EngX: A mini-conference on leading ideas from Stanford Engineering
TOPIC: Big Data, Big Impact
The analysis of large data sets has the potential to be what some have called a dashboard for humanity, an intelligent tool that could lead to better ways of understanding the world and making decisions. At the first EngX (pronounced “en-JEX”) – a School of Engineering mini-conference – three Engineering faculty will explain how they are using data to prevent unwanted drug interactions, better understand human behavior and shed light on climate change.
Tuesday, November 12
7 - 8:30 p.m
Huang Engineering Center, NVIDIA Auditorium
Talks, followed by networking reception
Introduction by Stanford Engineering Dean Jim Plummer
Or attend the event via webinar.
Big Data in Medicine: Unearthing Unexpected Drug Side Effects
Russ Altman, Kenneth Fong Professor of bioengineering, genetics and medicine and, by courtesy, computer science
As medical data increasingly become available in electronic form, scientists have begun to use data mining methods to discover new medical knowledge. Altman focuses on understanding drug responses, drug side effects and drug interactions. His laboratory combines big data sets from public health sources, electronic medical records and Internet web searches to discover new drug interactions and side effects. Altman will describe his methods for a lay audience, and discuss the potential benefits and costs of data mining in the realm of medical discovery.
The Web: Humanity’s Sensor Network
Jure Leskovec, assistant professor of computer science
Activity of millions of people on the Web leaves massive digital traces that can be naturally represented and analyzed as complex dynamic networks of human interactions. Today the Web is a `sensor' that captures the pulse of humanity and allows us to observe phenomena that were once essentially invisible: the social interactions and collective behavior of hundreds of millions of people. Leskovec will discuss how analysis of massive networks can be applied to study online interactions and the dynamics of information flows through such networks.
Shedding Light on Climate Change with Dark Data
Christopher Ré, assistant professor of computer science
The knowledge of centuries sits locked in books, journals and government reports too numerous for any human being to comprehend. Although technology allows us to digitize this “dark data,” the imprecision and ambiguity of words has so far defied our attempts to use computer-based systems to analyze this accumulated wisdom. In the last few years, however, new statistical algorithms have been able to extract meaning from this mass of words. Re will describe how he has used one of the first such systems to help build the world’s most complete fossil record and biodiversity estimates – information that promises to help scientists better understand biological responses to climate change.
Russ Biagio Altman is the Kenneth Fong Professor of bioengineering, genetics and medicine (and of computer science, by courtesy). His research applies computing and informatics technologies to basic biological problems relevant to medicine with the long-term goal of enabling personalized medicine. He is particularly interested in understanding drug action at molecular, cellular, organism and population levels. He leads the Pharmacogenomics Knowledgebase Project, focusing on how genetic variation influences drop response. Altman holds a BA from Harvard College, an MD from Stanford Medical School and a PhD in medical information sciences from Stanford. He received the U.S. Presidential Early Career Award for Scientists and Engineers and a National Science Foundation CAREER Award. He chairs the Science Board advising the Food and Drug Administration commissioner and is a founder of Personalis Inc.
Jure Leskovec is assistant professor of computer science at Stanford University. His research focuses on mining large social and information networks. Problems he investigates are motivated by large scale data, the Web and on-line media. This research has won several awards including a Microsoft Research Faculty Fellowship, the Alfred P. Sloan Fellowship and numerous best paper awards. Leskovec received his bachelor's degree in computer science from University of Ljubljana, Slovenia, his PhD in in machine learning from the Carnegie Mellon University and postdoctoral training at Cornell University. You can follow him on Twitter @jure.
Christopher Ré is an assistant professor of computer science at Stanford University. The goal of his work is to enable users and developers to build applications that more deeply understand and exploit data. For his PhD work in probabilistic data management, Chris received the SIGMOD 2010 Jim Gray Dissertation Award. Chris received an NSF CAREER Award in 2011 and an Alfred P. Sloan fellowship in 2013. Artifacts from his work are components of IceCube, the world's largest neutrino detector and some of the highest fidelity knowledge bases about our earth, as well as data science products from Oracle, Pivotal/Greenplum and other vendors.
Last modified Mon, 11 Nov, 2013 at 11:27