Computational Science and Engineering Seminar Series
http://hdl.handle.net/1853/29805
Weekly seminars in computing and important computational problems that lie at the boundary between computer science and science and engineering2016-05-28T09:58:48ZThe Aha! Moment: From Data to Insight
http://hdl.handle.net/1853/51308
The Aha! Moment: From Data to Insight
Shahaf, Dafna
The amount of data in the world is increasing at incredible rates. Large-scale data has potential to transform almost every aspect of our world, from science to business; for this potential to be realized, we must turn data into insight. In this talk, I will describe two of my efforts to address this problem computationally. The first project, Metro Maps of Information, aims to help people understand the underlying structure of complex topics, such as news stories or research areas. Metro Maps are structured summaries that can help us understand the information landscape, connect the dots between pieces of information, and uncover the big picture. The second project proposes a framework for automatic discovery of insightful connections in data. In particular, we focus on identifying gaps in medical knowledge: our system recommends directions of research that are both novel and promising. I will formulate both problems mathematically and provide efficient, scalable methods for solving them. User studies on real-world datasets demonstrate that our methods help users acquire insight efficiently across multiple domains.
Presented on February 7, 2014 from 11:00 to 12:00 pm in room 1116 West of the Klaus Advanced Computing Building on the Georgia Tech campus.|Dafna Shahaf is a postdoctoral fellow at Stanford University. She received her Ph.D. from Carnegie Mellon University; prior to that, she earned an M.S. from the University of Illinois at Urbana-Champaign and a B.Sc. from Tel-Aviv University. Shahaf's research focuses on helping people make sense of massive amounts of data. She has won a best research paper award at KDD 2010, a Microsoft Research Fellowship, a Siebel Scholarship, and a Magic Grant for innovative ideas.; Runtime: 52:45 minutes.
2014-02-07T00:00:00ZShahaf, DafnaThe amount of data in the world is increasing at incredible rates. Large-scale data has potential to transform almost every aspect of our world, from science to business; for this potential to be realized, we must turn data into insight. In this talk, I will describe two of my efforts to address this problem computationally. The first project, Metro Maps of Information, aims to help people understand the underlying structure of complex topics, such as news stories or research areas. Metro Maps are structured summaries that can help us understand the information landscape, connect the dots between pieces of information, and uncover the big picture. The second project proposes a framework for automatic discovery of insightful connections in data. In particular, we focus on identifying gaps in medical knowledge: our system recommends directions of research that are both novel and promising. I will formulate both problems mathematically and provide efficient, scalable methods for solving them. User studies on real-world datasets demonstrate that our methods help users acquire insight efficiently across multiple domains.Cyber Games
http://hdl.handle.net/1853/46280
Cyber Games
Vorobeychik, Yevgeniy
Over the last few years I have been working on game theoretic models of security, with a particular emphasis on issues salient in cyber security. In this talk I will give an overview of some of this work. I will first spend some time motivating game theoretic treatment of problems relating to cyber and describe some important modeling considerations. In the remainder, I will describe two game theoretic models (one very briefly), and associated solution techniques and analyses. The first is the "optimal attack plan interdiction" problem. In this model, we view a threat formally as a sophisticated planning agent, aiming to achieve a set of goals given some specific initial capabilities and considering a space of possible "attack actions/vectors" that may (or may not) be used towards the desired ends. The defender's goal in this setting is to "interdict" a select subset of attack vectors by optimally choosing among miti-gation options, in order to prevent the attacker from being able to achieve its goals. I will describe the formal model, explain why it is challenging, and present highly scalable decomposition-based integer programming techniques that leverage extensive research into heuristic formal planning in AI. The second model addresses the problem that defense decisions are typically decentralized. I describe a model to study the impact of decentralization, and show that there is a "sweet spot": for an intermediate number of decision makers, the joint decision is nearly socially optimal, and has the additional benefit of being robust to the changes in the environment. Finally, I will describe the Secure Design Competition (FIREAXE) that involved two teams of interns during the summer of 2012. The problem that the teams were tasked with was to design a highly stylized version of an electronic voting system. The catch was that after the design phase, each team would attempt to "attack" the other's design. I will describe some salient aspects of the specification, as well as the outcome of this competition and lessons that we (the designers and the students) learned in the process.
Presented on February 19, 2013 from 11:00 to 12:00 pm in room 1456 of the Klaus Advanced Computing Building on the Georgia Tech campus.; Yevgeniy Vorobeychik is a Principal Member of Technical Staff at Sandia National Laboratories. Between 2008 and 2010 he was a post-doctoral research associate at the University of Pennsylvania Computer and Information Science department. He received Ph.D. (2008) and M.S.E. (2004) degrees in Computer Science and Engineering from the University of Michigan, and a B.S. degree in Computer Engineering from Northwestern University. His work focuses on game theoretic modeling of security, algorithmic and behavioral game theory and incentive design, optimization, complex systems, epidemic control, network economics, and machine learning. Dr. Vorobeychik has published over 50 research articles on these topics, including publications in top Computer Science, Operations Research, Business, and Physics journals and conferences. Dr. Vorobeychik was nominated for the 2008 ACM Doctoral Dissertation Award and received honorable mention for the 2008 IFAAMAS Distinguished Dissertation Award. In 2012 he was nominated for the Sandia Employee Recognition Award for Technical Excellence. He was also a recipient of a NSF IGERT interdisciplinary research fellowship at the University of Michigan, as well as a distinguished Computer Engineering undergraduate award at Northwestern University.; Runtime: 56:19 minutes.
