Joel Jacinto Ramirez is an associate in the Litigation Department of Morrison & Foerster’s San Francisco office.
Mr. Ramirez is a graduate of Yale Law School, where he was an avid legal research assistant. Having worked for numerous professors, he notably drafted a brief on behalf of plaintiffs in Smith v. Trump (formerly Smith v. Obama) and conducted legal research for an amicus brief on behalf of plaintiffs in Obergefell v. Hodges. Mr. Ramirez was also the author of a research paper analyzing the Supreme Court’s campaign finance decisions and proposing a novel class of campaign finance regulations that circumvent First Amendment strictures on limiting campaign spending. The final paper was named Coalition for Integrity’s winning essay at the 2017 Integrity Awards honoring those committed to combatting corruption in the United States.
While in law school, Mr. Ramirez also gained experience working with both U.S. and foreign government agencies. He served as a legal intern at the U.S. Department of Justice’s Civil Appellate Division and, as a participant in the Yale Law School Governance Innovation Clinic, worked with the Mexican government to draft two briefs proposing policy to combat corruption in the Mexican judicial system.
In addition to his knowledge of the law, Mr. Ramirez holds a B.A. in neuroscience from Johns Hopkins University. Combining his scientific and legal knowledge, he worked as a research assistant at the Johns Hopkins Bloomberg School of Public Health for which he conducted a 50-state survey to assess state statutes and regulations on HIV+-to-HIV+ organ transplantation. He then drafted a legal memorandum to report his findings and to suggest legal advocacy strategies to clinical teams participating in the newly passed HIV Organ Policy Equity (HOPE) Act. He also previously worked as a student fellow at the Global Health Justice Partnership at Yale Law School, where he drafted policy memoranda advocating for increased transparency in medical research and developing strategies for increasing access to clinical drug trial data.
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