Short Bio to come soon.

Lisa is responsible for the development and implementation of new PheWAS and phenotyping methods. She has training in computer science and computational linguistics, over 10 years of programming experience, and a degree in mathematics from the University of Chicago. 

Professor Priscilla Chan and Mark Zuckerberg Distinguished Professor UCSF School of Medicine Pediatrics

Atul Butte, MD, PhD is the Priscilla Chan and Mark Zuckerberg Distinguished Professor and inaugural Director of the Institute for Computational Health Sciences (ichs.ucsf.edu) at the University of California, San Francisco (UCSF). Dr. Butte is also the Executive Director for Clinical Informatics across the six University of California Medical Schools and Medical Centers. Dr. Butte has authored over 200 publications, with research repeatedly featured in the New York Times, Wall Street Journal, and Wired Magazine. Dr. Butte was elected into the National Academy of Medicine in 2015, and in 2013, he was recognized by the Obama Administration as an Open Science Champion of Change for promoting science through publicly available data. Dr. Butte is also a founder of three investor-backed data-driven companies: Personalis, providing medical genome sequencing services, Carmenta (acquired by Progenity), discovering diagnostics for pregnancy complications, and NuMedii, finding new uses for drugs through open molecular data. Dr. Butte is a principal investigator of three major programs: the California Initiative to Advance Precision Medicine; ImmPort, the clinical and molecular data repository for the National Institute of Allergy and Infectious Diseases; and the California Precision Medicine Consortium, helping recruit tens of thousands of participants into President Obama's Precision Medicine Initiative. Dr. Butte trained in Computer Science at Brown University, worked as a software engineer at Apple and Microsoft, received his MD at Brown University, trained in Pediatrics and Pediatric Endocrinology at Children's Hospital Boston, then received his PhD from Harvard Medical School and MIT.
 

My vision is to fundamentally alter the fabric of biomedical science, utilizing my art as a data translator to weave together healthcare systems, basic science research, and patients; through development of data integration technologies, innovative communication strategies, and collaborative education and outreach.

My demonstrated success in leadership of cross- ­disciplinary international teams, development of applications used for rare disease diagnostics, implementation of platforms and tools for translational research, and open and reproducible science will serve me and the community at large to effect real change.

Faculty Pending, Department of Genetics
Director, Center for Translational Bioinformatics, Institute for Biomedical Informatics (IBI),
Associate Director for Bioinformatics, Institute for Biomedical Informatics (IBI),
Associate Director, Center for Precision Medicine

University of Pennsylvania

Dr. Marylyn Ritchie, PhD is a new faculty member in the Department of Genetics at the University of Pennsylvania, Perelman School of Medicine.  She is also the Associate Director of Bioinformatics and the Director of a new Center for Translational Bioinformatics at Penn.

Dr. Ritchie is a statistical and computational geneticist with a focus on understanding genetic architecture of complex human disease.  She has expertise in developing novel bioinformatics tools for complex analysis of big data in genetics, genomics, and clinical databases, in particular in the area of Pharmacogenomics.  Dr. Ritchie has received several awards and honors including selection as a Genome Technology Rising Young Investigator in 2006, an Alfred P. Sloan Research Fellow in 2010, a KAVLI Frontiers of Science fellow by the National Academy of Science from 2011-2014, and she was named one of the most highly cited researchers in her field by Thomas Reuters in 2014.  Dr. Ritchie has extensive experience in all aspects of genetic epidemiology and translational bioinformatics as it relates to human genomics.  She also has extensive expertise in dealing with big data and complex analysis including GWAS, next-generation sequencing, data integration of meta-dimensional omics data, Phenome-wide Association Studies (PheWAS), and development of data visualization approaches.

Brian Shirts M.D., Ph.D.

Assistant Professor

Department of Laboratory Medicine

University of Washington Medical Center

Clinical Positions

Associate Director, Genetics Division, Laboratory Medicine

Assistant Director, Informatics Division, Laboratory Medicine

My research focuses on integrating complex genetics knowledge into clinical care. There are several related aspects of this research:

1. Research on clinical classification of rare variants of uncertain significance, particularly variants in familial cancer genes.

We are developing optimal family-based methods to classify variants of uncertian significance with small to medium sized families.

We are investigating practical and ethical issues surrounding patient-driven pedigree building for variant classification.

We have launched an online guide to help patients and genetic counselors understand family analysis for variant classification: findmyvariant.org

2. Research on the storage and communication of genetic information in the health care setting. This includes formatting genetic test results so that they are searchable in clinical laboratory information systems and so that electronic health records can use genetics to provide active decision support.

3. Research on improving the use of complex and multifactorial clinical information, with special interest in personalized healthcare using genetic information.

We are developing statistical methods to integrate quantitative diagnostic information with genetic information.

We are investigating methods to identify when genetic information is clinically useful and when it may not be clinically useful despite clear biological associations.

In related clinical projects we have worked to evaluate laboratory test ordering patterns, to develop content for online decision support tools, and to implement complex and interactive test reports.