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He has served as a consultant to the White House and various state governments on financial aid policies. Bettinger, Eric and Bridget Long. Meeting global demand for growing the science, technology, engineering, and mathematics STEM workforce requires solutions for the shortage of qualified instructors.

We propose and evaluate a model for scaling up affordable access to effective STEM education through national online education platforms. These platforms allow resource-constrained higher education institutions to adopt online courses produced by the country's top universities and departments. A multisite randomized controlled trial tested this model with fully online and blended instruction modalities in Russia's online education platform.

We find that online and blended instruction produce similar student learning outcomes as traditional in-person instruction at substantially lower costs. Adopting this model at scale reduces faculty compensation costs that can fund increases in STEM enrollment.

View details for DOI View details for PubMedID View details for Web of Science ID This chapter discusses the collaboration between a national college access program, the National College Advising Corps NCAC , and its research and evaluation team at Stanford University.

NCAC is currently active in almost four hundred high schools and through the placement of a recent college graduate to serve as a college adviser provides necessary information and support for students who may find it difficult to navigate the complex college admission process.

The advisers also conduct outreach to underclassmen in an effort to improve the school-wide college-going culture. Analyses include examination of both quantitative and qualitative data from numerous sources and partners with every level of the organization from the national office to individual high schools. In the past, he has also worked on industry cloud computing projects with Impulsonic and Illumina.

He served as the vice-president of the Computer Science Graduate Student Association at NC State, and received the graduate student leadership award in He enjoys taking walks in nature, and studying cancer biology in his spare time. Prior to joining Stanford, Keith was Technical Lead at Motorola where he led development of software for handwriting recognition on a Chinese cell phone.

He broke into computational biology studying models of molecular evolution under Dr. Daniel earned his MS degree in Computer Science also from the University of Kentucky, but, if you want to elicit a knowing laugh from him, ask him about his BA degree from Transylvania University.

Previous research experience includes fruit genomics, archaea genomics, and human microbiome. He is responsible for diverse bioinformatics needs for the Center Initiated Projects and its extensive collaboration network. Prior to joining the GBSC, she worked as a senior member in the Product Care team at Roche Molecular Systems, conducting root-cause investigations into assay performance CAPA and leading post-launch product improvement projects from feasibility to verification and validation.

Her research interests are in the areas of statistical analysis, mathematical modeling and machine learning in computational systems biology. She was also responsible for the computational modeling parts of various projects related to plant systems biology at North Carolina State University NCSU. D in Systems Biology Research lab. She has a broad range of research interests in big data, bioinformatics, population genomics, and statistical genomics.

Her postdoctoral work at the DOE Joint Genome Institute, a division of Lawrence Berkeley National Lab, focused on population genetics using various types of NGS data to reveal molecular mechanisms underlying adaptive strategies in sorghum. Among her start-ups is Verseon, a drug design company, where she was founding team member responsible for building the platform that brought together multiple innovations in molecular modeling, library design, large scale computation and data mining.

It was while working on a project to predict solar flares at the Stanford Solar Center that she got interested in machine learning and data science. Most recently, she was part of the Pritchard Lab in the Genetics department at the Stanford where she helped develop SciReader, a recommendation engine for biomedical literature.

He has a PhD in Mechanical Engineering from MIT where he designed peptide scaffolds with tailored mechanical properties for tissue engineering applications. He holds two patents for medical devices. He was the lead software engineer and co-author on a wearable biosensor publication that got mentioned on NIH Director's blog. Yuzhen Ye on metagenomics and next generation sequencing.

Michael Snyder and his iPoP team to analyze accuracy of some of the best practice tools and methods for various types of metagenomic samples. He designed and developed ChIP-seq antibody validation pipelines which used motif discovery and alignment algorithms. He also created the SNAP system, a web-based analysis tool and data repository for functional genomics analyses. His research interests lie in large-scale parallel computing, distributed systems, and data analysis using machine learning techniques.

He worked on building a Hadoop ecosystem and HBase database in the cloud when he worked with our center as an intern, and then he fully joined our team to continue developing Hummingbird, an application helps to optimize performance of genomics pipelines on the cloud. She has expertise in statistical modeling, library normalization, classification, programming and visualization.

Yue developed active and close collaborations with dozens of labs at Stanford and brought her interdisciplinary knowledge and professional skills to help Stanford researchers achieve their goals. He has hands-on experience in analyzing different types of next generation sequencing NGS data. He has expertise in machine learning and algorithm development. He enjoys applying, defining, and validating computational approaches to deliver important biological insights.

She recently finished her post-doctorate training with Dr. Michael Snyder at Stanford University. With Dr. Wenyu has first-authored research articles published in Nature, Cell Stem Cell, Cell Systems, and EMBO Journal, and actively serves as a reviewer for a number of scientific journals in the genomics field. Prior to his bioinformatics stint, he was a pukka Signal Processing engineer. He was Sr. The team works with a large number of researchers grads, post-docs, faculty , and industry partners, and is responsible for several prestigious genomics projects.

The solutions are often developed in an open source general purpose manner so that it can benefit the larger biomedical community at Stanford and elsewhere. Presented at Genetics Retreat Presented at Genetics Retreat Download. Presented at HMP2 user meeting Download. Presented at Big Data for Biomedicine Download. Director of Bioinformatics, Sowmi Utiramerur, presents platform overview and Sr. Video link to her talk. Other panelist's presentations can be found here. The Center is generating banks of data and iPS cell lines that are valuable resources to the entire CIRM community and provide important insights into stem cell research.

It also serves as an important focal point for collaborative projects with other CIRM investigators and makes genomics capabilities available to the entire regenerative medicine community. Our goal is to make data easily usable by the research community.

We take on the stewardship of common biomedical knowledge bases needed for genomic data interpretation and develop a technology stack around this data APIs, Queries, Visualization so that genomic data interpretation can happen more easily. The other goal for this project is to bring the inter-disciplinary community together - instead of researchers struggling with deploying and correctly using tools and knowledgebases in their own compute environments, we intend to provide a Cloud based platform that provides the software stack as a service.

The goal of ENCODE is to build a comprehensive parts list of functional elements in the human genome, including elements that act at the protein and RNA levels, and regulatory elements that control cells and circumstances in which a gene is active. In this era of Big Data Genomics, our goal at the GBSC is to provide best-in-class high performance computational infrastructure and bioinformatics services for the Stanford community.

Our team provides a suite of comprehensive services including management of the infrastructure and users.

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