Person:
Towsley, Donald

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Distinguished University Professor, Department of Computer Science
Last Name
Towsley
First Name
Donald
Discipline
Computer Sciences
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Introduction
Professor Towsley's research spans a wide range of activities from stochastic analyses of queueing models of computer and telecommunications to the design and conduct of measurement studies. He has performed some of the pioneering work on the exact and approximate analyses of parallel/distributed applications and architectures. More recently, he pioneered the area of network tomography and the use of fluid models for large networks. He has published extensively, with over 150 articles in leading journals.
Professor Towsley has been an editor of the IEEE Transactions on Communications, IEEE/ACM Transactions on Networking, and Journal of Dynamic Discrete Event Systems. He is currently on the Editorial boards of Networks and Performance Evaluation. He was a Program Co-chair of the joint ACM SIGMETRICS and PERFORMANCE '92 conference. He is a two-time recipient of the Best paper Award of the ACM Sigmetrics Conference. He is a Fellow of the IEEE and of the ACM. He is also a member of ORSA and is active in the IFIP Working Groups 6.3 on Performance Modeling of Networks and 7.3 on Performance Modeling. Towsley is the recipient of one of the IEEE’s most prestigious honors, the 2007 IEEE Koji Kobayashi Computers and Communications Award. He lso received a UMass Amherst Distinguished Faculty Lecturer award in 2002 and a UMass Amherst College of Natural Sciences and Mathematics Faculty Research Award in 2003.
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  • Publication
    Evidence to support common application switching behaviour on smartphones
    (2019-01-01) Turner, Liam D.; Whitaker, Roger M.; Allen, Stuart M.; Linden, David E. J.; Tu, Kun; Towsley, Don
    We find evidence to support common behaviour in smartphone usage based on analysis of application (app) switching. This is an overlooked aspect of smartphone usage that gives additional insight beyond screen time and the particular apps that are accessed. Using a dataset of usage behaviour from 53 participants over a six-week period, we find strong similarity in the structure of networks built from app switching, despite diversity in the apps used, and the volume of app switching. App switch networks exhibit small-world, broad-scale network features, with a rapid popularity decay, suggesting that preferential attachment may drive next-app decision-making.