Computer Science Colloquium
| Date/Time: | Monday, 10 Mar 2014 at 3:40 pm |
|---|---|
| Location: | 223 Atanasoff Hall |
| Cost: | Free |
| Phone: | 515-294-4377 |
| Channel: | College of Liberal Arts and Sciences |
| Categories: | Lectures |
| Actions: | Download iCal/vCal | Email Reminder |
Bio
Tim Menzies (P.hD., UNSW) is a Professor in CS at WVU; the author of over 230 referred publications; and is one of the 50 most cited authors in software engineering (out of 50,000+ researchers, see http://goo.gl/wqpQl). At WVU, he has been a lead researcher on projects for NSF, NIJ, DoD, NASA, USDA, as well as joint research work with private companies. He teaches data mining and artificial intelligence and programming languages.
Prof. Menzies is the co-founder of the PROMISE conference series devoted to reproducible experiments in software engineering (see http://promisedata.googlecode.com). He is an associate editor of IEEE Transactions on Software Engineering, Empirical Software Engineering and the Automated Software Engineering Journal. In 2012, he served as co-chair of the program committee for the IEEE Automated Software Engineering conference. In 2015, he will serve as co-chair for the ICSE'15 NIER track.
For more information, see his:
website: http://menzies.us
vita: http://goo.gl/8eNhY
pubs: http://goo.gl/0SWJ2p
Abstract
Consider the premise of Big Data:
better conclusions = same algorithms + more data + more cpu
If this were always true, then there would be no role for human analysts that reflected over the domain to offer insights that produce better solutions (since all such insight is now automatically generated from the CPUs).
This talk proposes a marriage of sorts between Big Data and software engineering. It reviews over a decade of work by the author in exploring user goals using CPU-intensive methods. It will be shown that analyst-insight was useful from building "better" tools (where "better" means generate more succinct recommendations, runs faster, scales to much larger problems).
The conclusion will be that in the age of big data, human analysis is still useful and necessary. But a new kind of software engineering analyst is required- one that know how to take full advantage of the power of Big Data. Preview the talk at:
http://www.slideshare.net/timmenzies/in-...ngineering