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Conferences, Lectures, & Seminars
Events for October
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CS Colloquium: Raheem Beyah (Georgia Tech) - Password Security, Measurement, and Correlation Quantification
Thu, Oct 01, 2015 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Raheem Beyah, Georgia Tech
Talk Title: Password Security, Measurement, and Correlation Quantification
Series: CS Colloquium
Abstract: In this talk, results from a large-scale study on the crackability, correlation, and security of over 115 million real world passwords that were leaked from several popular Internet services and applications will be presented. Additionally, I will discuss a prototype system that provides a uniform comprehensive research platform for password security, measurement, and correlation quantification. Using this system, we analyze and evaluate 11 state-of-the-art password cracking algorithms, systematically and comprehensively evaluate these algorithms in multiple scenarios and identify their advantages and disadvantages. The system further consists of the implementation of 8 academic password meters, and 15 commercial password checkers/meters (both online and offline versions) from the top 150 websites. We identify that some commercial meters do little to guide users to select strong passwords, and often lead users to select vulnerable passwords. Additionally, a password correlation quantification framework will be presented, which is used to provide the correlation of different password datasets. Experimental results demonstrate that our quantification is consistent with the cracking results and existing observations. Finally, I will summarize and discuss future research directions (e.g., hybrid password cracking, social profile-aware/hybrid password meters) and challenges of password research.
The lecture will be available to stream HERE
Biography: Raheem Beyah, a native of Atlanta, Ga., is an Associate Professor in the School of Electrical and Computer Engineering at Georgia Tech where he leads the Communications Assurance and Performance Group (CAP) and is a member of the Institute for Information Security & Privacy (GTIISP) and the Communications Systems Center (CSC). Prior to returning to Georgia Tech, Dr. Beyah was an Assistant Professor in the Department of Computer Science at Georgia State University, a research faculty member with the Georgia Tech CSC, and a consultant in Andersen Consulting's (now Accenture) Network Solutions Group. He received his Bachelor of Science in Electrical Engineering from North Carolina A&T State University in 1998. He received his Masters and Ph.D. in Electrical and Computer Engineering from Georgia Tech in 1999 and 2003, respectively. Dr. Beyah has served as a Guest Editor for MONET and is currently an Associate Editor of the (Wiley) Wireless Communications and Mobile Computing Journal. His research interests include network security, wireless networks, network traffic characterization and performance, and critical infrastructure security. He received the National Science Foundation CAREER award in 2009 and was selected for DARPA's Computer Science Study Panel in 2010. He is a member of AAAS, ASEE, a lifetime member of NSBE, and a senior member of ACM and IEEE.
Host: CS Department
Webcast: https://bluejeans.com/156406552Location: Henry Salvatori Computer Science Center (SAL) - 101
WebCast Link: https://bluejeans.com/156406552
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Distinguished Lecture: Christos Papadimitriou (UC Berkeley) - Life Under the Lens
Tue, Oct 06, 2015 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Christos Papadimitriou, UC Berkeley
Talk Title: Life Under the Lens
Series: CS Distinguished Lectures
Abstract: Applying the algorithmic point of view to the natural, life, and social sciences often results in unexpected insights and progress in central problems, a mode of research that has been described as "the lens of computation." I will focus on examples in the life sciences, from joint work with Erick Chastain, Costis Daskalakis, Adi Livnat, Umesh Vazirani, Santosh Vempala, and Albert Wu: Evolution of a population through sexual reproduction can be rethought of as a repeated game between genes played through the multiplicative weight updates algorithm. In an infinite population, when selection acts not on genes alone but on pairs of genes, fixation can take exponentially many generations. And a neurally plausible device can be the basis of spontaneous unsupervised learning.
This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium. The lecture can be screened HERE.
Biography: Christos H. Papadimitriou is the C. Lester Hogan Professor of Computer Science at UC Berkeley. Before joining Berkeley in 1996, he taught at Harvard, MIT, NTU Athens, Stanford, and UCSD. He has written five textbooks and many articles on algorithms and complexity, and their applications to optimization, databases, control, AI, robotics, economics and game theory, the Internet, evolution, and the brain. He holds a PhD from Princeton, and eight honorary doctorates. He is a member of the National Academy of Sciences of the US, the American Academy of Arts and Sciences, and the National Academy of Engineering. He has also written three novels: "Turing," "Logicomix" (with Apostolos Doxiadis) and "Independence" (in Greek).
