معرفی
Michael Schatz is the Bloomberg Distinguished Professor of Computational Biology and Oncology at Johns Hopkins University, with joint appointments in the Department of Computer Science at the Whiting School of Engineering and the Department of Biology at the Krieger School of Arts and Sciences. He also serves as a member of the Cancer Prevention and Control Program at Johns Hopkins' Sidney Kimmel Comprehensive Cancer Center and maintains an adjunct position at Cold Spring Harbor Laboratory.
Dr. Schatz's research focuses on computational biology and genomics, with particular expertise in DNA sequencing analysis and scalable computing solutions for genomic data. His work spans medical applications for understanding autism spectrum disorders and cancer, as well as agricultural applications for crop improvement. He founded and directs the Schatz Lab, which has developed numerous widely-used computational tools including NGMLR, Sniffles, Scalpel, GECCO, Ginkgo, FALCON, Assemblytics, CloudBurst, and Crossbow.
His recent work has made significant contributions to understanding structural variations in cancer genomes, analyzing South Asian genomic diversity, and identifying genes responsible for size variations in nightshade plants like tomatoes and eggplants. Dr. Schatz has pioneered the use of cloud computing in genomics and remains at the forefront of developing algorithms for large-scale biological sequence analysis.
- Alfred P. Sloan Foundation Fellowship (2015)
- NSF CAREER Award (2014)
- Genome Technology's Young Investigator of the Year (2010)
- Winship Herr Award for Excellence in Teaching (twice)
- TIME100 recipient (2022)
Dr. Schatz actively advises PhD students including Arun Das (recently defended) and Mahler Revsine (NSF GRFP fellow). He serves on editorial boards for Genome Biology, GigaScience, and Cell Systems, and regularly participates in major genomics conferences including the Cold Spring Harbor Laboratory meetings. His lab continues to develop innovative computational approaches at the intersection of biotechnology and algorithmics, with applications spanning human health, agriculture, and evolutionary biology.
