Data Analyst Cecilia Huang Announces Retirement after more than 20 Years at Rutgers

IFH congratulates Cecilia Huang on her retirement after 22 years of service to Rutgers University and the Institute for Health, Health Care Policy and Aging Research. Over her tenure, she contributed her expertise across multiple IFH centers, including the Center for State Health Policy, Center for Health Services Research, and, most recently, the Center for Pharmacoepidemiology and Treatment Science.

Cecilia’s remarkable contributions, focused on data management and statistical analyses of major claims databases, have supported a variety of research studies on mental health, drug safety, aging, and health disparities. Her expertise and mentorship have made a lasting impact on the Rutgers research community.

Tobias Gerhard, Director of the Institute, said, “I was fortunate to work closely with Cecilia for more than 15 years. Her remarkable skillset, sharp thinking, and attention to detail will be greatly missed, not only by me, but by many colleagues at Rutgers and our research collaborators across the country. She was a wonderful colleague, mentor to many students and data analysts, and a true role model at the Institute.”

Joel Cantor, Director of the Center for State Health Policy, said, “Cecilia was the go-to member of the CSHP data analysis team as we were building our capacity to meet the New Jersey Medicaid agency’s analytic needs. She helped mentor our junior analysts, one of whom now leads our data team and another who went on to pursue her doctorate and has joined the Rutgers faculty. Cecilia’s legacy can be seen in our continued productive partnership with the state and in the people she helped to mentor.”

Stephen Crystal, director of the Center for Health Services Research, said, “Cecilia Huang will truly be missed as a member of the IFH family! Her skilled, meticulous and thoughtful approach to analyzing complex datasets has been invaluable to so many studies at IFH. Her sharp eyes and systematic approach have been invaluable in identifying and solving many challenges with these large and complex datasets, assuring that inferential issues like variable completeness and coding differences are carefully addressed. She has truly been an integral part of so many investigative teams, contributing her well-informed insights and assuring that analyses are rigorous.”