College of Engineering
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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.
http://www.bu.edu/eng/Sub-communities within this community
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Bioinformatics [101]
The Graduate Program in Bioinformatics -
Biomedical Engineering [276]
Department of Biomedical Engineering -
Electrical and Computer Engineering [335]
Department of Electrical and Computer Engineering -
Materials Science and Engineering [0]
Division of Materials Science and Engineering -
Mechanical Engineering [273]
Department of Mechanical Engineering -
Systems Engineering [1]
Division of Systems Engineering
Recently Added
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Deep divergence learning
(2020-07-12)Classical linear metric learning methods have recently been extended along two distinct lines: deep metric learning methods for learning embeddings of the data using neural networks, and Bregman divergence learning approaches ... -
Joint bilateral learning for real-time universal photorealistic style transfer
(2020-08-24)Photorealistic style transfer is the task of transferring the artistic style of an image onto a content target, producing a result that is plausibly taken with a camera. Recent approaches, based on deep neural networks, ... -
Learning to approximate a Bregman divergence
(2020)Bregman divergences generalize measures such as the squared Euclidean distance and the KL divergence, and arise throughout many areas of machine learning. In this paper, we focus on the problem of approximating an arbitrary ... -
An audio-based wakeword-independent verification system
(2020-10-25)We propose an audio-based wakeword-independent verification model to determine whether a wakeword spotting model correctly woke and should respond or incorrectly woke and should not respond. Our model works on any ... -
Building a robust word-level wakeword verification network
(2020-10-25) -
Metadata-aware end-to-end keyword spotting
(2020-10-25) -
Development and validation of a prognostic risk score system for COVID-19 inpatients: a multi-center retrospective study in China
(2020-11-28)Coronavirus disease 2019 (COVID-19) has become a worldwide pandemic. Hospitalized patients of COVID-19 suffer from a high mortality rate, motivating the development of convenient and practical methods that allow clinicians ... -
Congestion-aware routing and rebalancing of autonomous mobility-on-demand systems in mixed traffic
(Institute of Electrical and Electronics Engineers (IEEE), 2020)This paper studies congestion-aware route-planning policies for Autonomous Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles provides on demand mobility under mixed traffic conditions. Specifically, ... -
Online learning with imperfect hints
(2020-07-13)We consider a variant of the classical online linear optimization problem in which at every step, the online player receives a “hint” vector before choosing the action for that round. Rather surprisingly, it was shown ... -
Parameter-free, dynamic, and strongly-adaptive online learning
(2020-07-13)We provide a new online learning algorithm that for the first time combines several disparate notions of adaptivity. First, our algorithm obtains a “parameter-free” regret bound that adapts to the norm of the comparator ...
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