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URI: http://hdl.handle.net/2144/973

Welcome to the Department of Electrical & Computer Engineering

The Department of Electrical & Computer Engineering (ECE) offers a world-class education and conducts innovative research at the forefront of evolving technologies like computer hardware and software development, electronic and photonic devices, as well as sensing, processing and communication of various forms of information. With a renowned faculty, interdisciplinary research focus, cutting-edge facilities, and diverse student body, ECE is at the forefront of the technological breakthroughs that are shaping the future. Research activities in ECE are broadly classified into three primary areas: Computer Engineering, Electro-Physics, and Information and Data Sciences. The boundaries between these groups are not sharp, and interaction and cross-fertilization is common. In addition to rigorous class work, ECE degree programs encourage students to pursue hands-on research under the guidance of our accomplished faculty and in cooperation with university-wide centers and cross-disciplinary collaborations. This combination of practical and theoretical education ensures a breadth of experience in innovative problem solving and exploration that will prepare graduates for wide-range of interdisciplinary engineering careers.

ECE Contacts

Boston University Department of Electrical & Computer Engineering
W. Clem Karl, PhD, Chair
8 St. Mary's St., Room 324
Phone: (617) 353-2811
Fax: (617) 353-7337
www.bu.edu/ece

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Recently Added

  • Regularized Fourier ptychography using an online plug-and-play algorithm 

    Sun, Yu; Xu, Shiqi; Li, Yunzhe; Tian, Lei; Wohlberg, Brendt; Kamilov, Ulugbek
    The plug-and-play priors (PnP) framework has been recently shown to achieve state-of-the-art results in regularized image reconstruction by leveraging a sophisticated denoiser within an iterative algorithm. In this paper, ...
  • Illumination coding meets uncertainty learning: toward reliable AI-augmented phase imaging 

    Xue, Yujia; Cheng, Shiyi; Li, Yunzhe; Tian, Lei (2019-01-07)
    We propose a physics-assisted deep learning (DL) framework for large space-bandwidth product (SBP) phase imaging. We design an asymmetric coded illumination scheme to encode high-resolution phase information across a wide ...
  • Tiresias: predicting security events through deep learning 

    Shen, Yun; Mariconti, Enrico; Vervier, Pierre Antoine; Stringhini, Gianluca (ACM Press, 2018)
    With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when ...
  • On the origins of memes by means of fringe web communities 

    Zannettou, Savvas; Caulfield, Tristan; Blackburn, Jeremy; De Cristofaro, Emiliano; Sirivianos, Michael; Stringhini, Gianluca; Suarez-Tangil, Guillermo (2018-10-31)
    Internet memes are increasingly used to sway and manipulate public opinion. This prompts the need to study their propagation, evolution, and influence across the Web. In this paper, we detect and measure the propagation ...
  • TrojDRL: Trojan Attacks on Deep Reinforcement Learning Agents 

    Kiourti, Panagiota; Wardega, Kacper; Jha, Susmit; Li, Wenchao
    Recent work has identified that classification models implemented as neural networks are vulnerable to data-poisoning and Trojan attacks at training time. In this work, we show that these training-time vulnerabilities ...
  • Minimal reachability is hard to approximate 

    Jadbabaie, Ali; Olshevsky, Alexander; Pappas, George J.; Tzoumas, Vasileios (Institute of Electrical and Electronics Engineers, 2017)
    In this note, we consider the problem of choosing, which nodes of a linear dynamical system should be actuated so that the state transfer from the system's initial condition to a given final state is possible. Assuming a ...
  • Gradient descent for sparse rank-one matrix completion for crowd-sourced aggregation of sparsely interacting workers 

    Ma, Yao; Olshevsky, Alexander; Saligrama, Venkatesh; Czepesvari, Csaba (2018-07-10)
    We consider worker skill estimation for the singlecoin Dawid-Skene crowdsourcing model. In practice skill-estimation is challenging because worker assignments are sparse and irregular due to the arbitrary, and uncontrolled ...
  • A family of droids -- Android malware detection via behavioral modeling: static vs dynamic analysis 

    Onwuzurike, Lucky; Almeida, Mario; Mariconti, Enrico; Blackburn, Jeremy; Stringhini, Gianluca; Cristofaro, Emiliano De
    Following the increasing popularity of mobile ecosystems, cybercriminals have increasingly targeted them, designing and distributing malicious apps that steal information or cause harm to the device's owner. Aiming to ...
  • Deep speckle correlation: a deep learning approach toward scalable imaging through scattering media 

    Li, Yunzhe; Xue, Yujia; Tian, Lei (OPTICAL SOC AMER, 2018-10-20)
    Imaging through scattering is an important yet challenging problem. Tremendous progress has been made by exploiting the deterministic input–output “transmission matrix” for a fixed medium. However, this “one-to-one” mapping ...
  • Deep learning approach to Fourier ptychographic microscopy 

    Thanh, Nguyen; Xue, Yujia; Li, Yunzhe; Tian, Lei; Nehmetallah, George (OPTICAL SOC AMER, 2018-10-01)
    Convolutional neural networks (CNNs) have gained tremendous success in solving complex inverse problems. The aim of this work is to develop a novel CNN framework to reconstruct video sequences of dynamic live cells captured ...

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