Students presenting research posters in crowded lobby of Hearst Memorial Mining Building at 2024 Data Science Discovery Symposium
Photo / Brandilyn Buckley / Data Science Undergraduate Studies

On May 1st, 2024, the lobby of the Hearst Memorial Mining Building was abuzz with conversation as students unveiled their research projects conducted through the Data Science Discovery Program.

The Data Science Discovery Program, aimed at accelerating scientific research and social impact while providing UC Berkeley students with real-world research opportunities, has successfully engaged thousands of students and forged partnerships with hundreds of research entities over its nine-year tenure. These projects epitomize the extensive applications of data science across diverse fields, ranging from healthcare to public transportation, artificial intelligence to environmental studies.

Shreyes Sridhara, a sophomore whose project was one of two honored with the Best in Show award, reflected on his internship experience at MyMagic AI, facilitated through the program. He emphasized how the experience exposed him to a specialized technical domain he might not have otherwise explored. Sridhara lauded the access to "some of the best GPUs and LLMs" and the chance to engage with cutting-edge technology, expressing his eagerness to continue such work in the future.

First-year Iris Li echoed Sridhara's sentiments, underscoring the value of the technical skills she honed while collaborating with a small team on UI/UX projects. She highlighted the opportunity to enhance her design prowess and showcase creative concepts using Figma.

Beyond just technical skills, sophomore Darlene Navarro emphasized the program's capacity for significant societal impact. Navarro noted, “The most meaningful part of this experience was being able to contribute to a growing problem in society: news and credibility. Many people seek outside sources to gather information and form opinions and thoughts. The fact that I was able to analyze data with other team members really made it a special experience, considering that our analyses will now be used to further grow the Public Editor platform.”

Following the conclusion of the Spring 2024 semester, the Data Science Discovery Program will conclude to pave the way for a new undergraduate research initiative within the College of Computing, Data Science, and Society (CDSS). Set to launch in Fall 2024, this program will extend its reach to all CDSS undergraduate students. Additional details regarding the program will be shared with students, faculty, and partners once finalized.


2024 Data Science Discovery Symposium Award Recipients

2024 Data Science Discovery Program award winners pose in a group holding their posters
Photo / Brandilyn Buckley / Data Science Undergraduate Studies

Data Science Insights Award: Geospatial AI: Mangrove Analysis using Machine Learning

           Team: Pranav Walimbe, Kaito Garcia, Jayden Zheng, Natasha Thussu Dhar, Jaanavi Thanamala

Team Collaboration Award: Empowering Girls in Tech: Mentor Feature Analysis for Team Submission Success

           Team: Avani Gireesha, Ashley Pun

Data Visualization Award: Resilient Energy and Health Response System (REHRS)

           Team: Matthew Haynam, Harnoor Dhillon, Fariha Babar, Sarvagya Somvanshi

Cloud Computing Application Award: GPT Insight Generator for Survey Data

           Team: Jiahao Zhang, Maher Hasan, Tony Chan

Ribbon of Excellence (3 Recipients)

           Arctic Ice Forecasting Dynamics: Ensemble Learning, LSTM & IceNet AI Image Generation

                  Team: Weijie Yang, Chunting Zheng, Derek Yao, Nick Pan

           DS@BART: Training new BART employees THROUGH LLM/rag

                  Team: Esther Shen, Ferril Sucahyono, Joseph Nguyen, Kush Tummala, Michael Yip, Yijun Zhou

           Cross-Regional Lead Service Line Prediction using Machine Learning

                  Team: Akash Iyer, Ariana Siordia, Jasmine Andrade, Phuc Pham, Surya Ramkumar, Yihang Chen

Best in Show Award (2 Recipients)

           Public Editor: Building Misinformation Tools for Newsreaders and Journalists

                   Team: Iris Li, Isabelle Qian, Darlene Navarro, Sidharth Bansal, Audrey Wu,

                   Yinuo Shen, Varsha Chilukuri, Megan Armbrust, Melody Law

           GPU Throughput/Latency Benchmarking Of Quantized LLMs for Batch Inference

                   Team: Shreyes Sridhara


Name or team member corrections may be sent to