Trustworthy AI Lab X GES Hackathon
For Young Entrepreneurs to Make Things Happen
Join the Event
Overview
This year’s hackathon is dedicated to developing Data Clean Rooms—an innovative solution to redefine the landscape of data privacy.
Data Clean Rooms provide a secure space for data exchange between organizations, safeguarding user privacy while enabling collaboration and insights. This cutting-edge technology meets the growing demand for transparent and secure data-sharing practices.
Join us as we embark on a journey to design and implement robust Data Clean Rooms, set to revolutionize data exchange in digital ecosystems. Participants will harness their skills, creativity, and expertise to craft solutions that empower organizations to further unlock the value of data. Let’s build a safer, more transparent digital future—one where privacy is paramount, and innovation thrives.
May 22nd - June 1st
Be with us @UCLA Online
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)
Event Schedule
May 29th, 8 p.m. PST
Prompt Release (On zoom)
June 16th
Kickoff and Initial Data Analysis
Milestone: Complete an initial webinar to introduce participants to the data clean room concept, datasets, and hackathon rules.
Deliverable: Participants submit an initial data analysis report, outlining their understanding of the datasets and preliminary insights.
June 17th – June 21st
Development and Integration
Milestone: Participants develop their predictive models within the Data Clean Room scenario, calculating aggregate statistics, training machine learning models, and integrating synthetic data collaboratively between publishers and advertisers.
Deliverable: A prototype model that demonstrates the initial capability to calculate aggregate statistics, train machine learning models, and use synthetic data across both parties to improve CTR predictions, highlighting the benefits of secure data collaboration.
June 21st
First Round Submission
June 22nd – June 27th
Optimization and Final Presentation
Milestone: Participants refine their models based on performance metrics (detailed below), focusing on accurate calculation of aggregate statistics, effective training of machine learning models, and proper integration of synthetic data within the Data Clean Room scenario. They will also prepare for final presentations.
Deliverable: Final presentation of the completed model, showcasing its effectiveness and scalability. This includes a detailed demonstration of the model’s ability to calculate aggregate statistics, train machine learning models, and integrate synthetic data within the Data Clean Room to improve CTR predictions, highlighting the benefits of secure data collaboration.
June 27th
Second Round Submission
Day 10 (June 1st)
Evaluation and Awards
Milestone: A panel of judges evaluates the final presentations based on innovation, accuracy, scalability, and adherence to privacy standards.
Deliverable: Announcement of winners and distribution of awards based on the judges’ evaluations.
June 1st 4-6 p.m. PST
Judging Day (on Zoom)
Judges Information
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. Harry Xu, Machine Learning Engineer, Snap
Mr. Ken Lu, Chief Cloud System Architect, Intel
Mr. Shaoqing Yuan, Senior Applied Scientist, Amazon
Prize Pool: $1500
(First Prize:$300, Second Prize:$500, Third Prize:$700)
Additional Benefit: offer summer internship working in the Trustworthy AI lab
Co-Host: Trustworthy AI Lab
The trustworthy AI Lab at UCLA envisions AI 2.0 driven by trustworthiness and built upon generative data. Their research focuses on advancing Generative Data for marketing, healthcare, and finance sectors. They develop data-centric tools such as artificially generated tables and conversations to enable privacy-preserving data sharing and reliable scenario exploration.
Lab Director: Guang Cheng
Guang Cheng is a Professor of Statistics and Data Science and Graduate Vice Chair at UCLA, and leads the Trustworthy AI Lab (https://www.stat.ucla.edu/~guangcheng/). Cheng’s expertise spans a wide spectrum in AI and Machine Learning, attracting top-tier researchers and students to his lab. His impressive alumni network includes Tech Industry such as Deep Mind and Meta and Academia such as Purdue and Michigan State Univ, a testament to his mentorship and leadership in the field. His lab is continuously sponsored by industry and government funds such as National Science Foundation, Meta, JP Morgan Chase and Amazon.
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