Asad Lodhia
About Asad Lodhia
Asad Lodhia is a researcher currently at the Broad Institute of MIT and Harvard, focusing on graphical models for causal inference. He has a strong academic background with degrees in Physics and Applied Mathematics from the University of California, Davis, and a PhD in Mathematics from the Massachusetts Institute of Technology.
Work at Broad Institute
Asad Lodhia has been working at the Broad Institute of MIT and Harvard as a Researcher since 2021. His role involves conducting research within the Eric and Wendy Schmidt Center, where he focuses on graphical models for causal inference. This position allows him to apply his expertise in probabilistic modeling and data analysis to advance research initiatives at the institute.
Previous Experience at University of Michigan
Prior to his current role, Asad Lodhia served as an NSF RTG Postdoctoral Researcher at the University of Michigan from 2017 to 2020. During his three years in Ann Arbor, Michigan, he contributed to various research projects and gained valuable experience in the academic research environment.
Education and Expertise
Asad Lodhia holds a Bachelor of Science in Physics and a Bachelor of Science in Applied Mathematics from the University of California, Davis, where he studied from 2007 to 2012. He furthered his education at the Massachusetts Institute of Technology, earning a Doctor of Philosophy in Mathematics from 2012 to 2017. His academic background provides a strong foundation for his research interests, including robust de-noising, community detection for fMRI analysis, and time series analysis in high-dimensional settings.
Research Focus and Skills
Asad Lodhia has a significant research focus on robust de-noising and community detection techniques, particularly in the context of fMRI analysis. He possesses an extensive understanding of probabilistic modeling and has practical experience with Python for data analysis. His doctoral research at MIT involved studying estimators for independence testing in high-dimensional settings, which complements his current work in causal inference.