Host: Computer Science Department
Webcast: https://bluejeans.com/548743552Location: Henry Salvatori Computer Science Center (SAL) - 101
WebCast Link: https://bluejeans.com/548743552
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Seminar: Ece Kamar (Microsoft Research) - Towards Hybrid Systems for Combining and Machine and Human Intelligence
Mon, Oct 12, 2015 @ 03:00 PM - 04:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Ece Kamar, Microsoft Research Redmond
Talk Title: Towards Hybrid Systems for Combining and Machine and Human Intelligence
Series: CS Seminar Series
Abstract: Despite advances in Artificial Intelligence, computer systems still have limitations in accomplishing tasks that come naturally to humans such as making sense of images or carrying out a successful dialog. Having easy and on demand access to human intelligence through crowdsourcing offers us a new type of resource to train, improve and complement intelligent systems. However, this resource comes with a new set of challenges. Making human computation a reliable component of intelligent systems requires moving away from manual designs and controls towards generalizable automation techniques, algorithms, models and designs. In this talk, I will present an overview of our recent research efforts towards this goal focusing both on using machine intelligence for reliable crowdsourcing and on developing AI systems that can benefit from having humans in the loop.
I will start by showing how machine learning and decision-theoretic reasoning can be used in harmony to leverage the complementary strengths of humans and computational agents to solve crowdsourcing tasks efficiently. Next, I will present an overview of different studies towards maintaining reliable access to human intelligence. I will conclude the talk by discussing opportunities and challenges in using crowdsourcing to power and improve AI systems.
Biography: Ece Kamar is a researcher at the Adaptive Systems and Interaction group at Microsoft Research Redmond. Ece earned her Ph.D. in computer science from Harvard University. While at Harvard, she received the Microsoft Research fellowship and Robert L. Wallace Prize Fellowship for her work on Artificial Intelligence. She currently serves in the program committee of conferences such as AAAI, AAMAS, IJCAI, WWW, UAI and HCOMP. Her research interests include human-computer collaboration, decision-making under uncertainty, probabilistic reasoning and mechanism design with a focus on real-world applications that bring people and adaptive agents together.
Host: Teamcore Research Group
Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Student Colloquium: Fei Fang (USC) - Towards Addressing Spatio-Temporal Aspects in Security Games
Tue, Oct 13, 2015 @ 04:00 PM - 05:15 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Fei Fang, USC
Talk Title: Towards Addressing Spatio-Temporal Aspects in Security Games
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
My research aims to provide game-theoretic solutions for fundamental challenges of security resource optimization in the real-world, in domains ranging from infrastructure protection to sustainable development. Whereas first generation of "security games" research provided algorithms for optimizing security resources in mostly static settings, my thesis advances the state-of-the-art to a new generation of security games, handling massive games with complex spatio-temporal settings and leading to real-world applications that have fundamentally altered current practices of security resource allocation. My work provides the first algorithms and models for advancing three key aspects of spatio-temporal challenges in security games. First, focusing on games where actions are taken over continuous time (for example games with moving targets such as ferries and refugee supply lines), I provide an efficient linear-programming-based solution while accurately modeling the attacker's continuous strategy. This work has been deployed by the US Coast Guard for protecting the Staten Island Ferry in New York City in past few years and fundamentally altering previously used tactics. Second, for games where actions are taken over continuous space (for example games with forest land as target), I provide an algorithm computing the optimal distribution of patrol effort. Third, my work addresses challenges with one key dimension of complexity -- the temporal change of strategy. Motivated by the repeated interaction of players in domains such as preventing poaching and illegal fishing, I introduce a novel game model that accounts for temporal behavior change of opponents and provide algorithms to plan effective sequential defender strategies. Furthermore, I incorporate complex terrain information and design the PAWS application to combat illegal poaching, which generates patrol plans with detailed patrol routes for local patrollers. PAWS has been deployed in a protected area in Southeast Asia, with plans for worldwide deployment.
The lecture will be available to stream HERE.
Biography: Fei Fang is a PhD candidate in Department of Computer Science at University of Southern California. She is working with Professor Milind Tambe at Teamcore Research group. She received her bachelor degree from the Department of Electronic Engineering, Tsinghua Unviersity in July, 2011.
