2019 - IEEE Fellow For contributions to identification of switched systems and multiobjective control
The scientist’s investigation covers issues in Control theory, Mathematical optimization, Artificial intelligence, Convex optimization and Optimal control. His Control theory study is mostly concerned with Control theory, Linear system, Discrete system, Control synthesis and Robust control. He has included themes like Full state feedback and Sequence in his Mathematical optimization study.
His Artificial intelligence research incorporates themes from Machine learning, Computer vision and Pattern recognition. His work in the fields of Convex optimization, such as Proper convex function, intersects with other areas such as Affine transformation, Approximation theory and Zero. His study in Optimal control is interdisciplinary in nature, drawing from both Control system, Norm and Discrete mathematics.
His primary scientific interests are in Mathematical optimization, Control theory, Convex optimization, Robust control and Algorithm. Mario Sznaier interconnects Computational complexity theory, Norm, Frequency domain and Identification in the investigation of issues within Mathematical optimization. His works in Optimal control, Control theory, Linear system, Control system and Transfer function are all subjects of inquiry into Control theory.
His work deals with themes such as Discrete system and Nonlinear system, which intersect with Linear system. Many of his research projects under Convex optimization are closely connected to Affine transformation, Constrained optimization, Nonlinear programming and Sequence with Affine transformation, Constrained optimization, Nonlinear programming and Sequence, tying the diverse disciplines of science together. The study incorporates disciplines such as Active vision, Bounded function and Controller design in addition to Robust control.
His main research concerns Algorithm, Artificial intelligence, Mathematical optimization, Identification and Convex optimization. The concepts of his Algorithm study are interwoven with issues in Positive-definite matrix, Embedding, Subspace topology and Cluster analysis. His Artificial intelligence study combines topics from a wide range of disciplines, such as Machine learning and Computer vision.
His work carried out in the field of Mathematical optimization brings together such families of science as Linear system and Rank. His Convex optimization research encompasses a variety of disciplines, including Control theory and Decentralised system. Structure is closely connected to Data-driven in his research, which is encompassed under the umbrella topic of Control theory.
The scientist’s investigation covers issues in Artificial intelligence, Algorithm, Mathematical optimization, Control theory and Convex optimization. His research investigates the connection with Artificial intelligence and areas like Computer vision which intersect with concerns in Graph and Hankel matrix. His studies in Algorithm integrate themes in fields like Subspace topology, Embedding, Outlier and Cluster analysis.
His Mathematical optimization research is multidisciplinary, relying on both Linear system and Rank. In his research on the topic of Linear system, System identification and Computational complexity theory is strongly related with Randomized algorithm. A large part of his Control theory studies is devoted to Control theory.
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Person Re-Identification Using Kernel-Based Metric Learning Methods
Fei Xiong;Mengran Gou;Octavia I. Camps;Mario Sznaier.
european conference on computer vision (2014)
Robust Systems Theory and Applications
Ricardo S. Sanchez-Pena;Mario Sznaier.
The Way They Move: Tracking Multiple Targets with Similar Appearance
Caglayan Dicle;Octavia I. Camps;Mario Sznaier.
international conference on computer vision (2013)
Suboptimal control of linear systems with state and control inequality constraints
Mario Sznaier;Mark Damborg.
conference on decision and control (1987)
Cross-view activity recognition using Hankelets
Binlong Li;Octavia I. Camps;Mario Sznaier.
computer vision and pattern recognition (2012)
Persistent disturbance rejection via static-state feedback
F. Blanchini;M. Sznaier.
IEEE Transactions on Automatic Control (1995)
Activity recognition using dynamic subspace angles
Binlong Li;Mustafa Ayazoglu;Teresa Mao;Octavia I. Camps.
computer vision and pattern recognition (2011)
Heuristically enhanced feedback control of constrained discrete-time linear systems
M. Sznaier;M. Sznaier;M. J. Damborg.
Dynamic Appearance Modeling for Human Tracking
Hwasup Lim;O.I. Camps;M. Sznaier;V.I. Morariu.
computer vision and pattern recognition (2006)
A Sparsification Approach to Set Membership Identification of Switched Affine Systems
N. Ozay;M. Sznaier;C. M. Lagoa;O. I. Camps.
IEEE Transactions on Automatic Control (2012)
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