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Events for January 11, 2016
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Chevron Engineering Week Student Design Competition Application Deadline
Mon, Jan 11, 2016
Mork Family Department of Chemical Engineering and Materials Science
Student Activity
Viterbi Students are invited to participate in the 2016 Chevron Engineering Week Competition.
Competitions will be based on participation by teams each consisting of 7 students (Undergraduate and or/graduate) from any discipline studying at the USC Viterbi School of Engineering. Each team needs to select a team leader who must submit an application and must include the names of team members. No team additions/substitutions are allowed after the application has been submitted.
Application Deadline 12 noon PST January 14, 2016. Submit applications: https://uscviterbi.qualtrics.com/SE/?SID=SV_eQfcE6BabUzRAiN
Questions will be distributed to team leaders on January 15, 2016
Team projects are due Feb. 12, 2016
Presentations will be during Engineering Week (Feb 22-26)
Category 1 Petroleum Engineering Question
Category 2: Chemical Engineering Question
Application Deadline/ Rules:
1-No Faculty help may be solicited for the project.
2-Solution to Challenge question is due at 12 noon PST on February 12, 2016. Solution in digital format must be submitted to legat@usc.edu
3-The winning Team will be announced during Engineering Week.
4-All the engineering computations and backup materials must be included in the solution submitted.
Best of Luck!
Audiences: Everyone Is Invited
Contact: Juli Legat
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Rafael Ferreira da Silva (USC ISI) - Task Resource Consumption Prediction for Scientific Applications and Workflows
Mon, Jan 11, 2016 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Rafael Ferreira da Silva, USC ISI
Talk Title: Task Resource Consumption Prediction for Scientific Applications and Workflows
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
Estimates of task runtime, disk space usage, and memory consumption, are commonly used by scheduling and resource provisioning algorithms to support efficient and reliable scientific application executions. Such algorithms often assume that accurate estimates are available, but such estimates are difficult to generate in practice. In this work, we first profile real scientific applications and workflows, collecting fine-grained information such as process I/O, runtime, memory usage, and CPU utilization. We then propose a method to automatically characterize task requirements based on these profiles. Our method estimates task runtime, disk space, and peak memory consumption. It looks for correlations between the parameters of a dataset, and if no correlation is found, the dataset is divided into smaller subsets using the statistical recursive partitioning method and conditional inference trees to identify patterns that characterize particular behaviors of the workload. We then propose an estimation process to predict task characteristics of scientific applications based on the collected data. For scientific workflows, we propose an online estimation process based on the MAPE-K loop, where task executions are monitored and estimates are updated as more information becomes available. Experimental results show that our online estimation process results in much more accurate predictions than an offline approach, where all task requirements are estimated prior to workflow execution.
Biography: Rafael Ferreira da Silva is a Computer Scientist in the Collaborative Computing Group at the USC Information Sciences Institute. He received his PhD in Computer Science from INSA-Lyon, France, in 2013. In 2010, he received his Master's degree in Computer Science from Universidade Federal de Campina Grande, Brazil, and his BS degree in Computer Science from Universidade Federal da Paraiba, in 2007. His research focuses on the execution of scientific workflows on heterogeneous distributed systems such as clouds and grids. See http://www.rafaelsilva.com for further information.
Host: Computer Science Department
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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Seminars in Biomedical EngineeringMon, Jan 11, 2016 @ 12:30 PM - 01:49 PM
Alfred E. Mann Department of Biomedical Engineering
Conferences, Lectures, & Seminars
Talk Title: TBA
Host: K. Kirk Shung, PhD
Location: Olin Hall of Engineering (OHE) - 122
Audiences: Everyone Is Invited
Contact: Mischalgrace Diasanta
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PhD Defense - Yi Chang
Mon, Jan 11, 2016 @ 01:00 PM - 03:00 PM
Thomas Lord Department of Computer Science
University Calendar
Title: Time-Sensitive Social Media Mining and its Applications
PhD Candidate: Yi Chang
Date / Time: Jan. 11th (Monday), 1:00~3:00 PM
Place: SAL 213
Committee:
Prof. Yan Liu (Chair)
Prof. Cyrus Shahabi
Prof. Lian Jian (external)
Abstract
Social media is growing at an explosive rate and it becomes increasingly difficult for users to consume and digest useful information from massive and high-velocity data. To overcome this information overload problem, in this thesis, we have studied several key challenges, which could effectively re-structure and re-organize massive information on social media sites.
First, it is critical to effectively detect and model a burst of topics on social media, which is reflected by extremely frequent mentions of certain keywords in a short time interval. We propose a novel time-series modeling approach which captures the rise and fade temporal patterns via life cycle model, then invent a probabilistic graphical model to automatically discover inherent temporal patterns within a collection of buzz time-series. Second, as each individual tweet is short and lacks sufficient context information, users cannot effectively understand or consume information on Twitter, which can either make users less engaged or even detached from using Twitter. In order to provide informative context to a Twitter user, we initiate the task of Twitter cascade summarization, and propose a supervised learning framework with a set of novel features to generates a succinct summary from a large but noisy Twitter context cascade. Third, we address the challenge of timeline detection from social media, which is to detect a chain of spiking events in chronological order, and it can help social media users not only rediscover the most important historical events about entities but also understand the order and trends of those events. In order to capture the life circle patterns of events in timelines and combine temporal shapes with content information, we propose a novel probabilistic framework to effectively detect timelines of entities in social media. Finally, we address the challenge of timeline abstracting from social media, which is to detect a list of timeline events, and abstract each events with its representative social media post. In order to automatically identify the number of timeline events, we propose a non-parametric framework to effectively and efficiently detect and abstract timeline events. Gibbs sampling is employed to infer the model parameters, and a fast burn-in strategy based on temporal bursts is further introduced to speed up the model inference.
Location: Henry Salvatori Computer Science Center (SAL) - 213
Audiences: Everyone Is Invited
Contact: Lizsl De Leon
