Select a calendar:
Filter May Events by Event Type:
SUNMONTUEWEDTHUFRISAT
Events for May 27, 2011
-
Meet USC: Admission Presentation, Campus Tour, & Engineering TalkFri, May 27, 2011
Viterbi School of Engineering Undergraduate Admission
Receptions & Special Events
This half day program is designed for prospective freshmen and family members. Meet USC includes an information session on the University and the Admission process; a student led walking tour of campus and a meeting with us in the Viterbi School. Meet USC is designed to answer all of your questions about USC, the application process and financial aid.Reservations are required for Meet USC. This program occurs twice, once at 8:30 a.m. and again at 12:30 p.m. Please visit http://usconnect.usc.edu/ to check availability and make an appointment. Be sure to list an Engineering major as your "intended major" on the webform!
Location: USC Admission Center
Audiences: Everyone Is Invited
Contact: Viterbi Admission
-
Market-Oriented Cloud Computing and the Aneka Platform
Fri, May 27, 2011 @ 10:30 AM - 11:30 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Rajkumar Buyya, Professor/The University of Melbourne and Manjrasoft, Australia
Talk Title: Market-Oriented Cloud Computing and the Aneka Platform
Abstract: Computing is being transformed to a model consisting of services that are commoditised and delivered in a manner similar to utilities such as water, electricity, gas, and telephony. In such a model, users access services based on their requirements without regard to where the services are hosted. Several computing paradigms have promised to deliver this utility computing vision. Cloud computing is the most recent emerging paradigm promising to turn the vision of "computing utilities" into a reality.
Cloud computing has emerged as one of the buzzwords in the ICT industry. Several IT vendors are promising to offer storage, computation and application hosting services, and provide coverage in several continents, offering Service-Level Agreements (SLA) backed performance and uptime promises for their services. It delivers infrastructure, platform, and software (application) as services, which are made available as subscription-based services in a pay-as-you-go model to consumers. The price that Cloud Service Providers charge can vary with time and the quality of service (QoS) expectations of consumers.
This talk (1) presents the 21st century vision of computing and identifies various IT paradigms promising to deliver the vision of computing utilities; (2) defines the architecture for creating market-oriented Clouds by leveraging technologies such as VMs; (3) provides thoughts on market-based resource management strategies that encompass both customer-driven service management and computational risk management to sustain SLA-oriented resource allocation; (4) presents Aneka, a software system for rapid development of Cloud applications and their deployment on private/public Clouds with resource provisioning driven by SLAs and user QoS requirements, (5) reports experimental results on deploying Cloud applications in engineering, gaming, and health care domains on private or public Clouds, and (6) concludes with the need for convergence of competing IT paradigms for delivering our 21st century vision along with pathways for future research.
Biography: Dr. Rajkumar Buyya is Professor of Computer Science and Software Engineering; and Director of the Cloud Computing and Distributed Systems (CLOUDS) Laboratory at the University of Melbourne, Australia. He is also serving as the founding CEO of Manjrasoft., a spin-off company of the University, commercializing its innovations in Cloud Computing. He has authored 350 publications and four text books. He also edited several books including "Cloud Computing: Principles and Paradigms" recently published by Wiley Press, USA.
Software technologies for Grid and Cloud computing developed under Dr. Buyya's leadership have gained rapid acceptance and are in use at several academic institutions and commercial enterprises in 40 countries around the world. Dr. Buyya has led the establishment and development of key community activities, including serving as foundation Chair of the IEEE Technical Committee on Scalable Computing and five IEEE/ACM conferences. These contributions and international research leadership of Dr. Buyya are recognized through the award of "2009 IEEE Medal for Excellence in Scalable Computing" from the IEEE Computer Society, USA. Manjrasoftâs Aneka Cloud technology developed under his leadership has received "2010 Asia Pacific Frost & Sullivan New Product Innovation Award".Board of major Journals (like TCS, IEEE TC, COMNET, IJDSN, JEA). He has co-initiated international events related to sensor networks (ALGOSENSORS, DCOSS). He has coordinated several externally funded European Union R&D Projects related to fundamental aspects of modern networks.
Host: Professor Viktor K. Prasanna
Location: Ronald Tutor Hall of Engineering (RTH) - 324
Audiences: Everyone Is Invited
Contact: Janice Thompson
-
Daniel J. Epstein Department of Industrial and Systems Engineering Seminar
Fri, May 27, 2011 @ 11:00 AM - 12:00 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Suvrajeet Sen, Professor, Industrial and Systems Engineering & Director, Data-Driven Decisions Lab, Ohio State University
Talk Title: "Multi-scale Stochastic Optimization for Energy Systems Planning and Operations"
Abstract: Many energy systems planning models require that they be integrated with simulators. The latter are designed to operate at certain levels of granularity, whereas, operations planning may require a different time-scale. These types of multi-scale models are important for the integration of renewable resources (facing fine-grain uncertainty) into power grid operations (facing coarse-grain uncertainty). After introducing models for these emerging applications, we will discuss a multi-stage version of the Stochastic Decomposition (SD) algorithm. This algorithm, which works with sample paths, allows the integration of controllers based on Dynamic Programming (and ADP) for fine-grain simulation and control. Concurrently, SD produces operations plans that hedge against coarse-grain uncertainty.
Biography: Suvrajeet Sen is Professor of Industrial and Systems Engineering, and Director of the Data-Driven Decisions Lab at the Ohio State University. Until recently, he also served as the Director of the College's Center for Energy, Sustainability, and the Environment. Prior to joining OSU, he served on the faculty at the University of Arizona, and he also served as a program director at NSF where he was responsible for the Operations Research, and the Service Enterprise Engineering programs. Professor Sen is a Fellow of INFORMS. He has served on the editorial board of several journals, including Operations Research as Area Editor for Optimization, and as Associate Editor in INFORMS Journal on Computing, Operations Research, and Journal of Telecommunications Systems. Professor Sen is the past-Chair of the INFORMS Telecommunications Section and founded the INFORMS Optimization Section.
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Georgia Lum
-
"Readying Machine Learning for Quantum Computing"
Fri, May 27, 2011 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Hartmut Neven, Google
Talk Title: "Readying Machine Learning for Quantum Computing"
Abstract: Modern approaches to machine learning formulate training of a classifier as an optimization problem in which simultaneously the training error as well as the classifier complexity is minimized. For computational efficiency typically a convex objective is constructed. But it is well known that such a choice comes at a cost. For instance, convex loss functions designed to measure training performance are not as robust to noise as their non-convex counter parts and convex regularization does
not achieve as high levels of sparsity as versions involving the L0-norm. Non-convex losses also figure prominently in recent attempts to derive tighter bounds for the generalization error. Here we report on experiments to train with non-convex objectives using discrete optimization in a formulation adapted to take advantage of emerging hardware for quantum optimization. A key finding is that the resulting
classifiers are already competitive when using as temporary stand-in a classical heuristic solver. We will give an overview of the state of the quantum hardware development as well as what advantages in terms of quality of the solution we can hope to attain from a theoretical point of view.
Host: Daniel Lidar
Location: Seaver Science Library (SSL) - 150
Audiences: Everyone Is Invited
Contact: Daniel Lidar
