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X-ORIGINAL-URL:https://coe.northeastern.edu
X-WR-CALDESC:Events for Northeastern University College of Engineering
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DTSTART;VALUE=DATE:20201015
DTEND;VALUE=DATE:20201231
DTSTAMP:20260423T120025
CREATED:20201015T142444Z
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SUMMARY:Meet Your Graduate Student Ambassadors!
DESCRIPTION:Meet your Student Ambassadors! Prospective and Admitted Graduate Students are invited to meet their Student Ambassador via Unibuddy.
URL:https://coe.northeastern.edu/event/meet-your-graduate-student-ambassadors/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201030T110000
DTEND;TZID=America/New_York:20201030T120000
DTSTAMP:20260423T120025
CREATED:20201023T221519Z
LAST-MODIFIED:20201023T221519Z
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SUMMARY:ECE PhD Dissertation Defense: Ran Liu
DESCRIPTION:PhD Dissertation Defense: Optimal Proactive Services with Uncertain Predictions \nRan Liu \nLocation: Zoom Link \nAbstract: With the evolution of technologies such as machine learning and data science\, proactive services with the aid of predictive information have been recognized as a promising method to exploit network bandwidth\, storage\, and computation resources to achieve improved user experiences\, especially delay performance.\nSpecifically\, services can be processed proactively when the system is lightly loaded\, with the results stored to meet user demand in the future.\nOur primary goal in the thesis is to investigate the fundamental performance improvement that can be achieved from proactive services under uncertain predictions. We aim to analyze the queueing behavior of proactive systems under certain proactive strategies and characterize the improvement in terms of the limiting fraction of proactive work and the limiting average delay. \nIn the first work\, we analytically investigate the problem of how to efficiently utilize uncertain predictive information to design proactive caching strategies with provably good access-delay characteristics.\nFirst\, we derive an upper bound for the average amount of proactive service per request that the system can support.\nThen we analyze the behavior of a family of threshold-based proactive strategies with a Markov chain\, which shows that the average amount of proactive service per request can be maximized by properly selecting the threshold.\nFinally\, we propose the UNIFORM strategy\, which is the threshold-based strategy with the optimal threshold\, and show that it outperforms the commonly used Earliest-Deadline-First (EDF) type proactive strategies in terms of delay.\nWe perform extensive numerical experiments to demonstrate the influence of thresholds on delay performance under the threshold-based strategies\, and specifically\, compare the EDF strategy and the UNIFORM strategy to verify our results. \nIn the second work\, we study a more generalized proactive service problem with a more generalized service model and derive explicit solutions on the limiting average fraction of proactive work and the limiting average delay in closed-form expressions.\nIn this work\, we analytically investigate how to optimally take advantage of under-utilized network resources for proactive services with the aid of uncertain predictive information.\nSpecifically\, we first derive an upper bound on the fraction of services that can be completed proactively by a single-server system.\nThen we analyze a family of fixed-probability (FIXP) proactive strategies in two proactive systems\, namely the Genie-Aided system and the Realistic Proactive system.\nWe analyze the asymptotic behaviors of the FIXP strategies by modeling a Markov process and the corresponding embedded Markov Chain.\nWe obtain optimal FIXP strategies in both systems and prove that the optimal FIXP strategies maximize the limiting fraction of proactive service among all proactive strategies and minimize average delay among FIXP strategies.\nWe perform extensive numerical experiments to demonstrate the influence of the parameter of FIXP on the performance of the limiting fraction of proactive service and the limiting average delay in both proactive systems and verify our theoretical results in multiple scenarios.
URL:https://coe.northeastern.edu/event/ece-phd-dissertation-defense-ran-liu-2/
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DTSTART;TZID=America/New_York:20201030T120000
DTEND;TZID=America/New_York:20201030T124500
DTSTAMP:20260423T120025
CREATED:20200828T215120Z
LAST-MODIFIED:20200828T215120Z
UID:22064-1604059200-1604061900@coe.northeastern.edu
SUMMARY:10 Advanced EndNote Features
DESCRIPTION:Start your fall 2020 research off on the right foot with Snell Library’s series of online workshops about citation management! In this session\, learn how advanced features in EndNote can help you manage citations for yourself or your research group. \nRegister here: bit.ly/citationmgmtworkshops
URL:https://coe.northeastern.edu/event/10-advanced-endnote-features/
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BEGIN:VEVENT
DTSTART;TZID=America/New_York:20201030T120000
DTEND;TZID=America/New_York:20201030T130000
DTSTAMP:20260423T120025
CREATED:20200916T194143Z
LAST-MODIFIED:20201021T191428Z
UID:22277-1604059200-1604062800@coe.northeastern.edu
SUMMARY:Bioengineering Works in Progress Student Seminar Series
DESCRIPTION:This virtual seminar series is an opportunity for Bioengineering graduate students to present their research. The first presenter is Ronak Ansaripour from the Carrier lab\, title: “Controlling extracellular matrix environment in guiding 3D Retinal Organoid formation”. The second presenter is Alexander Grath from the Dai lab\, title: “Directly Reprogramming Fibroblasts into Functional Endothelial Cells”. \nPlease email Danielle at d.freshnock@northeastern.edu for the link to the seminar.
URL:https://coe.northeastern.edu/event/bioengineering-works-in-progress-student-seminar-series-8/
ORGANIZER;CN="Bioengineering":MAILTO:bioe@northeastern.edu
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