SUNMONTUEWEDTHUFRISAT
Events for March 25, 2014
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Epstein ISE Department Seminar
Tue, Mar 25, 2014 @ 10:00 AM - 11:00 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Dr. Ali E. Abbas, Art Davis Faculty Scholar, Department of Industrial and Enterprise Systems Engineering, University of Illinois at Urbana-Champaign
Talk Title: "Bracing for Today's National Security Decisions"
Abstract: Some of the significant features of our era include the prevalence of large-scale systems; advances in artificial intelligence, medicine and public policy; the role of social networks in predicting behavior and toppling governments, and the presence of multiple stakeholders with multiple objectives. Amidst these features lie eminent possibilities of cyber and biological attacks that pose significant threats to our infrastructure; liberty and pursuit of happiness. To remain ahead, National security policies within this era must proactively capitalize on advancements in different disciplines using multidisciplinary teams that can operate together coherently and communicate their results to policy makers.
In this talk, I will show examples of how tools derived in different disciplines can be unified into a theory of decision making that enhances national security decisions. These tools include connections between advances in utility theory (economics) and information theory to better predict adversary behavior; connections between the recent advances in multiattribute utility theory and controls theory for better mechanisms of information gathering with unmanned vehicles in hostile environments; ongoing work on connections between social networks and public policy, and essential decision analysis tools tailored to guide planetary defense missions conducted by NASA.
TUESDAY, MARCH 25, 2014
RONALD TUTOR HALL (RTH) ROOM 526
10:00 - 11:00 AM
Biography: Ali E. Abbas received an M.S. in electrical engineering; M.S. in engineering economic systems & operations research; PhD in management science and engineering, and PhD minor in electrical engineering all from the school of engineering at Stanford University. His research interests include all aspects of decision making under uncertainty (broadly defined), information theory, signal processing, artificial intelligence, and bioinformatics. He is co-author of two forthcoming books ââ¬ÅThe Foundations of Decision Analysisââ¬Â with Ronald Howard, and the single-author book ââ¬Åthe Foundations of Multiattribute Utilityââ¬Â. He is also an associate Editor for both the Operations Research and Decision Analysis journals of INFORMS and is the decision analysis area editor for IIE Transactions.
Dr. Abbas is the Art Davis Faculty Scholar in the Department of Industrial and Enterprise Systems Engineering at the University of Illinois at Urbana-Champaign. He previously worked as a Lecturer in the Department of Management Science and Engineering at Stanford University and in Schlumberger Oilfield Services, where he held several international positions in Wireline logging, operations management, and international training. He has organized numerous workshops including the decision analysis tracks of INFORMS 2007, 2008 and the Bayesian inference and Maximum Entropy conference in 2005. He received numerous National Science Foundation Awards including the National Science Foundation Career Award in 2008; the Decision Analysis Society of INFORMS (DAS) Best Publication Award in 2011; the National Science Foundation I-Corps award in 2012, and the Decision Analysis Society of INFORMS first runner up and second runner up publication awards in 2013. Dr. Abbasââ¬â¢ work has been featured in numerous media outlets including CBS, The Huffington Post, the WSJ, the National Science Foundation (NSF) discoveries, INFORMS Podcasts, and he has had a recent TV appearance on ââ¬ÅChicago Tonightââ¬Â. Dr. Abbas has also been invited to attend economic policy discussions on social welfare at the Houses of Parliament.
Host: Daniel J. Epstein Department of Industrial and Systems Engineering
More Information: Seminar-Abbas.doc
Location: Ronald Tutor Hall of Engineering (RTH) - 526
Audiences: Everyone Is Invited
Contact: Georgia Lum
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Epstein Institute / ISE 651 Seminar Series
Tue, Mar 25, 2014 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Jordan B. L. Smith, Ph.D. Candidate, Centre for Digital Music, Queen Mary University of London
Talk Title: "Using Large Datasets to Understand the Perception of Structure in Music"
Series: Epstein Institute Seminar Series
Abstract: The perception of grouping structure in music is one of the most fundamental and yet poorly understood aspects of listening. Grouping structure refers to how a listener divides a sequence of sounds into segments, and groups these segments together recursively. This process is somewhat automatic at the shortest timescales, but modelling how the mind forms larger groups is a formidable challenge. I will present two projects that seek to improve our understanding of what musical attributes listeners are most likely to focus on.
