Trustworthy Machine Learning for Biomedicine
Speaker: Prof. Xinghua Mindy Shi
DEIB - BIO1 Room (Bld. 21)
June 27th, 2025 | 11.30 am
Contact: Prof. Fabrizio Pittorino
DEIB - BIO1 Room (Bld. 21)
June 27th, 2025 | 11.30 am
Contact: Prof. Fabrizio Pittorino
Abstract
On June 27th, 2025 at 11.30 am the seminar titled "Trustworthy Machine Learning for Biomedicine" will take place at DEIB BIO1 Room (Building 21).
Recent biomedical data deluge has fundamentally transformed biomedical research into a data science frontier. The unprecedented accumulation of biomedical data presents a unique yet challenging opportunity to develop novel methods leveraging artificial intelligence (AI) and machine learning (ML) to further our understanding of biology and advance medicine.
In this talk, I will first introduce the cutting-edge research in characterizing human genetic variation and their associations with health and disease. I will then present generative and predictive AI/ML methods for robust modeling of medical data. Finally, I will overview recent development in trustworthy AI/ML to combat model overfitting, privacy and biases in biomedicine.
Recent biomedical data deluge has fundamentally transformed biomedical research into a data science frontier. The unprecedented accumulation of biomedical data presents a unique yet challenging opportunity to develop novel methods leveraging artificial intelligence (AI) and machine learning (ML) to further our understanding of biology and advance medicine.
In this talk, I will first introduce the cutting-edge research in characterizing human genetic variation and their associations with health and disease. I will then present generative and predictive AI/ML methods for robust modeling of medical data. Finally, I will overview recent development in trustworthy AI/ML to combat model overfitting, privacy and biases in biomedicine.
Short Bio
Xinghua Mindy Shi is an associate professor at the Department of Computer and Information Sciences and a core faculty member at the Institute for Genomics and Evolutionary Medicine at Temple University. Before that she was an assistant professor at the Department of Bioinformatics and Genomics in the University of North Carolina at Charlotte. She received her Ph.D. and M.S. degrees in Computer Science from the University of Chicago, and postdoctoral training at Brigham and Women’s Hospital, Harvard Medical School, and Broad Institute.
With research interests across the intersection of computer science and biomedical data science, her group is focused on developing statistical and machine learning tools to solve large-scale computational problems in biology and medicine. She co-leads the Functional Analysis Group of the Human Genome Structural Variation Consortium. Her research has been supported by NSF, NIH, DARPA, and Wells Fargo Foundation. She is a recipient of the NSF CAREER award and a distinguished member of the Association for Computing Machinery (ACM).
With research interests across the intersection of computer science and biomedical data science, her group is focused on developing statistical and machine learning tools to solve large-scale computational problems in biology and medicine. She co-leads the Functional Analysis Group of the Human Genome Structural Variation Consortium. Her research has been supported by NSF, NIH, DARPA, and Wells Fargo Foundation. She is a recipient of the NSF CAREER award and a distinguished member of the Association for Computing Machinery (ACM).
