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20260401T110706Z
DTSTART;TZID=America/New_York:20260415T100000
DTEND;TZID=America/New_York:2
 0260415T120000
URL;TYPE=URI:https://www.wpi.edu/news/calendar/events/ece-p
 hd-dissertation-defense-martha-cash
ECE PhD Dissertation Defense by: Martha Cash
\n\nImage\n  \n\n\n\nTitle:\nData Driven Approaches for Traffic Prediction 
 and Topology Inference in Communication Networks\nAbstract:\nModern commun
 ication networks require accurate, data-driven methods to understand both 
 how traffic evolves over time and how network structure affects end-to-end
  performance. This dissertation develops learning-based frameworks for two
  complementary problems in network management: traffic matrix prediction a
 nd network topology inference.\nThe first part of the dissertation focuses
  on traffic matrices, which describe the traffic exchanged between source-
 destination pairs in a network. Because Internet traffic is often bursty, 
 nonstationary, and heterogeneous across flows, models trained directly on 
 raw data can miss the underlying temporal structure. To address this, this
  work develops a preprocessing framework based on multivariate singular sp
 ectrum analysis and studies time-series clustering as a scalable predictio
 n strategy. Together, these methods improve traffic matrix prediction by e
 xtracting shared temporal structure and decomposing the forecasting task i
 nto smaller, more manageable subproblems.\nThe second part of the disserta
 tion addresses topology inference from end-to-end delay measurements colle
 cted only at boundary nodes. Since multiple physical topologies can produc
 e similar observations, exact recovery is inherently ambiguous. This work 
 reformulates topology inference as a supervised classification problem ove
 r topology equivalence classes and shows that accurate inference is possib
 le for moderate network sizes.\nOverall, this dissertation demonstrates ho
 w structure-aware learning can improve both temporal prediction and struct
 ural inference in communication network.\n\nResearch Advisor:\nProf. Alex 
 Wyglinski\nECE Department, WPI\n\nCommittee Members:\nProf. Bo Tang\nECE D
 epartment, WPI\nProf. Charlotte Fowler\nMathematical Sciences, WPI\nProf. 
 Randy Paffenroth\nMathematical Sciences, WPI\n\n
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