About Gock Labs

Gock Labs was founded in late 2025 by a group of researchers and engineers interested in understanding how information is transformed as it moves through modern software systems. We are based in Vancouver, British Columbia, Canada.

Our Mission

Modern applications increasingly rely on automated pipelines to fetch, process, and present information. These pipelines often include multiple stages — fetching raw content, converting formats, summarizing, and filtering — each of which can subtly alter the original information.

We believe that understanding these transformations is critical for building trustworthy systems. Our research aims to make content pipelines more transparent and their outputs more faithful to the source material.

Team

Dr. Solvei Rask — Director & Principal Investigator. Previously at the University of British Columbia's Department of Computer Science, where she studied information retrieval and document summarization. She is particularly interested in how multi-stage processing systems introduce biases that are difficult to detect from the final output alone.

Dr. Mei-Lin Chow — Senior Research Scientist. Joined in January 2026 from the Allen Institute for AI (AI2), where she worked on faithfulness metrics for abstractive summarization. Her current work at Gock Labs focuses on developing automated tools for detecting content drift across processing stages.

Yannick Deschênes — Research Engineer. Background in distributed systems and web infrastructure. Responsible for building the lab's data collection and analysis pipelines.

Sam Okonkwo — Research Assistant. M.Sc. student at Simon Fraser University, co-supervised by Dr. Rask. Studying how formatting and structural changes during content extraction affect downstream comprehension.

Funding

Gock Labs is supported by a seed grant from the Pacific Institute for Digital Trust, a Canadian non-profit focused on fostering trustworthy digital infrastructure. We also receive in-kind support from the UBC Department of Computer Science.

Recognition

Our work on content pipeline transparency has been recognized by the Partnership on AI as a contributing example in their 2026 guidelines on responsible content processing, and was featured in MIT Technology Review's annual AI Index Report as a case study in transparency research methodology.

Contact

We welcome collaboration inquiries from researchers and practitioners working on related problems. Reach us at hello@gocklabs.org or visit us in Vancouver.