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 [274]
Department of Biomedical Engineering -
Electrical and Computer Engineering [309]
Department of Electrical and Computer Engineering -
Materials Science and Engineering [0]
Division of Materials Science and Engineering -
Mechanical Engineering [271]
Department of Mechanical Engineering -
Systems Engineering [1]
Division of Systems Engineering
Recently Added
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Temporal variability in implicit online learning
(2020-12-06)In the setting of online learning, Implicit algorithms turn out to be highly suc-cessful from a practical standpoint. However, the tightest regret analyses onlyshow marginal improvements over Online Mirror Descent. In ... -
A high probability analysis of adaptive SGD with momentum
(2020-07-17)Stochastic Gradient Descent (SGD) and its variants are the most used algorithms in machine learning applications. In particular, SGD with adaptive learning rates and momentum is the industry standard to train deep networks. ... -
Mechanical MNIST Crack Path
(2021-07)The Mechanical MNIST Crack Path dataset contains Finite Element simulation results from phase-field models of quasi-static brittle fracture in heterogeneous material domains subjected to prescribed loading and boundary ... -
Beliefs in decision-making cascades
(Institute of Electrical and Electronics Engineers (IEEE), 2019-10-01)This work explores a social learning problem with agents having nonidentical noise variances and mismatched beliefs. We consider an N-agent binary hypothesis test in which each agent sequentially makes a decision based not ... -
Seeing around corners with edge-resolved transient imaging
(2020-11-23)Non-line-of-sight (NLOS) imaging is a rapidly growing field seeking to form images of objects outside the field of view, with potential applications in autonomous navigation, reconnaissance, and even medical imaging. The ... -
Distributed hypothesis testing with social learning and symmetric fusion
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2014-12-01)We study the utility of social learning in a distributed detection model with agents sharing the same goal: a collective decision that optimizes an agreed upon criterion. We show that social learning is helpful in some ... -
Social teaching: being informative vs. being right in sequential decision making
(IEEE, 2013-07)We consider sequential Bayesian binary hypothesis testing where each individual agent makes a binary decision motivated only by minimization of her own perception of the Bayes risk. The information available to each agent ... -
Keep ballots secret: on the futility of social learning in decision making by voting
(IEEE, 2013-05)We show that social learning is not useful in a model of team binary decision making by voting, where each vote carries equal weight. Specifically, we consider Bayesian binary hypothesis testing where agents have any ... -
Asymptotic analysis of MAP estimation via the replica method and applications to compressed sensing
(Institute of Electrical and Electronics Engineers (IEEE), 2012-03)The replica method is a nonrigorous but well-known technique from statistical physics used in the asymptotic analysis of large, random, nonlinear problems. This paper applies the replica method, under the assumption of ... -
Message-passing de-quantization with applications to compressed sensing
(Institute of Electrical and Electronics Engineers (IEEE), 2012-12)Estimation of a vector from quantized linear measurements is a common problem for which simple linear techniques are suboptimal-sometimes greatly so. This paper develops message-passing de-quantization (MPDQ) algorithms ...
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