School of Medicine
Showing 1-6 of 6 Results
Clinical Scholar, Dermatology
Bio I 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
Bio Prof. 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.
Professor (Research) of Medicine (Biomedical Informatics), of Biomedical Data Science and, by courtesy, of Epidemiology and Population Health
Current Research and Scholarly Interests Dr. 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.
Postdoctoral Research Fellow, Biomedical Data Sciences
Bio My research objectives are focused on the development of artificial intelligence technologies for neurology research. My graduate training revolved around medical engineering and offered me a multidisciplinary advanced education in computer science, physics, mathematics, biology, and chemistry. As I was progressing towards the start of my PhD, I decided to develop my expertise in machine learning? a type of artificial intelligence?and neurology, working for example on the automatic classification of fMRI signals of the auditory cortex under the supervision of Dr. Takerkart during my studies in Centrale Marseille, France. In Germany, I strengthened my expertise in machine learning in Prof. Navab's chair and developed and published an automated method for the segmentation of medical images based on Markov Chain Monte Carlo. During my PhD in the Netherlands, I focused on deep learning and neurology and developed methods for weakly supervised learning, regression neural networks, and brain lesion detection and quantification from MRI. One of my major contribution is my work on the automated quantification and detection of enlarged perivascular spaces?a type of brain lesion related to cerebral small vessel disease. During my PhD, I visited Prof. Rost group at MGH, Harvard Medical School, to strengthen my expertise in neurology research, and developed and published deep learning registration methods for clinical brain MRI. I am now doing my postdoctoral training in Prof. Daniel Rubin's group at Stanford with the additional supervision of the neurologist Prof. Lee-Messer. I am developing deep learning methods to detect and predict seizures from EEG and video recordings of epileptic patients.