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SUMMARY:Engineers Week: Harnessing Metamaterials to Manipulate Electromagnetic and Acoustic Waves
DESCRIPTION:Dr. Xin Zhang\, Professor\, Boston University \nLocation: 138 ISEC \nMetamaterials have been intensively studied and applied to a broad range of practical applications ranging from wireless communications to magnetic resonance imaging. Photonic metamaterials consisting of subwavelength “meta-atoms” have received enormous interest due to their extraordinary and unprecedented optical properties. Specifically\, the effective permittivity and permeability can be tailored and reconfigured to construct metamaterial devices by modulating or actuating the constituent meta-atoms. By leveraging microelectromechanical system (MEMS) technology\, we have developed functional metamaterial devices to manipulate and detect the terahertz waves. In addition\, metamaterials exhibit extraordinary near-field properties to control electric and magnetic field distribution. I will introduce our progress on intelligent magnetic metamaterials to enhance the signal to noise ratio of magnetic resonance imaging. Besides electromagnetic metamaterials\, acoustic metamaterials for sound wave shaping and silencing will also be discussed. \nXin Zhang received her Ph.D. in Mechanical Engineering from the Hong Kong University of Science and Technology (HKUST). She was a Postdoctoral Researcher and then a Research Scientist with the Massachusetts Institute of Technology (MIT). She then joined Boston University (BU) as a Faculty Member\, where she is currently a Professor of Mechanical Engineering\, Electrical & Computer Engineering\, Biomedical Engineering\, Materials Science & Engineering\, and the Photonics Center. Dr. Zhang is the Associate Director of the Boston University Nanotechnology Innovation Center and Director of both the NSF Research Experiences for Undergraduates (REU) and Teachers (RET) Sites in Integrated Nanomanufacturing at Boston University. \nDr. Zhang’s research interests are in the broad areas of microelectromechanical systems (MEMS or microsystems) and metamaterials (acoustic\, electromagnetic\, nonlinear\, photonic\, terahertz\, tunable\, etc.). She has published 160+ papers in interdisciplinary journals\, become both US and EU-US National Academy of Engineering Invitee (ages: 30-45)\, and is an Elected Fellow of AAAS\, AIMBE\, APS\, ASME\, IEEE\, NAI\, and OSA\, and Associate Fellow of AIAA. \nHosted by the Electrical and Computer Engineering Department
URL:https://ece.northeastern.edu/event/engineers-week-harnessing-metamaterials-to-manipulate-electromagnetic-and-acoustic-waves/
LOCATION:138 ISEC\, 360 Huntington Ave\, 138 ISEC\, Boston\, MA\, 02115\, United States
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SUMMARY:Electrical and Computer Engineering Seminar: Murat Kocaoglu
DESCRIPTION:Location: ISEC 138 \nCausality: From Learning to Generative Models \nAbstract: \nCausal inference is fundamental for multiple disciplines ranging from medical research to engineering\, statistics\, and economics. It is also central in machine learning and is now becoming a core component of artificial intelligence research. Although causal inference has been studied for a long time in various fields under different frameworks\, today we need tools that can process a large number of variables to handle modern large datasets. The graphical approach to probabilistic causation advocated by Judea Pearl and others provides a way to compactly represent the causal relations using directed acyclic graphs and paves the way for the design of algorithms that can answer causal questions for many variables. \nIn this talk\, Kocaoglu first provides a friendly introduction to causality and explain why causal understanding is important. As his first contribution\, he will propose a framework called entropic causal inference for inferring the causal direction between two variables from data. He will show that entropy can be used to capture the complexity of a causal mechanism. Further\, if the true direction has a simple mechanism\, we can identify it from data. The entropic causal inference framework leverages tools from information theory for causal inference. As his second contribution\, he will show how we can apply causality in deep generative models – deep neural networks used for modeling complex data. He will demonstrate how to define and train a causal deep generative model\, called CausalGAN for generating images with labels. As an extension of generative adversarial networks (GANs)\, CausalGAN allows sampling not only from the observed data distribution but also from the interventional distributions of images. He will conclude with future directions for causal inference and its applications in supervised learning and reinforcement learning. \n \nBio: \nMurat Kocaoglu received his B.S. degree in Electrical – Electronics Engineering with a minor degree in Physics from the Middle East Technical University in 2010. He received his M.S. degree from the Koc University\, Turkey in 2012 under the supervision of Prof. Ozgur B. Akan\, and PhD degree from The University of Texas at Austin in 2018\, under the supervision of Prof. Alex Dimakis and Prof. Sriram Vishwanath. He is currently a Research Staff Member in the MIT-IBM Watson AI Lab in IBM Research\, Cambridge\, Massachusetts. His current research interests include causal inference\, generative adversarial networks\, and information theory
URL:https://ece.northeastern.edu/event/electrical-and-computer-engineering-seminar-murat-kocaoglu/
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