The Department of Biostatistics is excited to announce our "Alumni of the Month" brown bag featuring 2005 graduate, Mingyao Li, PhD. From Dr. Li: I was trained as a statistical geneticist at the University of Michigan, but since I joined the faculty at the University of Pennsylvania, I have gradually transitioned my research from traditional statistical genetics to statistical genomics with the goal of having a deeper understanding of the molecular mechanism of human diseases. The central theme of my current research is to use statistical and machine learning methods to understand cellular heterogeneity in human-disease-relevant tissues, to characterize gene expression diversity across cell types, to study the patterns of cell state transition and crosstalk of various cells using data generated from single-cell and spatial transcriptomics studies, and to translate these findings into the clinics. In addition to methods development, I am also interested in collaborating with researchers seeking to identify complex disease susceptibility genes and acting cell types. At UPenn, I serve as the Director of Biostatistics for the Gene Therapy Program, where I advise biostatistics and bioinformatics analysis for various gene therapy studies. I also chair the Graduate Program in Biostatistics, and I have found the interactions with graduate students to be very rewarding. Talk title: Applications of statistical and machine learning methods in spatial transcriptomics.
Passcode: 705190
Mandi Larson, larsoama@umich.eduApplications of Statistical and Machine Learning Methods in Spatial Transcriptomics
Mingyao Li, PhD
August 19, 2021
12:00 pm - 1:00 pm
Online in Zoom
Contact Information: Mandi Larson, larsoama@umich.edu
The Department of Biostatistics is excited to announce our "Alumni of the Month" brown bag featuring 2005 graduate, Mingyao Li, PhD. From Dr. Li: I was trained as a statistical geneticist at the University of Michigan, but since I joined the faculty at the University of Pennsylvania, I have gradually transitioned my research from traditional statistical genetics to statistical genomics with the goal of having a deeper understanding of the molecular mechanism of human diseases. The central theme of my current research is to use statistical and machine learning methods to understand cellular heterogeneity in human-disease-relevant tissues, to characterize gene expression diversity across cell types, to study the patterns of cell state transition and crosstalk of various cells using data generated from single-cell and spatial transcriptomics studies, and to translate these findings into the clinics. In addition to methods development, I am also interested in collaborating with researchers seeking to identify complex disease susceptibility genes and acting cell types. At UPenn, I serve as the Director of Biostatistics for the Gene Therapy Program, where I advise biostatistics and bioinformatics analysis for various gene therapy studies. I also chair the Graduate Program in Biostatistics, and I have found the interactions with graduate students to be very rewarding. Talk title: Applications of statistical and machine learning methods in spatial transcriptomics.
Passcode: 705190