Eduardo Santacana

Eduardo is a skilled and strategic litigator who leverages a broad base of experience to deliver results in complex, high-stakes disputes. He represents a diverse array of clients, including technology companies, class action defendants and businesses of all sizes. His distinctive blend of experience makes him uniquely situated to counsel clients through their toughest decisions and most consequential challenges.

Eduardo has played key roles on trial teams in major commercial disputes, securing favorable outcomes in consumer fraud class actions, contract litigation and fraud-related claims. He also has significant experience in intellectual property litigation, having defended innovative companies in patent and copyright matters, including obtaining complete dismissal of a copyright suit against a video game developer and defending medical device companies in competitor-driven patent cases.

In addition to his commercial practice, Eduardo is deeply committed to pro bono advocacy. He has successfully represented asylum seekers and immigration clients, secured justice for a San Francisco prisoner subjected to excessive force, and served as counsel in a landmark class action challenging the Trump administration’s 2017 travel ban and on behalf of the San Francisco Unified School District challenging an agency order seeking to withhold federal funding pursuant to President Donald Trump’s 2025 anti-diversity, equity and inclusion executive order.

Eduardo’s public service includes his prior role as a commissioner on the San Francisco Board of Appeals, a quasi-judicial body that reviews city permitting and enforcement decisions – experience that deepens his understanding of administrative law and public governance.

Eduardo brings a thoughtful, client-centered approach to every matter, combining sharp legal analysis with a strong sense of purpose and advocacy.

Selected publications and lectures

Co-author, “Beyond Fair Use: Legal Risk Evaluation for Training LLMs on Copyrighted Text,” International Conference on Machine Learning, July 29, 2023