Computational Advisory Board

Atul Butte

Atul Butte, MD, PhD is Chief of the Division of Systems Medicine and Associate Professor of Pediatrics, Medicine, and by courtesy, Computer Science, at Stanford University and Lucile Packard Children’s Hospital. 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 in Health Sciences and Technology from Harvard Medical School and Massachusetts Institute of Technology (MIT).

The Butte Laboratory at Stanford builds and applies tools that convert more than 300 billion points of molecular, clinical, and epidemiological data measured by researchers and clinicians over the past decade into diagnostics, therapeutics, and new insights into disease. The Butte Laboratory currently has been funded by the Howard Hughes Medical Institute (HHMI) and under 15 National Institutes of Health (NIH) grants. The Butte Lab has developed bioinformatics methods to take genomic, genetic, and phenotypic data from multiple sources and diseases and reason over these data to create novel diagnostics, therapeutics, and discover novel molecular mechanisms of disease. Examples of this method includes work on cancer drug discovery published in the Proceedings of the National Academy of Science (2000), on type 2 diabetes published in the Proceedings of the National Academy of Science (2003), on fat cell formation published in Nature Cell Biology (2005), on obesity in Bioinformatics (2007), and in transplantation published in Proceedings of the National Academy of Science (2009). To facilitate this, the Butte Lab has developed tools to automatically index and find genomic data sets based on the phenotypic and contextual details of each experiment, published in Nature Biotechnology (2006), to remap microarray data, published in Nature Methods (2007), and to deconvolve multicellular samples, published in Nature Methods (2010). The Butte Lab has also been developing novel methods in comparing clinical data from electronic health record systems with gene expression data, as described in Science (2008), and was part of the team performing the first clinical annotation of a patient presenting with a whole genome, as described in the Lancet (2010). The laboratory currently has been funded by HHMI and under sixteen NIH grants.

Dr. Butte has authored more than 100 publications and delivered more than 120 invited presentations in personalized and systems medicine, biomedical informatics, and molecular diabetes, including 20 at the NIH or NIH-related meetings. Dr. Butte’s research has been featured in the New York Times Science Times and the International Herald Tribune (2008), Wall Street Journal (2010 and 2011), and San Jose Mercury News (2010). Dr. Butte’s recent awards include the 2010 Society for Pediatric Research Young Investigator Award, induction into the American College of Medical Informatics in 2009, the 2008 AMIA New Investigator Award, the 2007 Genome Technology “Tomorrow’s Principal Investigator” Award, the 2007 Society for Medical Decision Making Award for Outstanding Short Course, the 2006 Howard Hughes Medical Institute Early Career Award, the 2006 PhRMA Foundation Research Starter Grant in Informatics, and the 2002 and 2003 American Association for Clinical Chemistry Outstanding Speaker Award. Dr. Butte also coauthored one of the first books on microarray analysis titled “Microarrays for an Integrative Genomics” published by MIT Press.

 

David Haussler

David Haussler’s research lies at the interface of mathematics, computer science, and molecular biology. He develops new statistical and algorithmic methods to explore the molecular function and evolution of the human genome, integrating cross-species comparative and high-throughput genomics data to study gene structure, function, and regulation. He is credited with pioneering the use of hidden Markov models (HMMs), stochastic context-free grammars, and the discriminative kernel method for analyzing DNA, RNA, and protein sequences. He was the first to apply the latter methods to the genome-wide search for gene expression biomarkers in cancer, now a major effort of his laboratory.

As a collaborator on the international Human Genome Project, his team posted the first publicly available computational assembly of the human genome sequence on the Internet on July 7, 2000. Following this, his team developed the University of California, Santa Cruz (UCSC) Genome Browser, a web-based tool that is used extensively in biomedical research and serves as the platform for several large-scale genomics projects, including National Human Genome Research Institute’s (NHGRI’s) ENCODE project to use omics methods to explore the function of every base in the human genome (for which UCSC serves as the Data Coordination Center), NIH’s Mammalian Gene Collection, NHGRI’s 1000 genomes project to explore human genetic variation, and the National Cancer Institutes’ (NCIs’) Cancer Genome Atlas project to explore the genomic changes in cancer.

His group’s informatics work on cancer genomics, including the UCSC Cancer Genomics Browser, provides a complete analysis pipeline from raw DNA reads through the detection and interpretation of mutations and altered gene expression in tumor samples. His group collaborates with researchers at medical centers nationally, including members of the Stand Up To Cancer “Dream Teams” and the Cancer Genome Atlas, to discover molecular causes of cancer and pioneer a new personalized, genomics-based approach to cancer treatment. He cofounded the Genome 10K Project to assemble a genomic zoo—a collection of DNA sequences representing the genomes of 10,000 vertebrate species—to capture genetic diversity as a resource for the life sciences and for worldwide conservation efforts.

