scispace - formally typeset
Search or ask a question
Author

Constantine E. Kontokosta

Bio: Constantine E. Kontokosta is an academic researcher from New York University. The author has contributed to research in topics: Efficient energy use & Urban planning. The author has an hindex of 25, co-authored 75 publications receiving 1490 citations.


Papers
More filters
Journal ArticleDOI
TL;DR: In this article, the authors developed a predictive model of energy use at the building, district, and city scales using training data from energy disclosure policies and predictors from widely available property and zoning information.

170 citations

Journal ArticleDOI
TL;DR: Hurricane Sandy had a significant and immediate impact on neighborhoods classified as least resilient based on the calculated REDI scores, while the most resilient neighborhoods were shown to better withstand disruption to normal activity patterns and more quickly recover to pre-event functional capacity.

120 citations

Journal ArticleDOI
TL;DR: A building energy performance grading methodology using machine learning and city-specific energy use and building data that accounts for variations in the expected and actual performance of individual buildings, out-performing existing state-of-the-art methods.

91 citations

Journal ArticleDOI
TL;DR: This methodology has the potential to support collection truck route optimization based on expected building-level waste generation rates, and to facilitate new equitable solid waste management policies to shift behavior and divert waste from landfills based on benchmarking and peer performance comparisons.

88 citations

Journal ArticleDOI
TL;DR: In this paper, the authors examined the effects of ownership type, tenant demand, and real estate market location on building energy retrofit decisions in the commercial office sector and found that ownership type and local market do, in fact, influence the retrofit decision.

88 citations


Cited by
More filters
Christopher M. Bishop1
01 Jan 2006
TL;DR: Probability distributions of linear models for regression and classification are given in this article, along with a discussion of combining models and combining models in the context of machine learning and classification.
Abstract: Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

10,141 citations

01 Jan 2004

2,223 citations

Journal Article

1,449 citations

Journal ArticleDOI
25 Apr 2018
TL;DR: An overview of core ideas in GSP and their connection to conventional digital signal processing are provided, along with a brief historical perspective to highlight how concepts recently developed build on top of prior research in other areas.
Abstract: Research in graph signal processing (GSP) aims to develop tools for processing data defined on irregular graph domains. In this paper, we first provide an overview of core ideas in GSP and their connection to conventional digital signal processing, along with a brief historical perspective to highlight how concepts recently developed in GSP build on top of prior research in other areas. We then summarize recent advances in developing basic GSP tools, including methods for sampling, filtering, or graph learning. Next, we review progress in several application areas using GSP, including processing and analysis of sensor network data, biological data, and applications to image processing and machine learning.

1,306 citations

Journal ArticleDOI
TL;DR: Sampson, Robert J. as mentioned in this paper, The Great American city: Chicago and the enduring neighborhood effect. Chicago: University of Chicago Press. 2012. pp. 552, $27.50 cloth.
Abstract: Sampson, Robert J. 2012. Great American city: Chicago and the enduring neighborhood effect. Chicago: University of Chicago Press. ISBN-13: 9780226734569. pp. 552, $27.50 cloth. Robert J. Sampson’s ...

1,089 citations