Her research lies in the field of artificial intelligence and multi-agent systems, focusing on computational game theory with applications to security and sustainability domains. Her work has won the Outstanding Paper Award at the International Joint Conferences on Artificial Intelligence (IJCAI), Computational Sustainability Track (2015) and was a finalist for poster competition in the First Conference on Validating Models of Adversary Behaviors (2013). She is the recipient of WiSE Merit Fellowship (2014) and she has been awarded the Meritorious Team Commendation from Commandant of the US Coast Guard and Flag Letter of Appreciation from Vice Admiral. She is the chair of the AAAI Spring Symposium 2015 on Applied Computational Game Theory. Her work on "Protecting Moving Targets with Mobile Resources" has been deployed by the US Coast Guard for protecting the Staten Island Ferry in New York City since April 2013. Her work on designing patrol strategies to combat illegal poaching has leads to the deployment of PAWS application in a conservation area in Southeast Asia for protecting tigers.
Host: Computer Science Department
Webcast: https://bluejeans.com/331768755Location: Henry Salvatori Computer Science Center (SAL) - 101
WebCast Link: https://bluejeans.com/331768755
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Xifeng Yan (UC Santa Barbara) - Graph Analysis and Search in Networks
Thu, Oct 15, 2015 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Xifeng Yan, University of California at Santa Barbara
Talk Title: Graph Analysis and Search in Networks
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
In this talk, I will first give an overview about graph data mining and data management studies conducted in my lab and then introduce two projects related to analyzing and searching collaborative and information networks. Collaborative networks are composed of experts who cooperate with each other to complete specific tasks, such as resolving problems reported by customers. We attempt to deduce the cognitive process of task routing and model the decision making of experts. We formalize multiple routing patterns by taking into account both rational and random analysis of tasks, and present a generative model to combine them.
In the second part of my talk, I will show the challenge of querying complex graphs such as knowledge graphs and introduce a novel framework enabling schemaless graph querying (SLQ), where a user need not describe queries precisely as required by SQL. I will also brief our new progress in benchmarking graph queries.
This lecture will be available to stream HERE.
Biography: Xifeng Yan is an associate professor at the University of California, Santa Barbara. He holds the Venkatesh Narayanamurti Chair of Computer Science. He has been working on modeling, managing, and mining graphs in information networks, computer systems, social media and bioinformatics. He received NSF CAREER Award, IBM Invention Achievement Award, ACM-SIGMOD Dissertation Runner-Up Award, and IEEE ICDM 10-year Highest Impact Paper Award. He received his Ph.D. from the University of Illinois at Urbana-Champaign in 2006 and was a research staff member at the IBM T. J. Watson Research Center between 2006 and 2008.
Host: Yan Liu
Webcast: https://bluejeans.com/543217029Location: Henry Salvatori Computer Science Center (SAL) - 101
WebCast Link: https://bluejeans.com/543217029
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Student Colloquium: Benjamin Ford (USC) - Beware the Soothsayer: From Attack Prediction Accuracy to Predictive Reliability in Security Games
Tue, Oct 20, 2015 @ 04:00 PM - 05:15 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Benjamin Ford , USC
Talk Title: Beware the Soothsayer: From Attack Prediction Accuracy to Predictive Reliability in Security Games
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
Interdicting the flow of illegal goods (such as drugs and ivory) is a major security concern for many countries. The massive scale of these networks, however, forces defenders to make judicious use of their limited resources. While existing solutions model this problem as a Network Security Game (NSG), they do not consider humans' bounded rationality. Previous human behavior modeling works in Security Games, however, make use of large training datasets that are unrealistic in real-world situations; the ability to effectively test many models is constrained by the time-consuming and complex nature of field deployments. In addition, there is an implicit assumption in these works that a model's prediction accuracy strongly correlates with the performance of its corresponding defender strategy (referred to as predictive reliability). If the assumption of predictive reliability does not hold, then this could lead to substantial losses for the defender. In the following paper, we (1) first demonstrate that predictive reliability is indeed strong for previous Stackelberg Security Game experiments. We also run our own set of human subject experiments in such a way that models are restricted to learning on dataset sizes representative of real world constraints. In the analysis on that data, we demonstrate that (2) predictive reliability is extremely weak for NSGs. Following that discovery, however, we identify (3) key factors that influence predictive reliability results: the training set's exposed attack surface and graph structure.
This lecture will be available to stream HERE.
