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Conferences, Lectures, & Seminars
Events for January
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CS Seminar: Bo An (Nanyang Technological University, Singapore) - Computing Optimal Monitoring Strategy for Detecting Terrorist Plots
Thu, Jan 07, 2016 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Bo An, Nanyang Technological University, Singapore
Talk Title: Computing Optimal Monitoring Strategy for Detecting Terrorist Plots
Series: Teamcore Seminar
Abstract: In recent years, terrorist organizations (e.g., ISIS or al-Qaeda) are increasingly directing terrorists to launch coordinated attacks in their home countries. One example is the Paris shootings on January 7, 2015. By monitoring potential terrorists, security agencies are able to detect and stop terrorist plots at their planning stage. Although security agencies may have knowledge about potential terrorists (e.g., who they are, how they interact), they usually have limited resources and cannot monitor all terrorists. Moreover, a terrorist planner may strategically choose to arouse terrorists considering the security agency's monitoring strategy. This talk will discuss our contributions toward the challenging problem of computing optimal monitoring strategies: 1) A new Stackelberg game model for terrorist plot detection; 2) A modified double oracle framework for computing the optimal strategy effectively; 3) Complexity results for both defender and attacker oracle problems; 4) Novel mixed-integer linear programming (MILP) formulations for best response problems of both players; and 5) Effective approximation algorithms for generating suboptimal responses for both players.
Biography: Bo An is a Nanyang Assistant Professor with the School of Computer Engineering, Nanyang Technological University, Singapore. His current research interests include artificial intelligence, multiagent systems, game theory, and optimization. He has published over 40 referred papers at AAMAS, IJCAI, AAAI, ICAPS, JAAMAS and IEEE Transactions. Dr. An was the recipient of the 2010 IFAAMAS Victor Lesser Distinguished Dissertation Award, an Operational Excellence Award from the Commander, First Coast Guard District of the United States, the Best Innovative Application Paper Award at AAMAS'12, and the 2012 INFORMS Daniel H. Wagner Prize for Excellence in Operations Research Practice. He is a member of the Board of Directors of IFAAMAS and the Associate Editor of JAAMAS.
Host: Teamcore Group
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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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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CS Colloquium: Emilio Ferrara (USC ISI) - Predicting human behavior in techno-social systems: fighting abuse and illicit activities
Tue, Jan 12, 2016 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Emilio Ferrara, Information Sciences Institute
Talk Title: Predicting human behavior in techno-social systems: fighting abuse and illicit activities
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium.
The increasing availability of data across different socio-technical systems, such as online social networks, social media, and mobile phone networks, presents novel challenges and intriguing research opportunities. As more online services permeate through our everyday life and as data from various domains are connected and integrated with each other, the boundary between the real and the online worlds becomes blurry. Such data convey both online and offline activities of people, as well as multiple time scales and resolutions.
In this talk, I'll discuss my research efforts aimed at characterizing and predicting human behavior and activities in techno-social worlds: starting by discussing network structure and information spreading on large online social networks, I'll move toward characterizing entire online conversations, such as those around big real-world events, to capture the dynamics driving the emergence of collective attention and trending topics. I'll describe a machine learning framework leveraging these insights to detect promoted campaigns that mimic grassroots conversation. Aiming at learning the signature of abuse at the level of the single individuals, I'll illustrate the challenges posed by characterizing human activity as opposed to that of synthetic entities (social bots) that attempt emulate us, to persuade, smear, tamper or deceive. I'll draw a parallel with detecting illicit activities in the real world leveraging the traces left by criminals' interactions via mobile phones.
I'll conclude envisioning the design of computational systems that will help us making effective, timely decisions (informed by social data), and create actionable policies to contribute create a better future society.
Biography: Dr. Emilio Ferrara is a Computer Scientist at the USC's Information Sciences Institute. Ferrara's research interests include designing machine-learning systems to model and predict individual behavior in techno-social systems, characterize information diffusion and information campaigns, and predict crime and abuse in such environments. He has held research positions in institutions in Italy, Austria, and UK (2009-2012). Before joining USC in 2015, he was a Research Assistant Professor at the School of Informatics and Computing of Indiana University (2012-2015).
Ferrara earned a Ph.D. in Mathematics and Computer Science from University of Messina (Italy), and has published over 60 articles on machine learning, network science, and social media, appeared in top venues including PNAS, Communications of the ACM, Physical Review Letters, and several ACM and IEEE transactions and top conferences (WWW, CSCW, etc.). His research on social network abuse and crime prediction has been featured on the major news outlets (TIME, BBC, The New York Times, etc.) and tech magazines (MIT Technology Review, Vice, Mashable, New Scientist, etc). His research has been supported by DARPA, ONR, and IARPA.
Ferrara is Guest Editor of two special issues on network science and computational social sciences, published respectively on EPJ Data Science and Future Internet. He's member of the PC for conferences including ACM WWW, ICWSM, and SocInfo. Ferrara is co-chair of workshops recurring at ECCS, WWW, SocInfo, and WebScience; he was Local & Sponsor Chair of ACM Web Science 2014 and Publicity co-chair of SocInfo 2014. In 2015, Ferrara was named IBM Watson Analytics VIP Influential in Big Data.
