Alon Efrat

About

Alon Efrat is an Associate Professor in the Computer Science Department at the University of Arizona. He received the NSF CAREER award in 2004 for his work on pattern matching, realistic input models, sensor placement, and useful algorithms in computational geometry. His research focuses on computational geometry and its applications, including sensor network algorithms and geometric optimization in wireless communication and sensing. He has served on the editorial boards of the International Journal of Computational Geometry and Applications (IJCGA) and the Journal of Discrete Algorithms (JDA). Professor Efrat has been actively involved in numerous program committees for prestigious conferences such as SoCG, Broadnets, ACM GIS, FOCS, INFOCOM, MILCOM, and ICDCS, and has co-chaired tracks and workshops related to sensor network algorithms and geometric optimization. His teaching includes courses like Computer Graphics (CSc433/533), and he has supervised students who have gone on to work at leading technology companies. His research projects include scheduling the motion of UAV swarms for terrain sweeping and moving target detection, as well as context-driven text expansion applied to educational video browsing systems.

Research topics

• Computer Science
• Data Mining
• Machine Learning
• Political Science
• Artificial Intelligence
• Data science
• Geography
• Medicine
• Business
• Engineering

Selected publications

• Prediction and prevention of pandemics via graphical model inference and convex programming

  Scientific Reports · 2022-05-09 · 8 citations

  articleOpen access

  Hard-to-predict bursts of COVID-19 pandemic revealed significance of statistical modeling which would resolve spatio-temporal correlations over geographical areas, for example spread of the infection over a city with census tract granularity. In this manuscript, we provide algorithmic answers to the following two inter-related public health challenges of immense social impact which have not been adequately addressed (1) Inference Challenge assuming that there are N census blocks (nodes) in the c…

  PublisherOA PDFDOI

• Graphical Models of Pandemic

  medRxiv (Cold Spring Harbor Laboratory) · 2021 · 8 citations

  Both COVID-19 and novel pandemics challenge those of us within the modeling community, specifically in establishing suitable relations between lifecycles, scales, and existing methods. Herein we demonstrate transitions between models in space/time, individual-to-community, county-to-city, along with models for the trace beginning with exposure, then to symptom manifest, then to community transmission. To that end, we leverage publicly available data to compose a chain of Graphical Models (GMs) f…

  PublisherOA PDFDOI

• Data Inference from Encrypted Databases: A Multi-dimensional Order-Preserving Matching Approach

  arXiv (Cornell University) · 2020-01-23 · 3 citations

  preprintOpen access

  Due to increasing concerns of data privacy, databases are being encrypted before they are stored on an untrusted server. To enable search operations on the encrypted data, searchable encryption techniques have been proposed. Representative schemes use order-preserving encryption (OPE) for supporting efficient Boolean queries on encrypted databases. Yet, recent works showed the possibility of inferring plaintext data from OPE-encrypted databases, merely using the order-preserving constraints, or…

  PublisherOA PDFDOI

• Polygons with Prescribed Angles in 2D and 3D

  Journal of Graph Algorithms and Applications · 2022-06-01 · 2 citations

  articleOpen access1st authorCorresponding

  We consider the construction of a polygon $P$ with $n$ vertices whose turning angles at the vertices are given by a sequence $A=(\alpha_0,\ldots, \alpha_{n-1})$, $\alpha_i\in (-\pi,\pi)$, for $i\in\{0,\ldots, n-1\}$. The problem of realizing $A$ by a polygon can be seen as that of constructing a straight-line drawing of a graph with prescribed angles at vertices, and hence, it is a special case of the well studied problem of constructing an angle graph. In 2D, we characterize sequences $A$ for w…

  PublisherDOI

• Data inference from encrypted databases

  2020-10-08 · 2 citations

  article

  Due to increasing concerns of data privacy, databases are being encrypted before they are stored on an untrusted server. To enable search operations on the encrypted data, searchable encryption techniques have been proposed. Representative schemes use order-preserving encryption (OPE) for supporting efficient Boolean queries on encrypted databases. Yet, recent works showed the possibility of inferring plaintext data from OPE-encrypted databases, merely using the order-preserving constraints, or…

  PublisherDOI

Recent grants

• TC: Small: Collaborative Research: Protecting Networks from Large-Scale Physical Attacks and Disasters

  NSF · $167k · 2010–2015

• CAREER: Pattern Matching, Realistic Input Models and Sensor Placement. UsefulAlgorithms in Computational Geometry

  NSF · $427k · 2004–2012

Frequent coauthors

• Micha Sharir

  Tel Aviv University

  54 shared

• Joseph S. B. Mitchell

  35 shared

• Stephen Kobourov

  University of Arizona

  33 shared

• Valentin Polishchuk

  31 shared

• Pankaj K. Agarwal

  29 shared

• Swaminathan Sankararaman

  Akamai (United States)

  18 shared

• Esther M. Arkin

  Hangzhou Dianzi University

  16 shared

• Michael Chertkov

  University of Arizona

  16 shared

Labs

• Alon Efrat's LabPI

  Computational Geometry, Pattern Matching, Realistic Input Models and Sensor Placement

Education

• PhD, Mathematics and Computer Science

  Tel Aviv University

  1998

Similar researchers at University of Arizona

• Saumya Debrayh-35
• Christian Collbergh-32
• Roberto Giacobazzih-29