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Emre Sefer
Researcher at Carnegie Mellon University
Publications - 22
Citations - 284
Emre Sefer is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Metric (mathematics) & Chromosome conformation capture. The author has an hindex of 10, co-authored 22 publications receiving 247 citations. Previous affiliations of Emre Sefer include Özyeğin University & University of Maryland, College Park.
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Proceedings ArticleDOI
NetVisia: Heat Map & Matrix Visualization of Dynamic Social Network Statistics & Content
Robert Gove,Nick Gramsky,Rose Kirby,Emre Sefer,Awalin Sopan,Cody Dunne,Ben Shneiderman,Meirav Taieb-Maimon +7 more
TL;DR: NetVisia is introduced, a social network visualization system designed to support users in exploring temporal evolution in networks by using heat maps to display node attribute changes over time, and implemented improvements to the system are discussed.
Journal ArticleDOI
Tradeoffs between Dense and Replicate Sampling Strategies for High-Throughput Time Series Experiments
TL;DR: This work develops a theoretical framework that focuses on a restricted yet expressive set of possible curves over a wide range of noise levels and observes that, under reasonable noise levels, autocorrelations in the time series data allow dense sampling to better determine the correct levels of non-sampled points when compared to replicate sampling.
Journal ArticleDOI
Selecting the most appropriate time points to profile in high-throughput studies.
Michael Kleyman,Emre Sefer,Teodora Nicola,Celia R. Espinoza,Celia R. Espinoza,Divya Chhabra,Divya Chhabra,James S. Hagood,James S. Hagood,Naftali Kaminski,Namasivayam Ambalavanan,Ziv Bar-Joseph +11 more
TL;DR: By applying TPS to study mouse lung development, the points selected by TPS can be used to reconstruct an accurate representation for the expression values of the non selected points, and can thus serve as a key design strategy for high throughput time series experiments.
Proceedings ArticleDOI
Convex Risk Minimization to Infer Networks from probabilistic diffusion data at multiple scales
Emre Sefer,Carl Kingsford +1 more
TL;DR: This work improves upon the existing approaches for inferring graph edges if the authors can only observe a SEIR diffusion process spreading over the nodes of a graph and presents a more general framework that better uses trace data to model edge non-existence under SEIR model.
Journal ArticleDOI
Parsimonious reconstruction of network evolution.
TL;DR: A parsimony-based approach to ancestral network reconstruction is shown to be both efficient and accurate and to show that considering a larger set of potential ancestral interactions by not assuming a relative ordering of unrelated duplication events can lead to improved ancestral network inference.