idea

Generator, Python)

Prizes to the Winning Ideas

• Cash Prizeso1st place: Rs. 35,000o2nd place: Rs. 25,000o3rd place: Rs. 15,000

Ideas• Your idea has to be usable by

users (government, relief organizations, volunteers, etc.)

• It might be a cool idea, but if there are no users, it does not add value to the community.

Simplicity of Ideas• E.g. Google spreadsheet, Google forms was very

useful in coordinating the efforts during the relief efforts. Make no mistake, these are complex tools (technologically speaking) but are tools that are readily available and are intuitive and easy to use

• This means do not go for some complicated solution just because it is technologically sophisticated. E.g. advanced machine learning system, computer vision system, etc.

Ecosystem• Identify risks for adoption (this is a

startup perspective)• Technical risk is usually not that

risky• Is your product usable?• Is your product serving a need?• Will the users adopt your product?• Does the model work?

Few Last Tips…• Don’t reinvent• Specialize instead of targeting a

broad category• E.g. Uber: it is a USD 40 billion dollar company, it specializes on one need: get you a ride, it is usable. It does one thing and does it well!•Don’t think about creating a broad category of products