The first is a study of the correlation between acoustic changes and the perception of boundaries, and is based on an analysis of SALAMI, a large collection of structural annotations. While datasets like this are generally used for evaluating analysis algorithms, we have repurposed SALAMI to study the inverse problem: deducing how listeners interpret acoustic signals as structured events. We computed smoothed differential functions of a number of musical features and observed how often moments of change coincided with boundaries and non-boundaries. Reinforcing and extending results from psychological experiments, we found that a change in some musical feature is a necessary but not sufficient condition for a point in time to be considered a boundary, and that the number of simultaneous changes in different musical features correlates with the salience of the boundary.
In the second project, we developed a tool that seeks to identify the acoustic parameters a listener was plausibly focusing on when they analyzed the piece. Our approach uses multiple self-similarity matrices, which are often used to detect repeated patterns for music structure analysis. Using Quadratic Programming, we find the optimal piece-wise combination of matrices to reproduce the listener's analysis, resulting in a time-series estimate of the listener's attentional focus. Examples illustrate many aspects of listener disagreements, such as the origin and independent plausibility of conflicting interpretations.
TUESDAY, MARCH 25, 2014
VON KLEINSMID CENTER (VKC) ROOM 100
3:30 - 4:50 PM
Biography: Jordan B. L. Smith is a Ph.D. candidate at the Centre for Digital Music at Queen Mary University of London, studying with Prof. Elaine Chew. He received his M.Sc. in operations research engineering in 2012 at University of Southern California (Los Angeles, CA, USA), his M.A. in music technology in 2010 at McGill University (Montreal, QC, Canada), and in 2006 his A.B. in music and physics at Harvard College (Cambridge, MA, USA).
As a research assistant at McGill, he planned and implemented the collection of ground truth for the Structural Analysis of Large Amounts of Music Information (SALAMI) project. His current research, which focuses on differences among listeners in the perception of musical structure, has been published in IEEE Transactions on Multimedia and at the ACM Conference on Multimedia, and he has delivered talks on the subject at the Society for Music Perception and Cognition and at the Digital Music Research Network.
In 2012, Smith was awarded doctoral fellowships from both the Social Sciences and Humanities Research Council of Canada and the Fonds de recherche du Québec; both agencies also awarded him a masterââ¬â¢s fellowship in 2009. He was awarded a Provostââ¬â¢s Ph.D. fellowship from the University of Southern California in 2010.
Host: Daniel J. Epstein Department of Industrial and Systems Engineering
More Information: Seminar-Smith_Jordan.doc
Location: Von Kleinsmid Center For International & Public Affairs (VKC) - Room 100
Audiences: Everyone Is Invited
Contact: Georgia Lum
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Epstein Institute / ISE 651 Seminar Series
Tue, Mar 25, 2014 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Jordan B. L. Smith, Ph.D. Candidate, Centre for Digital Music, Queen Mary University of London
Talk Title: "Using Large Datasets to Understand the Perception of Structure in Music"
Series: Epstein Institute Seminar Series
Abstract: The perception of grouping structure in music is one of the most fundamental and yet poorly understood aspects of listening. Grouping structure refers to how a listener divides a sequence of sounds into segments, and groups these segments together recursively. This process is somewhat automatic at the shortest timescales, but modelling how the mind forms larger groups is a formidable challenge. I will present two projects that seek to improve our understanding of what musical attributes listeners are most likely to focus on.
The first is a study of the correlation between acoustic changes and the perception of boundaries, and is based on an analysis of SALAMI, a large collection of structural annotations. While datasets like this are generally used for evaluating analysis algorithms, we have repurposed SALAMI to study the inverse problem: deducing how listeners interpret acoustic signals as structured events. We computed smoothed differential functions of a number of musical features and observed how often moments of change coincided with boundaries and non-boundaries. Reinforcing and extending results from psychological experiments, we found that a change in some musical feature is a necessary but not sufficient condition for a point in time to be considered a boundary, and that the number of simultaneous changes in different musical features correlates with the salience of the boundary.
In the second project, we developed a tool that seeks to identify the acoustic parameters a listener was plausibly focusing on when they analyzed the piece. Our approach uses multiple self-similarity matrices, which are often used to detect repeated patterns for music structure analysis. Using Quadratic Programming, we find the optimal piece-wise combination of matrices to reproduce the listenerââ¬â¢s analysis, resulting in a time-series estimate of the listener's attentional focus. Examples illustrate many aspects of listener disagreements, such as the origin and independent plausibility of conflicting interpretations.
TUESDAY, MARCH 25, 2014
VON KLEINSMID CENTER (VKC) ROOM 100
3:30 - 4:50 PM
Biography: Jordan B. L. Smith is a Ph.D. candidate at the Centre for Digital Music at Queen Mary University of London, studying with Prof. Elaine Chew. He received his M.Sc. in operations research engineering in 2012 at University of Southern California (Los Angeles, CA, USA), his M.A. in music technology in 2010 at McGill University (Montreal, QC, Canada), and in 2006 his A.B. in music and physics at Harvard College (Cambridge, MA, USA).
