Madrona Awards 2022 Madrona Prize to UW Video Projects that Make Video Content Searchable Without Trained Models

Rhea-AI Summary

Madrona awarded the 15th Annual Madrona Prize to the University of Washington’s Paul G. Allen School for their innovative VOCAL system, which extracts semantic information from unlabeled data. Ph.D. students Maureen Daum, Enhao Zhang, and Dong He developed sub-projects that won the prize. Runners-up included ClearBuds for real-time speech enhancement and tools for non-experts to create statistical models. Madrona has funded 21 companies from the University, including Impinj (NASDAQ: PI) and A-Alpha Bio, demonstrating a strong commitment to fostering technology innovation in the region.

Positive

• Awarded 15th Annual Madrona Prize to a promising technology project, VOCAL, indicating strong research and commercial potential.
• Madrona has funded 21 companies from the University of Washington, including notable mentions like Impinj (NASDAQ: PI), showcasing a successful investment track record.

Negative

• None.

SEATTLE--(BUSINESS WIRE)-- Madrona awarded the 15^th Annual Madrona Prize last night to the University of Washington’s Paul G. Allen School of Computer Science & Engineering team working on the VOCAL system. VOCAL (Video Organization and Interactive Compositional AnaLytics) system enables users to extract semantic information from large quantities of unlabeled data without pre-trained models. VOCAL helps users organize their data, build domain-specific models as they go, and then search for interesting events. Ph.D. students Maureen Daum, Enhao Zhang, and Dong He worked together on the two VOCAL sub-projects that received the prize: Video Organization and Exploration and video search technology called Interactive Video Analytics for Compositional Queries. They were co-advised by Professor and Director Magda Balazinsk and Professor Ranjay Krishna, and they also worked with a collaborator (and Allen School graduate) from Microsoft, Brandon Haynes.

Madrona always awards the Madrona Prize at the end of the Allen School’s annual Industry Affiliates Research Day for projects that combine excellent research with strong commercial potential. Finding it very hard to choose, Madrona always selects several runners-up. In 2022 they are:

ClearBuds: Wireless Binaural Earbuds for Machine Learning Based Speech Enhancement
Hardware and software system for real-time speech enhancement. The ClearBuds neural network runs completely on an iPhone, suppressing unwanted noises to a far greater extent than the current state-of-the-art.
Ishan Chatterjee, Maruchi Kim, Vivek Jayaram More info
Advisers: Shyam Gollakota, Ira Kemelmacher-Shlizerman, Shwetak Patel, Steven M. Seitz

Data Analysis Systems for Statistical Non-Experts
Enable non-experts to generate accurate statistical models via an interactive no-code process.
Eunice Jun More info
Advisers: Jeffrey Heer and Rene Just

Levity: Contactless Robotics and Automation for Synthetic Biology
Suspend and manipulate millimeter-scale objects using ultrasonic levitation.
Jared Nakahara More info
Adviser: Joshua R. Smith

The Industrial Affiliates event includes technical talks throughout the day and culminates in an open house and poster session to showcase the latest research projects and papers faculty and students are pursuing. Since Madrona’s inception more than two decades ago, Madrona has funded 21 companies out of the University of Washington. These companies include Impinj (NASDAQ: PI), SkyTap and Turi (acquired by Apple), and more recently, A-Alpha Bio, Modulus Therapeutics, and OctoML, a company based on the TVM research project that won a Madrona Prize in 2017 and has raised $132 million in venture capital.

“The University of Washington and the Allen School specifically has grown to be not just a source of incredible innovation for our region but one of the top universities in the world,” said Madrona Managing Director Tim Porter. “Being able to meet and talk to students who are passionate about their work and have put years into refining their innovations is inspiring. Awarding the Madrona Prize is something we look forward to every year, and congrats to all the students who came to the event with their poster and their stories.”

Just this past month, the University of Washington was named to the US News and World Report’s List of the Top Global Universities, ranking it 6^th among names such as Harvard and Oxford.

For past winners, visit: https://www.cs.washington.edu/industrial_affiliates/madrona

About Madrona

Madrona (www.madrona.com) is a venture capital firm based in Seattle, Washington. With more than 25 years of investing in early-stage technology companies, the firm has worked with founders from Day One to help build their company for the long run. Madrona initially invests in seed and Series A rounds across the information technology spectrum and in acceleration stage (Series B and C) companies building intelligent applications. Madrona manages $3 billion and was an early investor in companies such as Amazon, Smartsheet, Snowflake, Apptio, Redfin, and UI Path.

View source version on businesswire.com: https://www.businesswire.com/news/home/20221116006034/en/

Erika Shaffer, erika@madrona.com 206-972-5514

Source: Madrona

FAQ

What is the Madrona Prize awarded for?

The Madrona Prize is awarded for projects that combine excellent research with strong commercial potential, presented during the Allen School’s annual Industry Affiliates Research Day.

Which project won the Madrona Prize in 2022?

The 2022 Madrona Prize was awarded to the VOCAL system developed by the University of Washington’s Paul G. Allen School.

Who were the winners of the Madrona Prize in 2022?

The winners included Ph.D. students Maureen Daum, Enhao Zhang, and Dong He for their work on the VOCAL system.

Which companies has Madrona funded?

Madrona has funded 21 companies from the University of Washington, including Impinj (NASDAQ: PI), SkyTap, and OctoML.

What technologies were runners-up for the Madrona Prize?

Runners-up included ClearBuds for speech enhancement and systems for statistical analysis for non-experts.