Haussler received his PhD in computer science from the University of Colorado at Boulder. He is a member of the National Academy of Sciences and the American Academy of Arts and Sciences and a fellow of the Academy and Association for the Advancement of Artificial Intelligence (AAAI). He has won a number of awards, including the 2011 Weldon Memorial Prize from University of Oxford, the 2009 ASHG Curt Stern Award in Human Genetics, the 2008 Senior Scientist Accomplishment Award from the International Society for Computational Biology, the 2005 Dickson Prize for Science from Carnegie Mellon University, and the 2003 ACM/AAAI Allen Newell Award in Artificial Intelligence.

 

Daphne Koller

Daphne Koller is the Rajeev Motwani Professor in the Computer Science Department at Stanford University. Her main research interest is in developing and using machine learning and probabilistic methods to model and analyze complex domains. Her current research projects include models in computational biology, computational medicine, and in extracting semantic meaning from sensor data of the physical world. Daphne Koller is the author of over 180-refereed publications, which have appeared in venues spanning Science, Science Translational Medicine, Nature Genetics, Cell, Games and Economic Behavior, and a variety of conferences and journals in AI and Computer Science. She has received 9 best paper or best student paper awards and has given keynote talks at over 10 different major conferences, also spanning a variety of areas. She was the program co-chair of the NIPS 2007 and UAI 2001 conferences, and has served on numerous program committees and as associate editor of the Journal of Artificial Intelligence Research, the Machine Learning Journal, and the Journal of Machine Learning Research. She was awarded the Arthur Samuel Thesis Award in 1994, the Sloan Foundation Faculty Fellowship in 1996, the ONR Young Investigator Award in 1998, the Presidential Early Career Award for Scientists and Engineers (PECASE) in 1999, the IJCAI Computers and Thought Award in 2001, the Cox Medal for excellence in fostering undergraduate research at Stanford in 2003, the MacArthur Foundation Fellowship in 2004, the ACM/Infosys Award in 2008, and was inducted into the National Academy of Engineering in 2011.

 

Jill Mesirov

Jill Mesirov is Associate Director and Chief Informatics Officer at the Broad Institute, where she directs Computational Biology and Bioinformatics. The Broad’s Computational Biology and Bioinformatics community includes computational scientists, software engineers, and information technology experts working throughout the institute’s programs and platforms. They share their expertise across a wide range of biological and technological applications. Mesirov is also a member of the David H. Koch Institute for Integrative Cancer Research at MIT and adjunct professor of bioinformatics at Boston University.

Mesirov is a computational scientist who has spent many years working in the area of high-performance computing on problems that arise in science, engineering, and business applications. Her current research interest is computational biology with a focus on algorithms and analytic methodologies for pattern recognition and discovery with applications to cancer genomics and infectious disease. In addition, Mesirov is committed to the development of practical, accessible software tools to bring these methods to the general biomedical research community.

In 1997, Mesirov came to the Whitehead Institute/MIT Center for Genome Research, now part of the Broad Institute, from IBM, where she was manager of computational biology and bioinformatics in the Healthcare/Pharmaceutical Solutions Organization. Before joining IBM in 1995, she was the director of research at Thinking Machines Corporation for 10 years. She has also held positions in the mathematics department at the University of California at Berkeley and has served as an associate executive director of the American Mathematical Society.

Mesirov is a fellow of the American Association for the Advancement of Science, director of the International Society for Computational Biology, and former president of the Association for Women in Mathematics. She serves on numerous academic and corporate scientific advisory and journal editorial boards.

Mesirov received her BA in mathematics from the University of Pennsylvania. She earned her MA and PhD in mathematics from Brandeis University.

 

John Quackenbush, PhD

John Quackenbush received his PhD in 1990 in theoretical physics from the University of California, Los Angeles (UCLA) working on string theory models. Following 2 years as a postdoctoral fellow in physics, Dr. Quackenbush applied for and received a Special Emphasis Research Career Award from the National Center for Human Genome Research to work on the Human Genome Project. He spent 2 years at the Salk Institute working on developing physical maps of human chromosome 11 and 2 years at Stanford University working on new laboratory and computational strategies for sequencing the Human Genome. In 1997, he joined the faculty of The Institute for Genomic Research (TIGR), where his focus began to shift to postgenomic applications with an emphasis on microarray analysis. Using a combination of laboratory and computational approaches, Dr. Quackenbush and his group developed analytical methods based on integration of data across domains to learn biological meaning from high-dimensional data. In 2005, he was appointed Professor of Biostatistics and Computational Biology and Professor of Cancer Biology at the Dana-Farber Cancer Institute (DFCI) and Professor of Computational Biology and Bioinformatics at the Harvard School of Public Health. Since that time, his work has increasingly focused on the analysis of human cancer using systems-based approaches to understanding and modeling biological problems. In 2010, he launched the Center for Cancer Computational Biology (CCCB) at the DFCI, which provides broad-based bioinformatics support to the local research community using a collaborative consulting model, as well as performing and analyzing large-scale second generation DNA sequencing.

His expertise is in genomics technologies, including sequencing and array-based approaches, integrative genomics, personalized genomics, and the integration of clinical and research data to drive discovery.