Topic: Big Data: Unleashing Information
Speaker：Professor James M. Tien, member of the prestigious U. S.National Academy of Engineering
Brief introduction to the speaker
In 2007, Dr. James M. Tien became a Distinguished Professor and the Dean of the College of Engineering at the University of Miami, Coral Gables, Florida. He received the BEE from Rensselaer Polytechnic Institute (RPI) and the SM, EE and PhD from the Massachusetts Institute of Technology (MIT). He has held leadership positions at Bell Telephone Laboratories, at the Rand Corporation, and at Structured Decisions Corporation (which he co-founded). He joined the Department of Electrical, Computer and Systems Engineering at RPI in 1977, became Acting Chair of the department, joined a unique interdisciplinary Department of Decision Sciences and Engineering Systems as its founding Chair, and twice served as the Acting Dean of Engineering. Dr. Tien has published extensively, been invited to present dozens of plenary lectures, and been honored with both teaching and research awards, including being elected a Fellow in IEEE, INFORMS and AAAS and being a recipient of the IEEE Joseph G. Wohl Outstanding Career Award, the IEEE Major Educational Innovation Award, the IEEE Norbert Wiener Award, the IEEE Richard M. Emberson Award, and the IBM Faculty Award. He received a Doctor of Engineering (honoris causa) from Canada’s University of Waterloo and is also an Honorary Professor at over a dozen non-U.S. universities. Dr. Tien is an elected member of the prestigious U. S. National Academy of Engineering.
Brief introduction to the report:
At present, it is projected that about 4 zettabytes (or 10*21 bytes) of electronic data are being generated per year by everything from underground physics experiments to retail transactions to security cameras to global positioning systems. In the U. S., major research programs are being funded to deal with big data in all five sectors (i.e., services, manufacturing, construction, agriculture and mining) of the economy. Big Data is a term applied to data sets whose size is beyond the ability of available tools to undertake their acquisition, access, analytics and/or application in a reasonable amount of time. Whereas Tien (2003) forewarned about the data rich, information poor (DRIP) problems that have been pervasive since the advent of large-scale data collections or warehouses, the DRIP conundrum has been somewhat mitigated by the Big Data approach which has unleashed information in a manner that can support informed – yet, not necessarily defensible or knowledgeable – decisions or choices. Thus, by somewhat overcoming data quality issues with data quantity, data access restrictions with on-demand cloud computing, causative analysis with correlative data analytics, and model-driven with evidence-driven applications, appropriate actions can be undertaken with the obtained information. New acquisition, access, analytics and application technologies are being developed to further Big Data as it is being employed to help resolve the 14 grand challenges (identified by the National Academy of Engineering in 2008), underpin the 10 breakthrough technologies (compiled by the Massachusetts Institute of Technology in 2013) and support the Third Industrial Revolution of mass customization.
Time：2:00pm Sep. 18, 2014
Venue：Room2321, IGSNRR
Host：Prof. ZHOU Chenghu, academician of Chinese Academy of Sciences
