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
• Marketing
• Database
• Algorithm
• Operations research

Selected publications

• Agent-in-Cell Modeling of Pandemics

  Sustainable development goals series · 2026-01-01

  book-chapter
  PublisherDOI

• Agent-In-Cell Modeling of Pandemics: Harnessing Super-Agents for Predictive Modeling

  Sustainable development goals series · 2026-01-01

  book-chapter
  PublisherDOI

• The Gate-Cover Problem

  Lecture notes in computer science · 2026-01-01

  book-chapter
  PublisherDOI

• Visualization of bipartite graphs in limited window size

  Acta Informatica · 2025-04-03

  articleOpen access1st authorCorresponding

  Abstract Bipartite graphs are commonly used to visualize objects and their features. An object may possess several features and several objects may share a common feature. The standard visualization of bipartite graphs, with objects and features on two (say horizontal) parallel lines at integer coordinates and edges drawn as line segments, can often be difficult to work with. A common task in visualization of such graphs is to consider one object and all its features. This naturally defines a drawing window, defined as the smallest interval that contains the x-coordinates of the object and all its features. We show that if both objects and features can be reordered, minimizing the average window size is NP-hard. However, if the features are fixed, then we provide an efficient polynomial-time algorithm for arranging the objects, so as to minimize the average window size. Finally, we introduce a different way of visualizing the bipartite graph, by placing the nodes of the two parts on two concentric circles. For this setting we also show NP-hardness for the general case and a polynomial-time algorithm when the features are fixed.

  PublisherOA PDFDOI

• Voluntary mobility clustering for epidemic control

  2025-11-03

  articleOpen access

  In case of a future pandemic, the mobility dynamics of a city can be controlled by intervening in the mobility patterns of people. Instead of hard quarantine policies, incentives can be designed that are compatible with people's preferences. At first, we distinguish mobility from the different types of locations for which distance matters. We match these types of locations in a way that maximizes the natural preference of people to visit the locations. We investigate different approaches for matching locations, such as retail and educational services, while considering people's preferences. We show that satisfying the preferences of the entire city is a computationally hard problem. Approximation algorithms are proposed in which the penalty for preference violation is bounded. We propose a fast approximation algorithm that focuses on the penalty value of locations, and we propose a more computationally heavy approximation that focuses on user penalty with a specific scheme of user allocation to locations. Additionally, we investigated higher-order matching of locations and the complexity of urban partitioning. We tested our approach in Euclidean space and network space. Finally, we show that applying such mobility restrictions can reduce the transmission rate, and we extract cells whose people can be incentivized to fulfill their needs based on the proposed algorithms, slowing down a future pandemic and preventing potential superspreading events.

  PublisherOA PDFDOI

• Visualization of Bipartite Graphs in Limited Window Size

  Research Square · 2024-05-08

  preprintOpen access1st authorCorresponding
  PublisherOA PDFDOI

• The Effect of Personality Characteristics on the Development of Interpersonal Communication Skills Through One-Time Training

  Scientific Annals of Economics and Business · 2024-06-27 · 2 citations

  articleOpen access1st authorCorresponding

  The importance of interpersonal communication skills in the business environment will only increase as the world undergoes trends of globalization and digitization, as well as various crises. The factors that affect interpersonal skills, such as life experience, situational factors, and individual characteristics, are difficult to isolate. Among the prominent antecedents of interpersonal communication effectiveness are personality characteristics. The current study used one-time training to examine how personality traits and interpersonal skills relate among 127 managers from a wide variety of professions in Israel. The current study confirmed the effect of personality characteristics on interpersonal communication skills, albeit weakly. A significant improvement was found in the Emotional stability following the training. Participating in the training changed the way people associate personality traits with Interaction management. An in-depth study of an intervening variable found that those with low extraversion and high conscientiousness improved assertiveness, empathy, supportiveness, openness to experience, and self-disclosure, in contrast to those with less solid personality characteristics who showed a smaller improvement or even decreased in these skills. Our findings have important implications for increasing the effectiveness of interpersonal skills training.

  PublisherOA PDFDOI

• The Interrelationship Amid Characteristics of Flexibility and Bargaining Style With the Perceived Value Gained Amongst Negotiators in the Hi-Tech Business Sector

  SSRN Electronic Journal · 2023-01-01

  articleOpen access1st authorCorresponding
  PublisherDOI

• Improving Interpersonal Communication Skills Through Adaptive Training - Influences of Negotiation Styles, Perception of Trust and Personality Traits- Findings From Preliminary Statistical Analysis

  SSRN Electronic Journal · 2023-01-01 · 1 citations

  articleOpen access1st authorCorresponding
  PublisherDOI

• Redefining the Driver's Attention Gauge in Semi-Autonomous Vehicles

  2023-10-26 · 1 citations

  article

  Driver distraction caused by over-reliance on automotive technology is one of the leading causes of accidents in semi-autonomous vehicles. Existing driver's attention-gauging approaches are intrusive and as such emphasize constant driver engagement. In case of an urgent traffic event, they fail to measure the event's criticality and subsequently generate timely alerts. In this paper, we re-position the driver's attention-gauging approach as a way to improve the driver's situational awareness during critical situations. We exploit how a vehicle captures its surroundings information to convert an automotive decision into defining the criticality and timeliness of an alert. For this, we identify the relationship between the traffic event, the type of automotive sensing technologies, and its processing resources to capture that event to design the driver's attention gauge. We evaluate the timeliness of alerts for different traffic scenarios over a prototype built using NVIDIA Jetson Xavier AGX and Carla. Our results show that we can improve the timeliness of an alert by up to 75x as compared to existing state-of-the-art approaches, while also providing feedback on its criticality.

  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