As a research assistant at McGill, he planned and implemented the collection of ground truth for the Structural Analysis of Large Amounts of Music Information (SALAMI) project. His current research, which focuses on differences among listeners in the perception of musical structure, has been published in IEEE Transactions on Multimedia and at the ACM Conference on Multimedia, and he has delivered talks on the subject at the Society for Music Perception and Cognition and at the Digital Music Research Network.
In 2012, Smith was awarded doctoral fellowships from both the Social Sciences and Humanities Research Council of Canada and the Fonds de recherche du Québec; both agencies also awarded him a master's fellowship in 2009. He was awarded a Provost's Ph.D. fellowship from the University of Southern California in 2010.
Host: Daniel J. Epstein Department of Industrial and Systems Engineering
More Information: Seminar-Smith_Jordan.doc
Location: Room 100
Audiences: Everyone Is Invited
Contact: Georgia Lum
-
Epstein Institute / ISE 651 Seminar Series
Tue, Mar 25, 2014 @ 03:30 PM - 04:50 PM
Daniel J. Epstein Department of Industrial and Systems Engineering
Conferences, Lectures, & Seminars
Speaker: Jordan B. L. Smith, Ph.D. Candidate, Centre for Digital Music, Queen Mary University of London
Talk Title: "Using Large Datasets to Understand the Perception of Structure in Music"
Series: Epstein Institute Seminar Series
Abstract: The perception of grouping structure in music is one of the most fundamental and yet poorly understood aspects of listening. Grouping structure refers to how a listener divides a sequence of sounds into segments, and groups these segments together recursively. This process is somewhat automatic at the shortest timescales, but modelling how the mind forms larger groups is a formidable challenge. I will present two projects that seek to improve our understanding of what musical attributes listeners are most likely to focus on.
The first is a study of the correlation between acoustic changes and the perception of boundaries, and is based on an analysis of SALAMI, a large collection of structural annotations. While datasets like this are generally used for evaluating analysis algorithms, we have repurposed SALAMI to study the inverse problem: deducing how listeners interpret acoustic signals as structured events. We computed smoothed differential functions of a number of musical features and observed how often moments of change coincided with boundaries and non-boundaries. Reinforcing and extending results from psychological experiments, we found that a change in some musical feature is a necessary but not sufficient condition for a point in time to be considered a boundary, and that the number of simultaneous changes in different musical features correlates with the salience of the boundary.
In the second project, we developed a tool that seeks to identify the acoustic parameters a listener was plausibly focusing on when they analyzed the piece. Our approach uses multiple self-similarity matrices, which are often used to detect repeated patterns for music structure analysis. Using Quadratic Programming, we find the optimal piece-wise combination of matrices to reproduce the listenerââ¬â¢s analysis, resulting in a time-series estimate of the listenerââ¬â¢s attentional focus. Examples illustrate many aspects of listener disagreements, such as the origin and independent plausibility of conflicting interpretations.
TUESDAY, MARCH 25, 2014
VON KLEINSMID CENTER (VKC) ROOM 100
3:30 - 4:50 PM
Biography: Jordan B. L. Smith is a Ph.D. candidate at the Centre for Digital Music at Queen Mary University of London, studying with Prof. Elaine Chew. He received his M.Sc. in operations research engineering in 2012 at University of Southern California (Los Angeles, CA, USA), his M.A. in music technology in 2010 at McGill University (Montreal, QC, Canada), and in 2006 his A.B. in music and physics at Harvard College (Cambridge, MA, USA).
As a research assistant at McGill, he planned and implemented the collection of ground truth for the Structural Analysis of Large Amounts of Music Information (SALAMI) project. His current research, which focuses on differences among listeners in the perception of musical structure, has been published in IEEE Transactions on Multimedia and at the ACM Conference on Multimedia, and he has delivered talks on the subject at the Society for Music Perception and Cognition and at the Digital Music Research Network.
In 2012, Smith was awarded doctoral fellowships from both the Social Sciences and Humanities Research Council of Canada and the Fonds de recherche du Québec; both agencies also awarded him a masterââ¬â¢s fellowship in 2009. He was awarded a Provostââ¬â¢s Ph.D. fellowship from the University of Southern California in 2010.
Host: Daniel J. Epstein Department of Industrial and Systems Engineering
More Information: Seminar-Smith_Jordan.doc
Location: Von Kleinsmid Center For International & Public Affairs (VKC) - Room 100
Audiences: Everyone Is Invited
Contact: Georgia Lum
