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URI: http://hdl.handle.net/2144/971

The College of Engineering at Boston University is a community of students, faculty, and staff focused on advancing science and technology through research and discovery, and preparing students to be technology leaders in the 21st century. Undergraduate students participate in a comprehensive core curriculum that sets the foundation for their engineering studies while delivering a breadth of education across the humanities, mathematics, and social and natural sciences. Through an array of majors and concentrations, they can study aerospace, biomedical, computer, electrical, manufacturing or mechanical engineering, as well as nanotechnology, and energy technologies and environmental engineering. They also have the opportunity work side-by-side with research faculty in a number of modern, high-tech facilities. Graduate students partake in myriad programs and research opportunities leading to doctoral or master’s degrees in biomedical, computer, computer systems, electrical, manufacturing, mechanical, global manufacturing, photonics, systems, or materials science and engineering.

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Recently Added

  • Reconstruction of 1,000 projection neurons reveals new cell types and organization of long-range connectivity in the mouse brain 

    Winnubst, Johan; Bas, Erhan; Ferreira, Tiago A.; Wu, Zhuhao; Economo, Michael N.; Edson, Patrick; Arthur, Ben J.; Bruns, Christopher; Rokicki, Konrad; Schauder, David; Olbris, Donald J.; Murphy, Sean D.; Ackerman, David G.; Arshadi, Cameron; Baldwin, Perry; Blake, Regina; Elsayed, Ahmad; Hasan, Mashtura; Ramirez, Daniel; Dos Santos, Bruno; Weldon, Monet; Zafar, Amina; Dudman, Joshua T.; Gerfen, Charles R.; Hantman, Adam W.; Korff, Wyatt; Sternson, Scott M.; Spruston, Nelson; Svoboda, Karel; Chandrashekar, Jayaram (2019-09-19)
    Neuronal cell types are the nodes of neural circuits that determine the flow of information within the brain. Neuronal morphology, especially the shape of the axonal arbor, provides an essential descriptor of cell type and ...
  • Information-distilling quantizers 

    Bhatt, Alankrita; Nazer, Bobak; Ordentlich, Or; Polyanskiy, Yury (2018-02)
    Let X and Y be dependent random variables. This paper considers the problem of designing a scalar quantizer for Y to maximize the mutual information between the quantizer's output and X, and develops fundamental properties ...
  • Automatic graph-based modeling of brain microvessels captured with two-photon microscopy 

    Damseh, Rafat; Pouliot, Philippe; Gagnon, Louis; Sakadzic, Sava; Boas, David; Cheriet, Farida; Lesage, Frederic (2019-11)
    Graph models of cerebral vasculature derived from two-photon microscopy have shown to be relevant to study brain microphysiology. Automatic graphing of these microvessels remain problematic due to the vascular network ...
  • A computational paradigm for real-time MEG neurofeedback for dynamic allocation of spatial attention 

    Rana, Kunjan D.; Khan, Sheraz; Hämäläinen, Matti S.; Vaina, Lucia (BioMed Central, 2020-06-12)
    BACKGROUND: Neurofeedback aids volitional control of one's own brain activity using non-invasive recordings of brain activity. The applications of neurofeedback include improvement of cognitive performance and treatment ...
  • Mechanical MNIST - Fashion 

    Lejeune, Emma (2020)
    Each dataset in the Mechanical MNIST collection contains the results of 70,000 (60,000 training examples + 10,000 test examples) finite element simulation of a heterogeneous material subject to large deformation. Mechanical ...
  • Quantifying heart valve interstitial cell contractile state using highly tunable poly(ethylene glycol) hydrogels 

    Khang, Alex; Gonzalez Rodriguez, Andrea; Schroeder, Megan E.; Sansom, Jacob; Lejeune, Emma; Anseth, Kristi S.; Sacks, Michael S. (Elsevier BV, 2019-09)
    Valve interstitial cells (VIC) are the primary cell type residing within heart valve tissues. In many valve pathologies, VICs become activated and will subsequently profoundly remodel the valve tissue extracellular matrix ...
  • Mechanical MNIST - Multi-Fidelity 

    Lejeune, Emma (2020-07)
    Each dataset in the Mechanical MNIST collection contains the results of 70,000 (60,000 training examples + 10,000 test examples) finite element simulation of a heterogeneous material subject to large deformation. Mechanical ...
  • Active learning for efficient microfluidic design automation 

    McIntyre, David; Lashkaripour, Ali; Densmore, Douglas (IWBDA, 2020-08-03)
    Droplet microfluidics has the potential to eliminate the testing bottleneck in synthetic biology by screening biological samples encapsulated in water-in-oil emulsions at unprecedented throughput. Sophisticated screens ...
  • Efficient large-scale microfluidic design-space exploration: from data to model to data 

    Lashkaripour, Ali; McIntyre, David; Densmore, Douglas (IWBDA, 2020-08-03)
    Droplet microfluidics is well poised to improve the gold standard in many fields such as synthetic biology. However, the lack of available design automation tools that can create a microfluidic droplet generator based on ...
  • Dependence of the MR signal on the magnetic susceptibility of blood studied with models based on real microvascular networks 

    Cheng, Xiaojun; Berman, Avery J. L.; Polimeni, Jonathan R.; Buxton, Richard B.; Gagnon, Louis; Devor, Anna; Sakadzic, Sava; Boas, David A. (Wiley, 2019-06)
    PURPOSE: The primary goal of this study was to estimate the value of beta , the exponent in the power law relating changes of the transverse relaxation rate and intra-extravascular local magnetic susceptibility differences ...

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