TEAM: Rewrite NH's bio in third-person voice

src/team.qmd

@@ -41,7 +41,7 @@ Dr. Jeremy Goecks is Assistant Center Director for Research Informatics and an A
 
 ![](assets/Hejazi_Nima.webp){.left-aligned-img}
 
-My research explores how advances in causal inference, statistical machine learning, and computational statistics can empower discovery in the biomedical and health sciences. I focus primarily on the development of model-agnostic, assumption-lean statistical inference procedures, doing so while emphasizing a science-first, translational philosophy that stresses the rich interplay between the applied sciences and statistical methodology: how emerging questions in the former spur advances in the latter, which, in turn, help to refine scientific discoveries. To accomplish this, my work leverages causal inference as a framework to translate scientific questions into precise, causally interpretable statistical estimands, and then aims to obtain inference about these from data by formulating analytic methods that incorporate flexible, adaptive modeling strategies (i.e., machine learning), to avoid imposing restrictions that may not be justified by domain knowledge, and semi-parametric efficiency theory for best-in-class uncertainty quantification. I am also interested in statistical instrumentation---that is, high-performance computing and open-source software and programming---to push the boundaries of statistical methodology and to promote transparency and reproducibility in the practice of applied statistics and data science.
+Nima Hejazi, PhD, is an assistant professor in the Department of Biostatistics at the Harvard T.H. Chan School of Public Health. His research explores how advances in causal inference, statistical machine learning, and computational statistics can empower discovery in the biomedical and health sciences. Nima focus primarily on the development of model-agnostic, assumption-lean statistical inference procedures, doing so while emphasizing a science-first, translational philosophy that stresses the rich interplay between the applied sciences and statistical methodology: how emerging questions in the former spur advances in the latter, which, in turn, help to refine scientific discoveries. To accomplish this, Nima's work leverages causal inference as a framework to translate scientific questions into precise, causally interpretable statistical estimands, and then aims to obtain inference about these from data by formulating analytic methods that incorporate flexible, adaptive modeling strategies (i.e., machine learning), to avoid imposing restrictions that may not be justified by domain knowledge, and semi-parametric efficiency theory for best-in-class uncertainty quantification. He is also interested in statistical instrumentation---that is, high-performance computing and open-source software and programming---to push the boundaries of statistical methodology and to promote transparency and reproducibility in the practice of applied statistics and data science.
 
 :::