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Mario  Sznaier, Ph.D. - Global Resilience Institute. Boston, MA, UNITED STATES

Mario Sznaier, Ph.D.

Dennis Picard Trustee Professor, Electrical and Computer Engineering; Affiliated Faculty, Bioengineering, Northeastern University | Faculty Affiliate, Global Resilience Institute

Boston, MA, UNITED STATES

Professor Sznaier focuses on engineering, robust control information extraction from high volume data stream, and reduced order models.

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Mario  Sznaier, Ph.D. Publication

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Biography

Mario Sznaier received the Ingeniero Electronico and Ingeniero en Sistemas de Computacion degrees from the Universidad de la Republica, Uruguay and the MSEE and Ph.D degrees from the University of Washington. From 1991 to 1993 he was an Assistant Professor of Electrical Engineering at the University of Central Florida. In 1993 he joined the Pennsylvania State University, where he was promoted to Associate Professor in 1997 and to Professor of Electrical Engineering in 2001. In July 2006 he joined the Electrical and Computer Engineering Department at Northeastern University, Boston, MA, as the Dennis Picard Trustee Professor. He has also held visiting appointments at the California Institute of Technology in 1990 and 2000 and currently holds an appointment at Penn State as Adjunct Professor of Electrical Engineering. His research interest include Multiobjective Robust Control; Dynamic Vision and Imaging, Control Oriented Identification, Robust Model (In) Validation and Application of Dynamical Systems Theory to Physics. He is currently serving as an Associate Editor for the journal Automatica and as a member of the board of governors of the IEEE Control Systems Society.

Areas of Expertise (4)

Reduced Order Models

Robust Control

Tools and Measurement/Design and Engineering

Information Extraction from High Volume Data Streams

Accomplishments (1)

Control Systems Society Distinguished Member Award

IEEE

Education (3)

University of Washington: Ph.D.

University of Washington: M.S.E.E

Universidad de la Republica, Uruguay: Ingeniero Electronico and Ingeniero en Sistemas de Computacion

Media Appearances (1)

Airport Security Advances Clash With Privacy Issues

The New York Times  

2015-03-08

The video surveillance software was developed by Octavia Camps and Mario Sznaier, both engineering professors, to detect passengers going the wrong way through exits, and it has been tested since April 2014 at Cleveland Hopkins International Airport. The software is used at one exit, which handles 50,000 people a week, and has a 99 percent detection rate with only five false alarms a week, according to local officials...

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Articles (6)

Sos-rsc: A sum-of-squares polynomial approach to robustifying subspace clustering algorithms


Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition

Mario Sznaier, Octavia Camps

2018 This paper addresses the problem of subspace clustering in the presence of outliers. Typically, this scenario is handled through a regularized optimization, whose computational complexity scales polynomially with the size of the data. Further, the regularization terms need to be manually tuned to achieve optimal performance. To circumvent these difficulties, in this paper we propose an outlier removal algorithm based on evaluating a suitable sum-ofsquares polynomial, computed directly from the data.

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Hankel Matrix Rank as Indicator of Ghost in Bearing-only Tracking


IEEE Transactions on Aerospace and Electronic Systems

Korkut Bekiroglu, Mustafa Ayazoglu, Constantino Lagoa, Mario Sznaier

2018 Usually, bearing angle measurements are employed in triangulation methods to display the position of targets. However, in multi-radar and multi-target scenarios, triangulation approaches bring out ghosts that operate like real targets. This article proposes a target/ghost classifier that relies on the fact that the trajectory of a ghost is actually a function of trajectories of at least two targets and therefore, the complexity of a ghost trajectory is" greater" than the complexity of targets' trajectories...

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Convex Optimization Approaches to Information Structured Decentralized Control


IEEE Transactions on Automatic Control

Yin Wang, Jose A Lopez, Mario Sznaier

2018 This paper considers the problem of synthesizing output feedback controllers subject to sparsity constraints. This problem is known to be generically NP-hard, unless the plant satisfies the Quadratic Invariance property. Our main results show that, even if this property does not hold, tractable convex relaxations with optimality certificates can be obtained by recasting the problem into a polynomial optimization through the use of polyhedral Lyapunov functions...

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A randomized algorithm for parsimonious model identification


IEEE Transactions on Automatic Control

Korkut Bekiroglu, Constantino Lagoa, Mario Sznaier

2018 Identifying parsimonious models is generically a “hard” nonconvex problem. Available approaches typically rely on relaxations such as Group Lasso or nuclear norm minimization. Moreover, incorporating stability and model order constraints into the formalism in such methods entails a substantial increase in computational complexity. Motivated by these challenges, in this paper we present algorithms for parsimonious linear time invariant system identification aimed at identifying low-complexity models which i) incorporate a priori knowledge on the system (eg, stability), ii) allow for data with missing/nonuniform measurements, and iii) are able to use data obtained from several runs of the system with different unknown initial conditions. The randomized algorithms proposed are based on the concept of atomic norm and provide a numerically efficient way to identify sparse models …

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Multi-camera Multi-Object Tracking


arXiv preprint arXiv:1709.07065

Wenqian Liu, Octavia Camps, Mario Sznaier

2017 In this paper, we propose a pipeline for multi-target visual tracking under multi-camera system. For multi-camera system tracking problem, efficient data association across cameras, and at the same time, across frames becomes more important than single-camera system tracking. However, most of the multi-camera tracking algorithms emphasis on single camera across frame data association. Thus in our work, we model our tracking problem as a global graph, and adopt Generalized Maximum Multi Clique optimization problem as our core algorithm to take both across frame and across camera data correlation into account all together. Furthermore, in order to compute good similarity scores as the input of our graph model, we extract both appearance and dynamic motion similarities. For appearance feature, Local Maximal Occurrence Representation (LOMO) feature extraction algorithm …

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The Way They Move: Tracking Multiple Targets with Similar Appearance


IEEE International Conference on Computer Vision

Caglayan Dicle, Octavia I. Camps, Mario Sznaier

2013 We introduce a computationally efficient algorithm for multi-object tracking by detection that addresses four main challenges: appearance similarity among targets, missing data due to targets being out of the field of view or occluded behind other objects, crossing trajectories, and camera motion. The proposed method uses motion dynamics as a cue to distinguish targets with similar appearance, minimize target mis-identification and recover missing data. Computational efficiency is achieved by using a Generalized Linear Assignment (GLA) coupled with efficient procedures to recover missing data and estimate the complexity of the underlying dynamics. The proposed approach works with track lets of arbitrary length and does not assume a dynamical model a priori, yet it captures the overall motion dynamics of the targets. Experiments using challenging videos show that this framework can handle complex target motions, non-stationary cameras and long occlusions, on scenarios where appearance cues are not available or poor.

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