Machine learning and artificial intelligence are changing the nature of biological research, especially genomics. Artificial intelligence applications are opening up our understanding of ourselves and disease, and we must strive to create tools that can work as partners in research, not simply as black boxes. Barbara Engelhardt is an assistant professor in the Computer Science Department at Princeton University since 2014. She graduated from Stanford University and received her Ph.D. from the University of California, Berkeley, advised by Professor Michael Jordan. She did postdoctoral research at the University of Chicago, working with Professor Matthew Stephens, and three years at Duke University as an assistant professor. Interspersed among her academic experiences, she spent two years working at the Jet Propulsion Laboratory, a summer at Google Research, and a year at 23andMe, a DNA ancestry service. Professor Engelhardt received an NSF Graduate Research Fellowship, the Google Anita Borg Memorial Scholarship, the Walter M. Fitch Prize from the Society for Molecular Biology and Evolution, an NIH NHGRI K99/R00 Pathway to Independence Award, and the Sloan Faculty Fellowship. Professor Engelhardt is currently a PI on the Genotype-Tissue Expression (GTEx) Consortium. Her research interests involve statistical models and methods for analysis of high-dimensional data, with a goal of understanding the underlying biological mechanisms of complex phenotypes and human diseases. This talk was given at a TEDx event using the TED conference format but independently organized by a local community.Watch Now
NASA’s Human Research Program releases “Metabolomics: You Are What You Eat” video to highlight its Twins Study which uses omics to study Mark and Scott Kelly’s metabolites. Omics is an evolving field integrating collections of measurements, biomolecules and sub-disciplines to provide a more complete picture of health.Watch Now
Only 17% of the English-language biographies on Wikipedia are about women – but the statistic won’t stay that low for long if Dr Jess Wade has her way. A passionate advocate for diversity in science, Jess balances her work as an award-winning physicist at Imperial College London with her role as a ‘Wikipedian’, creating and uploading the biographies of underrepresented groups in science.Watch Now
Ted Goldstein is a Silicon Valley tech executive turned cancer researcher. After a successful career and an instrumental role as top executive at Apple, Ted returned to College to obtained his Ph.D. in Bioinformatics and Biomolecular Engineering to study cancer and to approach the disease the way software engineers approach difficult problems.Watch Now
Discover how Microsoft Genomics is empowering organizations to explore new avenues of research. The Seattle Children's Hospital SIDS (Sudden Infant Death Syndrome) research with Microsoft Genomics uses Microsoft AI to analyze data in a massive scale. With Microsoft AI, Seattle Children's Hospital is able to identify genetic contributions to pediatric disorders and come closer to reaching their goals of recognizing SIDS earlier and preventing it from happening.Watch Now
Modern machine learning is great for helping scientists sort through huge data sets. But it’s less useful for things that require inference or reasoning – both vital to the scientific process. One group of scientists are now trying to fix this problem with a new kind of machine learning. This new approach aims to find the underlying algorithmic models that interact and generate data, to help scientists uncover the dynamics of cause and effect.Watch Now
Having been in the Laboratory Informatics and LIMS Consulting business for quite some time, we at CSols, Inc. have been exposed to many myths and misconceptions. While there may be a small kernel of truth to some of these, for the most part these statements do not hold water. In this video, we listed out the top five myths and “Bust” them.