Biography: Ben is a third year PhD student of Computer Science at the University of Southern California's Viterbi School of Engineering. He joined Teamcore in August 2013 and is advised by Professor Milind Tambe. Previously, he completed his B.S. and M.S. in Computer Science at the University of Massachusetts Dartmouth in 2008 and 2010, respectively. After graduation and prior to joining Teamcore, he worked at the Naval Undersea Warfare Center in Newport, RI as a Software Engineer. His primary research interests are in the application of concepts from the social sciences of Psychology, Criminology, Sociology, and Anthropology to improve the algorithms and solutions of Computer Science. Specifically, he is interested in applying human behavioral models to multi-agent systems with a large focus on human decision making. Since joining Teamcore, he has developed an interest in applying Behavioral Game Theory to the Wildlife Conservation domain to prevent wildlife poaching and smuggling.
Host: Computer Science Department
Webcast: https://bluejeans.com/846279055Location: Henry Salvatori Computer Science Center (SAL) - 101
WebCast Link: https://bluejeans.com/846279055
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Lihong Li (Microsoft Research) - Taming the Monster: Provably Efficient Algorithms for Contextual Bandits with General Policy Classes
Thu, Oct 22, 2015 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Lihong Li, Microsoft Research
Talk Title: Provably Efficient Algorithms for Contextual Bandits with General Policy Classes
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
We consider contextual bandit problems, where in each round the learner takes one of K actions in response to the observed context, and observes the reward only for that chosen action. In the first part of the talk, we focus on the standard setting, where the challenge is to efficiently balance exploration/exploitation to maximize total rewards (equivalently, minimize total regret) in T rounds, a problem commonly encountered in many important interaction problems like advertising and recommendation. Our algorithm assumes access to an oracle for solving a form of classification problems and achieves the statistically optimal regret guarantee with a small number of oracle calls across T rounds. The resulting algorithm is the most practical one amongst contextual-bandit algorithms that work for general policy classes. In the second part of the talk, we show how the above general algorithmic idea can be adapted to contextual bandits with global convex constraints and concave objective functions, a setting that is substantially harder and is important in many applications. Joint work with Alekh Agarwal, Shipra Agrawal, Nikhil R. Devanur, Daniel Hsu, Satyen Kale, John Langford, and Robert E. Schapire.
This lecture will be available to stream HERE.
Biography: Lihong Li is a Researcher in the Machine Learning Department at Microsoft Research-Redmond. Prior to joining Microsoft, he was a Research Scientist in the Machine Learning Group at Yahoo! Research in Silicon Valley. He obtained a PhD degree from Rutgers University in Computer Science. His main research interests are machine learning with interaction, including reinforcement learning, multi-armed bandits, online learning, and their applications especially those on the Internet like recommender systems, search, and advertising. He has served as area chair or senior program committee member at ICML, NIPS, and IJCAI.
Host: Yan Liu
Webcast: https://bluejeans.com/350859861Location: Henry Salvatori Computer Science Center (SAL) - 101
WebCast Link: https://bluejeans.com/350859861
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Seminar: Dr. Hoa Khanh Dam (University of Wollongong) - Predicting delays in software projects using networked classification
Mon, Oct 26, 2015 @ 11:00 AM - 12:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Dr. Hoa Khanh Dam, University of Wollongong, Australia
Talk Title: Predicting delays in software projects using networked classification
Series: CS Seminar Series
Abstract: One of the challenges in (software) project management is to make reliable prediction of delays in the context of constant and rapid changes inherent in (software) projects. In this talk, I will present our recent work in data-driven software engineering to provide automated support for project managers and other decision makers in predicting whether a subset of software tasks (among the hundreds to thousands of ongoing tasks) in a software project have a risk of being delayed. Our approach makes use of not only features specific to individual software tasks (i.e. local data) - as done in previous work - but also their relationships (i.e. networked data). In addition, using collective classification, our approach can simultaneously predict the degree of delay for a group of related tasks. Our evaluation results show a significant improvement over traditional approaches which perform classification on each task independently.
Biography: Dr Hoa Khanh Dam is a Senior Lecturer at the School of Computing and Information Technology, University of Wollongong, Australia. He holds PhD and Master degrees in Computer Science from RMIT University, and Bachelor of Computer Science degree from the University of Melbourne in Australia. His work has won multiple Best Paper Awards (at WICSA, APCCM, and ASWEC) and ACM SIGSOFT Distinguished Paper Award (at MSR). His research has been published in the top venues in software engineering (ICSE, ASE, ER), AI/intelligent agents (AAMAS, JAAMAS), and service-oriented computing (ICSOC and BPM). He served as Program Co-Chair for the 17th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA) in 2014. Other major international conferences that he has been involved with include AAMAS (PC), ICSOC 2015 (Publication Chair and PC), ASWEC and EDOC 2015 (Publicity Chair). Prior to his academic career, he spent a number of years in the industry at various positions, including technical architect, project manager and software engineer. He is Associate Director for the Decision Systems Lab at the University of Wollongong. His research interests span across a number of areas in data-driven Software Engineering (e.g. applications of data mining and machine learning into software engineering), model-driven development and evolution, agent-oriented software engineering, service-oriented engineering and business process management.
