About the Princeton EDGE LabTheory is "inalienable" since it offers explanatory, rather than descriptive models and top-down design with predictive power. Theory is also "incomplete" given its sensitivity to the mathematical crystallization and the need to make a difference in live networks. As an edge between theory and practice of networking, the Princeton EDGE Lab builds systems designed by proven theorems, and proves theorems about deployed systems.It targets:
1. Bigger overlap between the two (e.g., develop the theory for tight bounds on convergence rate, transient behavior characterization rather than equilibrium behavior, impact of control parameter granularity and feedback noise, remove timescale separation assumptions, etc.)
2. New theory questions (e.g., proper accounting of computational and communication overhead, or simplicity-driven optimization: insist on zero overhead rather than optimality proof and then tightly bound suboptimality gap and its impact on user performance)
3. Theory-inspired deployment (e.g., transfer some of the theory inspired algorithms to commercial adoption and large scale operations serving real customers, and turn some of the challenges in that process to inspire new theory).
Princeton EDGE LabPEL
ID-ec649977-ef5c-4fe5-b6f0-755c62ddcbd0The lab consists of two rooms and has experimental facilities to provide an edge between the "theory node" and the "systems node" in the networking research community, especially for edge networking. It leverages the lessons and data accumulated through realistic experiments to validate the predictions of theory, falsify the assumptions behind theory, sharpen the characterizations that are loose in theory, and inspire new question formulations in theory. It partners with many systems and deployments in both academia and industry. It builds systems designed by proven theorems, and proves theorems about deployed systems.Mung ChiangProfessor of Electrical Engineering; Director of EDGE Lab --
Mung Chiang is the Arthur LeGrand Doty Professor of Electrical Engineering at Princeton University. He is also an affiliated faculty in Applied and Computational Mathematics, and in Computer Science, and has served as the Director of Graduate Studies in Electrical Engineering since 2009. He received his B.S. (Hons.), M.S., and Ph.D. degrees from Stanford University in 1999, 2000, and 2003, respectively, and was an Assistant Professor 2003-2008, a tenured Associate Professor 2008-2011, and a Professor 2011-2013 at Princeton University. He was a Hertz Fellow in 1999-2003, a H. B. Wentz Junior Faculty at Princeton in 2005, and was elected an IEEE Fellow in 2012.
Chiangâ€™s research areas include the Internet, wireless networks, broadband access networks, content distribution networks, network economics, and online social networks...Bharath BalasubramanianPostdoctoral research associateZhenming LiuPostdoctoral research associateAveek DuttaPostdoctoral research associateFelix Ming Fai WongPhD candidateJiasi ChenPhD candidateSrinivas NaryanaPhD candidate; Co-advised by Jennifer RexfordChris BrintonPhD candidateCarlee Joe-WongPhD candidateMichael WangPhD candidateShirley Xiaoli WangPhD studentPrinceton EDGE Lab SponsorsNational Science FoundationOffice of Naval ResearchAir Force Office of Sponsored ResearchArmy Research OfficePrinceton UniversityDARPANokia-SiemensQualcommAT&TMicrosoftUniversity Research Program: The EDGE Lab GoogleTelcordiaIntelHPSESTo bridge over the theory-practice divide in networking and build on the combined core of rigor in the answers and relevance in the questions.ID-258743c3-72e1-43c4-b648-c1dc62e3a78cMethodologyThe underlying methodology include distributed optimization, stochastic control, games and economics, graphs and random processes, etc. and the functionalities involved range from power control and scheduling to congestion control and routing, from topology control and distribution to pricing and measuring. Distributed OptimizationStochastic ControlGamesEconomics Graphs Random ProcessesPower ControlPower SchedulingCongestion ControlRoutingTopology Control Distribution Pricing MeasuringGrand ChallengesThe grand challenges in fundamental research include nonconvexity, dynamics, and high dimensionality. Nonconvexity Dynamics High DimensionalityEngineering Artifacts in NetworkingRe-examine the mathematical crystallization of engineering artifacts in networking. ID-489f0f78-2d9f-40f6-9ab9-63fd145588e6Through collaboration across many disciplinary boundaries as well as the academia-industry boundary, it constantly re-examines the mathematical crystallization of engineering artifacts in networking... In addition to an environment where theory's impact can be amplified, it also serves as an incubator for innovations, a platform for technology transfers, and a venue for undergrad and grad education. ResearchConduct research projects spanning the modeling, analysis, and design of networks, both technological and human ones.ID-84af8948-b133-4fcd-8cac-6d0007b02a031The research part consists of an evolving set of research projects spanning the modeling, analysis, and design of networks, both technological and human ones. InnovationServe as an incubator for innovations. ID-6d65c266-6ff5-4027-9a89-0371dcd920f42Technology TransferServe a platform for technology transfers. ID-ec9edf8f-3aaf-4f11-b0ea-b028c047d84f3EducationServe a venue for undergrad and grad education.ID-28438c76-ef35-4c50-a2b0-c40a6ddfd17e42013-12-23OwenAmburOwen.Ambur@verizon.net