2013-02-19T00:00:00ZVorobeychik, YevgeniyOver the last few years I have been working on game theoretic models of security, with a particular emphasis on issues salient in cyber security. In this talk I will give an overview of some of this work. I will first spend some time motivating game theoretic treatment of problems relating to cyber and describe some important modeling considerations. In the remainder, I will describe two game theoretic models (one very briefly), and associated solution techniques and analyses. The first is the "optimal attack plan interdiction" problem. In this model, we view a threat formally as a sophisticated planning agent, aiming to achieve a set of goals given some specific initial capabilities and considering a space of possible "attack actions/vectors" that may (or may not) be used towards the desired ends. The defender's goal in this setting is to "interdict" a select subset of attack vectors by optimally choosing among miti-gation options, in order to prevent the attacker from being able to achieve its goals. I will describe the formal model, explain why it is challenging, and present highly scalable decomposition-based integer programming techniques that leverage extensive research into heuristic formal planning in AI. The second model addresses the problem that defense decisions are typically decentralized. I describe a model to study the impact of decentralization, and show that there is a "sweet spot": for an intermediate number of decision makers, the joint decision is nearly socially optimal, and has the additional benefit of being robust to the changes in the environment. Finally, I will describe the Secure Design Competition (FIREAXE) that involved two teams of interns during the summer of 2012. The problem that the teams were tasked with was to design a highly stylized version of an electronic voting system. The catch was that after the design phase, each team would attempt to "attack" the other's design. I will describe some salient aspects of the specification, as well as the outcome of this competition and lessons that we (the designers and the students) learned in the process.Extending Hadoop to Support Binary-Input Applications
http://hdl.handle.net/1853/45211
Extending Hadoop to Support Binary-Input Applications
Hong, Bo
Many data-intensive applications naturally take multiple inputs, which is not well supported by some popular MapReduce implementations, such as Hadoop. In this talk, we present an extension of Hadoop to better support such applications. The extension is expected to provide the following benefits: (1) easy to program for such applications, (2) explores data localities better than native Hadoop, and (3) improves application performance.
Presented on October 19, 2012 from 2:00-3:00 pm in room 2447 of the Klaus Advanced Computing Building on the Georgia Tech campus.; Runtime: 55:36 minutes.
2012-10-19T00:00:00ZHong, BoMany data-intensive applications naturally take multiple inputs, which is not well supported by some popular MapReduce implementations, such as Hadoop. In this talk, we present an extension of Hadoop to better support such applications. The extension is expected to provide the following benefits: (1) easy to program for such applications, (2) explores data localities better than native Hadoop, and (3) improves application performance.Magnetic Resonance Imaging of the Brain
http://hdl.handle.net/1853/45212
Magnetic Resonance Imaging of the Brain
Hu, Xiaoping
Magnetic Resonance Imaging (MRI) has become a powerful, indispensable, and ubiquitously used methodology in neuroimaging. In particularly, functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) are two specific techniques which have broadly impacted the field. In this talk, I will briefly describe the bases and principles of these techniques and highlight several aspects of to data processing and analysis, including statistical analyses, support vector machine based classification, causal modelling and graph theoretic analysis.
Presented on October 12, 2012 from 2:00-3:00 pm in room 2447 of the Klaus Advanced Computing Building on the Georgia Tech campus.; Dr. Hu is a professor of biomedical engineering at Georgia Tech/Emory University and a Georgia Research Alliance eminent scholar in biomedical imaging. With a Ph.D. in medical physics from the University of Chicago, Dr. Hu has worked on the development and biomedical applications of magnetic resonance imaging/spectroscopy, particularly in the study of brain for almost 3 decades. Dr. Hu has authored or co-authored 200+ peer-reviewed journal articles. His work has been cited more than 10000 times. Among various contributions, he is recognized for his pioneering work on acquisition and analysis methods for functional magnetic resonance imaging (fMRI), including methods for removing physiological noise, ultrahigh field fMRI, real-time fMRI, and Granger causality analysis of fMRI data. In addition to neuroimaging, his current research interest also includes MR molecular imaging and in vivo MR detection of action potential.; Runtime: 53:35 minutes.
2012-10-12T00:00:00ZHu, XiaopingMagnetic Resonance Imaging (MRI) has become a powerful, indispensable, and ubiquitously used methodology in neuroimaging. In particularly, functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI) are two specific techniques which have broadly impacted the field. In this talk, I will briefly describe the bases and principles of these techniques and highlight several aspects of to data processing and analysis, including statistical analyses, support vector machine based classification, causal modelling and graph theoretic analysis.