Vahid Jalili
About Vahid Jalili
Vahid Jalili is a Computational Scientist II at the Broad Institute of MIT and Harvard, with a background in computational biology and deep learning applications in cancer research. He has held various academic and research positions, including roles at Oregon Health and Science University and Politecnico di Milano.
Current Role at Broad Institute
Vahid Jalili serves as a Computational Scientist II at the Broad Institute of MIT and Harvard. He has held this position since 2021, contributing to various computational biology projects. His work focuses on enhancing computational methods and frameworks that support biomedical research.
Previous Experience in Computational Biology
Prior to his current role, Vahid Jalili worked at Oregon Health and Science University as a Computational Biologist from 2019 to 2021. He also served as a Postdoctoral Scholar at the same institution from 2017 to 2018. His experience includes a role as a Visiting Researcher at The Johns Hopkins University in 2016 and as a Postdoctoral Researcher at The George Washington University for three months in 2016.
Education and Academic Background
Vahid Jalili completed his Doctor of Philosophy (PhD) in Computer Engineering at Politecnico di Milano from 2012 to 2015. He also worked as a Research Associate at Politecnico di Milano in 2016. His academic background provides a strong foundation for his research interests in computational biology and deep learning.
Contributions to the Galaxy Project
Vahid Jalili contributed to the Galaxy project, focusing on enhancing user authentication and authorization delegation for protected data cohorts. He also worked on extending the Galaxy framework to support storage and compute resource federation, as well as deploying Galaxy instances both on-premise and in the cloud.
Research Interests in Cancer Studies
Vahid Jalili's research interests include the application of deep learning techniques to cancer studies. His work aims to leverage computational methods to advance understanding and treatment of cancer, aligning with his expertise in computational biology.