School of Medicine
Showing 1-4 of 4 Results
-
Roxana Daneshjou, MD, PhD
Clinical Scholar, Dermatology
Postdoctoral Scholar, Biomedical Data SciencesBioI am interested in bridging new technologies such as genomics and machine learning with clinical medicine. I am also interested in the use of Twitter for scientific communication and medical education. I am on Twitter: @RoxanaDaneshjou.
-
Francisco M. De La Vega
Adjunct Professor, Biomedical Data Science
BioProf. Francisco De La Vega is a geneticist, computational biologist and experienced technical executive of the life sciences industry, having spent over a decade at Applied Biosystems/Life Technologies developing several successful genetic analysis products, and more recently contributing to technology start-up companies focused on bringing genome sequencing into the clinic. He has participated in several breakthrough international projects such as the 1000 Genomes Project, the Genome-in-a-Bottle Consortium, and the International Cancer Genome Consortium. Francisco has co-authored more than 100 scientific publications, including papers in top journals such as Nature, Nature Genetics, Science, Genome Research and others, which have received over 20,000 citations. Currently he is Chief Scientific Officer and Senior Vice President of Research and Development at Fabric Genomics, an Oakland-based privately held company that develops an Artificial Intelligence-driven software-as- a-service platform for genomic interpretation and clinical reporting from genomes, exomes, and gene panels.
-
Manisha Desai (She/Her/Hers)
Kim and Ping Li Professor, Professor (Research) of Medicine (Quantitative Sciences Unit), of Biomedical Data Science and, by courtesy, of Epidemiology and Population Health
Current Research and Scholarly InterestsDr. Desai is the Director of the Quantitative Sciences Unit. She is interested in the application of biostatistical methods to all areas of medicine including oncology, nephrology, and endocrinology. She works on methods for the analysis of epidemiologic studies, clinical trials, and studies with missing observations.