Host: Computer Science Department
Location: Olin Hall of Engineering (OHE) - 136
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium & Yahoo! Labs Seminar: Jure Leskovec (Stanford) - Machine Learning for Human Decision Making
Tue, Jan 19, 2016 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Jure Leskovec, Stanford University
Talk Title: Machine Learning for Human Decision Making
Series: Yahoo! Labs Machine Learning Seminar Series
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
In many real-life settings human judges are making decisions and choosing among many alternatives in order to label or classify items: Medical doctor diagnosing a patient, criminal court judge making a decision, a crowd-worker labeling an image, and a student answering a multiple-choice question. Gaining insights into human decision making is important for determining the quality of individual decisions as well as identifying mistakes and biases. In this talk we discuss the question of developing machine learning methodology for estimating the quality of individual judges and obtaining diagnostic insights into how various judges decide on different kinds of items. We develop a series of increasingly powerful hierarchical Bayesian models which infer latent groups of judges and items with the goal of obtaining insights into the underlying decision process. We apply our framework to a wide range of real-world domains, and demonstrate that our approach can accurately predict judges decisions, diagnose types of mistakes judges tend to make, and infer true labels of items.
The lecture will be available to stream HERE. [For best quality, right click -> open in new tab]
Biography: Jure Leskovec is assistant professor of Computer Science at Stanford University and chief scientist at Pinterest. His research focuses on mining large social and information networks, their evolution, and the diffusion of information and influence over them. Computation over massive data is at the heart of his research and has applications in computer science, social sciences, economics, marketing, and healthcare. This research has won several awards including a Lagrange Prize, Microsoft Research Faculty Fellowship, Alfred P. Sloan Fellowship, and numerous best paper awards. Leskovec received his bachelor's degree in computer science from University of Ljubljana, Slovenia, and his PhD in in machine learning from the Carnegie Mellon University and postdoctoral training at Cornell University. You can follow him on Twitter @jure
Host: Yan Liu
More Info: http://www-bcf.usc.edu/~liu32/mlseminar.html
Webcast: https://bluejeans.com/469517570Location: Henry Salvatori Computer Science Center (SAL) - 101
WebCast Link: https://bluejeans.com/469517570
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
Event Link: http://www-bcf.usc.edu/~liu32/mlseminar.html
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CS Colloquium: Christopher Ré (Stanford) - DeepDive: A Dark Data System
Tue, Jan 26, 2016 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Christopher Ré, Stanford
Talk Title: DeepDive: A Dark Data System
Series: Yahoo! Labs Machine Learning Seminar Series
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
Many pressing questions in science are macroscopic, as they require scientists to integrate information from numerous data sources, often expressed in natural languages or in graphics; these forms of media are fraught with imprecision and ambiguity and so are difficult for machines to understand. Here I describe DeepDive, which is a new type of system designed to cope with these problems. It combines extraction, integration and prediction into one system. For some paleobiology and materials science tasks, DeepDive-based systems have surpassed human volunteers in data quantity and quality (recall and precision). DeepDive is also used by scientists in areas including genomics and drug repurposing, by a number of companies involved in various forms of search, and by law enforcement in the fight against human trafficking. DeepDive does not allow users to write algorithms; instead, it asks them to write only features. A key technical challenge is scaling up the resulting inference and learning engine, and I will describe our line of work in computing without using traditional synchronization methods including Hogwild! and DimmWitted. DeepDive is open source on github and available from DeepDive.Stanford.Edu.
The lecture will be available to stream HERE . (For best results, right click -> open in new tab).
Biography: Christopher (Chris) Re is an assistant professor in the Department of Computer Science at Stanford University and a Robert N. Noyce Family Faculty Scholar. His work's goal is to enable users and developers to build applications that more deeply understand and exploit data. Chris received his PhD from the University of Washington in Seattle under the supervision of Dan Suciu. For his PhD work in probabilistic data management, Chris received the SIGMOD 2010 Jim Gray Dissertation Award. He then spent four wonderful years on the faculty of the University of Wisconsin, Madison, before moving to Stanford in 2013. He helped discover the first join algorithm with worst-case optimal running time, which won the best paper at PODS 2012. He also helped develop a framework for feature engineering that won the best paper at SIGMOD 2014. In addition, work from his group has been incorporated into scientific efforts including the IceCube neutrino detector and PaleoDeepDive, and into Cloudera's Impala and products from Oracle, Pivotal, and Microsoft's Adam. He received an NSF CAREER Award in 2011, an Alfred P. Sloan Fellowship in 2013, a Moore Data Driven Investigator Award in 2014, the VLDB early Career Award in 2015, and the MacArthur Foundation Fellowship in 2015.
Host: Yan Liu
Webcast: https://bluejeans.com/745105731Location: Henry Salvatori Computer Science Center (SAL) - 101
WebCast Link: https://bluejeans.com/745105731
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
