About Global Entrepreneurship Society at UCLA

The Global Entrepreneurship Society at UCLA (formerly Three Day StartUp at UCLA) is an entrepreneurial and educational extracurricular program aimed at university students, emphasizing hands-on learning.

Our mission is to foster entrepreneurial mindsets and skills within our student community to tackle problems, launch new ventures, and cultivate successful businesses with global impacts.

Our approach encompasses every stage of the entrepreneurial journey, from ideation and team formation to market research, rapid prototyping, and final pitch presentations. Through workshops and practical experiences, members directly apply their acquired knowledge to develop sustainable business models, with a focus on expanding startup ideas into thriving global enterprises.

Website: https://gesatucla.org/

About Trustworthy AI Lab @ UCLA

Trustworthy AI lab at UCLA envisions AI 2.0 as being driven by trustworthiness (beyond performance), and being built upon generative data (on top of raw data). Our lab drives advancements in Generative Data for marketing, healthcare, and finance sectors. We develop data-centric tools, such as artificially generated tables and conversations, which enables privacy-preserving data sharing and reliable scenario exploration.

http://www.stat.ucla.edu/~guangcheng/index.html

GES X Trustworthy AI Lab Hackathon Background

Prize Pool Allocation: $1500

The 2024 Hackathon is dedicated to pioneering Generative AI for Data Collaboration – an innovative approach to redefine data privacy and protection. Let's design and implement robust Generative AI solutions set to revolutionize the way data is shared within digital ecosystems.

Who Should Participate

  • Undergraduate & Graduate Students in the US
  • Interested in Data Science and Machine learning
  • Eager to meet like-minded peers
  • Ready to harness creativity and empower organizations
  • (No restrictions to academic majors)

Objectives

Prompt: https://docs.google.com/document/d/1-SpZ2NWVQix0dEYfMaBKvXFI5MRIp2-nDeOiCzXfrtw/edit?usp=sharing

Leverage predictive modeling to enhance forecasts of user engagement and interactions with content. The challenge lies in refining the accuracy and efficiency of these predictions, crucial for content publishers and advertisers focused on optimizing user experiences and advertising effectiveness.

Participants will utilize 2 comprehensive datasets that detail user demographics, user interactions, engagement metrics, and advertisement specifics from both the content publishers' and advertisers' perspectives. By employing innovative data analytics and machine learning techniques, participants will tackle this dual challenge and cultivate an engaging user experience that drives the digital publishing and advertising industries.

Challenge

GES X Trustworthy AI Lab Hackathon tends to empower both content creators and advertisers by fostering collaboration and innovation in developing platforms that serve the needs of both parties. The challenge lies in creating a seamless integration between content creation and advertising, ensuring that both sides can thrive while maintaining a positive user experience.

Event Schedule

Date: May 29th - June 28th, 2024, PST

Location: Online

May 29th 8 p.m. PST

  • Prompt Release and Kickoff (on Zoom)

June 16th

  • Registration Deadline

June 21st

  • First Round Submission

June 27th

  • Second Round Submission

June 28th 4-6 p.m. PST

  • Judging Day (on Zoom)

Judging Panel

Professor Guang Cheng, director of Trustworthy AI Lab, UCLA

Dr. Chi-hua Wang, Postdoc at the Trustworthy AI Lab, UCLA

Mr. Minrui Gui, PhD student at the Trustworthy AI Lab, UCLA

Mr. Shaoqing Yuan, Senior Applied Scientist, Amazon

Mr. Harry Xu, Machine Learning Engineer, Snap

Mr. Ken Lu, Chief Cloud System Architect, Intel

Prize Pool Details

  • Prize Pool: $300(third), $500(second), $700(first)
  • Total Prize Pool Allocation: $1500
  • Additional Benefit: offer summer internship working in the Trustworthy AI Lab @ UCLA. Past summer interns have gone on to attend top graduate schools, such as Berkeley and Princeton.

Submission Requirements

  • Runnable Data Clean Room code in a Github Repo
  • CTR Prediction machine learning code in a Python package/script
  • 3-5 page presentation slides, not including index and references

Judging Criteria

  • Accuracy and Predictive Performance: Projects will be evaluated based on the accuracy and reliability of their predictive models.

  • Innovation and Creativity: Judges will assess the novelty and creativity of the solutions, especially in terms of how participants handle data integration, synthesis, and privacy-preserving techniques.

  • Scalability and Efficiency: The ability of the solution to scale efficiently and process data in real-time will be evaluated. Projects need to demonstrate performance stability and resource management.

Resource Package

https://drive.google.com/drive/folders/1lVOT0evxCsxPpDsTI37tPWeq0TgQ0x5w?usp=drive_link

Kickoff Event

https://drive.google.com/file/d/1QHxI3VElJEVvNjrQjxpuMLz6XDSAWIZP/view?usp=sharing

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