Congratulations to the 2026 Analytics Lab Showcase Winning Teams
The Berkeley Analytics Lab Showcase 2026 brought together an inspiring range of student-led analytics projects spanning healthcare, sustainability, finance, AI, retail, transportation, and more. Held on May 1, the annual showcase celebrated the hands-on work of Master of Analytics students, highlighting how data-driven solutions can tackle real-world challenges across industries.
Students presented interactive demos, posters, and live applications while networking with a distinguished panel of industry judges, many of whom are alumni of the program.
We are excited to congratulate the winners of this year’s showcase:
🥇 First Place — Group 15: Guia: A Guide to Museums and Their Reserves
Team: JP Schuchter, Jasmine Guan, Bainuo Bao, Hanning Lin, Lanxi Liu, Yuqian Tao, Caiyuan Yin
Description: Major museums usually display only a fraction of their collections — often less than 10% — leaving the majority in storage, away from the public. GUIA is an app designed to connect visitors to these hidden works through three integrated experiences: visitors describe their interests in natural language and receive a personalized walking route on an interactive floor plan of the museum; they can input the code of any piece that catches their eye to instantly see similar works from the collection, including pieces tucked away in storage; and curators get a dashboard showing which stored works visitors gravitate toward, plus a search tool to browse the reserves by describing ideas for a show. We chose the MET as our initial focus because of its vast collection, the breadth of its open-access metadata, and the availability of images for most pieces — including those in storage. Under the hood, GUIA is powered by a hybrid recommendation model that fuses CLIP visual embeddings — fine-tuned on museum-specific image-text pairs so it learns the language of culture, period, and technique — with machine-learning similarity measures over the MET’s structured metadata, unifying how an artwork looks and how it’s catalogued into a single searchable space. The result: visitors can query hundreds of thousands of artworks using nothing but a natural-language phrase or a mood, and walk away having seen art the museum itself rarely gets to show.
🥈 Second Place — Group 8: Plastic Hunters
Team: Aivan Durfee, Alex Yu, Bonnie Lu, Elena Chen, Evelyn Yeh, Jun Li, Yiran Ding
Description: Plastic pollution in the Pacific Ocean is constantly moving, which makes cleanup planning difficult under limited fuel and mission time. In Plastic Hunter, we combine river-based plastic emission hotspots (Meijer) with ocean current data (HYCOM) to simulate debris drift and forecast where plastic is likely to concentrate. We convert simulated particle trajectories into plastic density maps so the system can identify high-value cleanup zones. Using these forecasts, we run a fuel-aware routing optimizer that recommends routes for cleanup vessel(s) under operational constraints. In our demo scenario, the optimized routing recovers 2.5–3.4× more plastic than simple baseline strategies under the same budget. We package the workflow into an interactive dashboard where users can set mission inputs (e.g., departure port and date, trip duration, and number of vessels) and immediately compare route recommendations and expected recovery.
🥉 Third Place — Group 10: WearMe
Team: Tiancheng Guo, Ray Zhang, Mel Zhao, Yixing Ma, RanXin Deng, Catherine Pang, Jingrong Yan
Description: Imagine being able to try on any outfit — without ever stepping into a fitting room. This project builds an end-to-end AI pipeline that lets users upload a photo of themselves and a piece of clothing, then see a strikingly realistic virtual try-on: the right fit, the right texture, the right drape — as if they actually wore it. But it doesn’t stop at one photo. The system generates multi-angle views — front, side, close-up — and even places the outfit into real-life motion scenarios like a commute, a stage performance, or a dance, so shoppers see not just what they’d wear, but how they’d live in it. Over time, every try-on result gets saved into a personal digital wardrobe, turning a one-off experiment into a growing, reusable fashion asset. For brands, it means transforming static product images into compelling visual content; for users, it means making smarter style decisions — faster, and without the guesswork.
Congratulations to all of the students who participated in this year’s showcase. The creativity, technical skill, and collaboration demonstrated across every project reflected the innovative spirit of the UC Berkeley Master of Analytics Program community.
Thank you as well to our judges, faculty, alumni, and guests who helped make this year’s event such a success.