Host: Teamcore Group
Location: Kaprielian Hall (KAP) - 144
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Yisong Yue (Caltech) - A Decision Tree Framework for Data-Driven Speech Animation
Tue, Oct 27, 2015 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Yisong Yue, Caltech
Talk Title: A Decision Tree Framework for Data-Driven Speech Animation
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
In many animation projects, the animation artist typically spends significant time animating the face. This process involves many labor-intensive tasks that offer relatively little potential for creative expression. One particularly tedious task is speech animation: animating the face to match spoken audio. Indeed, the often prohibitive cost of speech animation has limited the types of animations that are feasible, including localization to different languages.
In this talk, I will show how to view speech animation through the lens of data-driven sequence prediction. In contrast to previous sequence prediction settings, visual speech animation is an instance of contextual spatiotemporal sequence prediction, where the output is continuous and high-dimensional (e.g., a configuration of the lower face), and also depends on an input context (e.g., audio or phonetic input).
I will present a decision tree framework for learning to generate context-dependent spatiotemporal sequences given training data. This approach enjoys several attractive properties, including ease of training, fast performance at test time, and the ability to robustly tolerate corrupted training data using a novel latent variable approach. I will showcase this approach in a case study on speech animation, where our approach outperforms several competitive baselines in both quantitative and qualitative evaluations, and also demonstrates strong robustness to corrupted training data.
This is joint work with Taehwan Kim, Sarah Taylor, Barry-John Theobald and Iain Matthews.
The lecture will be available to stream HERE.
Host: Yan Liu
Webcast: https://bluejeans.com/535615811Location: Henry Salvatori Computer Science Center (SAL) - 101
WebCast Link: https://bluejeans.com/535615811
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
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CS Colloquium: Nina Balcan (CMU) - Learning Submodular Functions
Thu, Oct 29, 2015 @ 04:00 PM - 05:00 PM
Thomas Lord Department of Computer Science
Conferences, Lectures, & Seminars
Speaker: Nina Balcan , Carnegie Mellon University
Talk Title: Learning Submodular Functions
Series: CS Colloquium
Abstract: This lecture satisfies requirements for CSCI 591: Computer Science Research Colloquium
Submodular functions are discrete functions that model laws of diminishing returns and enjoy numerous applications in many areas, including algorithmic game theory, machine learning, and social networks. For example, submodular functions are commonly used to model valuation functions for bidders in auctions, and the influence of various subsets of agents in social networks. Traditionally it is assumed that these functions are known to the decision maker; however, for large scale systems, it is often the case they must be learned from observations.
In this talk, I will discuss a recent line of work on studying the learnability of submodular functions. I will describe general upper and lower bounds on the learnability of such functions that yield novel structural results about them of interest to many areas. I will also discuss even better guarantees that can be achieved for important classes that exhibit additional structure. These classes include probabilistic coverage functions that can be used to model the influence function in classic models of information diffusion in networks and functions with bounded complexity used in modeling bidder valuation functions in auctions.
I will also discuss an application of our algorithms for learning the influence functions in social networks, that outperforms existing approaches empirically in both synthetic and real world data.
This event will be available to stream HERE.
Biography: Maria-Florina Balcan is an Associate Professor in the School of Computer Science at Carnegie Mellon University. Her main research interests are machine learning, computational aspects in economics and game theory, and algorithms. Her honors include the CMU SCS Distinguished Dissertation Award, an NSF CAREER Award, a Microsoft Faculty Research Fellowship, a Sloan Research Fellowship, and several paper awards. She was a Program Committee Co-chair for COLT 2014, and is currently a board member of the International Machine Learning Society and a Program Committee Co-chair for ICML 2016.
Host: Yan Liu
Location: Henry Salvatori Computer Science Center (SAL) - 101
Audiences: Everyone Is Invited
Contact: Assistant to CS chair
