UConn Researcher Wins Competitive Global Interstellar Initiative Alumni Program

Paola Vera-Licona, Ph.D., assistant professor at the Center for Quantitative Medicine at UConn School of Medicine, is a 2022 recipient of the Interstellar Initiative Alumni Program of the New York Academy of Sciences and the Japan Research Agency and medical development.

The Interstellar Initiative brings together early career researchers from around the world – selected through a competitive application process – and groups them into interdisciplinary, collaborative teams. Under the guidance of eminent senior scientists, each team develops a grant proposal centered on a new scientific research question.

The Interstellar Initiative Alumni Program brings together those who have participated in previous cycles of the Interstellar Initiative to support the development and strengthening of research teams and projects in pursuit of international awards; share best practices and science developed through the program; and provide ongoing networking and mentorship opportunities for Interstellar Initiative alumni.

Vera-Licona and collaborators Adrian Teo Kee Keong from A*STAR in Singapore, Ayesha Saleem from the University of Manitoba in Canada and Shintaro Yamada from Kyoto University in Japan won first place for their research project in the healthy longevity field titled “Rescuing cellular alterations in aged pancreatic beta cells to restore insulin secretory function.

“Congratulations to Paola for this well-deserved honor,” shared Pedro Mendes, Ph.D. professor and director of the Richard D. Berlin Center for Cell Analysis and Modeling and the Center for Quantitative Medicine at UConn School of Medicine.

Vera-Licona’s laboratory research focuses on computational systems biology and systems medicine, mathematical biology, and bioinformatics. Application areas include cancer and immunology. His research team works on the design, software development and application of mathematical algorithms to model, simulate and control gene regulatory networks and intracellular signaling networks.

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