World's Best Scientists 2026 revealed!

Overview

Robert Moskovitch is affiliated with Columbia University in the United States and has contributed extensively to the field of computer science, with a particular focus on signal processing and artificial intelligence. Their research spans various subfields including information systems, computer networks and communications, and health information management.

The scientist has published a significant number of papers, covering prominent topics such as time series analysis and forecasting, data management and algorithms, data mining applications, machine learning in healthcare, music and audio processing, anomaly detection techniques, and artificial intelligence in healthcare.

Recent publications include:

  • "Decompiled APK based malicious code classification" (2020) in Future Generation Computer Systems
  • "Outcomes prediction in longitudinal data: Study designs evaluation, use case in ICU acquired sepsis" (2021) in Journal of Biomedical Informatics
  • "Complete Closed Time Intervals-Related Patterns Mining" (2021) in Proceedings of the AAAI Conference on Artificial Intelligence
  • "Multivariate temporal data analysis - a review" (2021) in Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery
  • "Temporal pattern-based malicious activity detection in SCADA systems" (2020) in Computers & Security

Their publication history shows a pattern of contributions to notable academic venues including the Journal of Biomedical Informatics, Future Generation Computer Systems, Proceedings of the AAAI Conference on Artificial Intelligence, Artificial Intelligence in Medicine, and PLoS ONE.

Robert Moskovitch has also contributed to book publications, with at least one title published through Springer Science+Business Media titled "Artificial Intelligence in Medicine" (2020).

Collaborative work is an important aspect of their research profile. Frequent co-authors include Nevo Itzhak, Szymon Jaroszewicz, Roni Mateless, Omer David Harel, and Paulo Saldanha, reflecting ongoing partnerships within their fields of study.

Main research fields and topics:

  • Computer Science
  • Signal Processing
  • Artificial Intelligence
  • Information Systems
  • Computer Networks and Communications
  • Health Information Management
  • Time Series Analysis and Forecasting
  • Data Management and Algorithms
  • Data Mining Algorithms and Applications
  • Machine Learning in Healthcare
  • Music and Audio Processing
  • Anomaly Detection Techniques and Applications
  • Artificial Intelligence in Healthcare

The scope of their research reflects interdisciplinary engagement across computational techniques, healthcare applications, and cybersecurity aspects, indicating a broad approach within computer science and its applied domains.

Best Publications

  • Detection of malicious code by applying machine learning classifiers on static features: A state-of-the-art survey

    Asaf Shabtai;Robert Moskovitch;Yuval Elovici;Chanan Glezer

  • Detecting unknown malicious code by applying classification techniques on OpCode patterns

    Asaf Shabtai;Robert Moskovitch;Clint Feher;Shlomi Dolev

  • Unknown Malcode Detection Using OPCODE Representation

    Robert Moskovitch;Clint Feher;Nir Tzachar;Eugene Berger

  • User identity verification via mouse dynamics

    Clint Feher;Yuval Elovici;Robert Moskovitch;Lior Rokach

  • A framework for a distributed, hybrid, multiple-ontology clinical-guideline library, and automated guideline-support tools

    Yuval Shahar;Ohad Young;Erez Shalom;Maya Galperin

  • Medical temporal-knowledge discovery via temporal abstraction.

    Robert Moskovitch;Yuval Shahar

  • Detection of unknown computer worms based on behavioral classification of the host

    Robert Moskovitch;Yuval Elovici;Lior Rokach

  • Identity theft, computers and behavioral biometrics

    Robert Moskovitch;Clint Feher;Arik Messerman;Niklas Kirschnick

  • Classification-driven temporal discretization of multivariate time series

    Robert Moskovitch;Yuval Shahar

  • Unknown malcode detection via text categorization and the imbalance problem

    R. Moskovitch;D. Stopel;C. Feher;N. Nissim

  • Novel active learning methods for enhanced PC malware detection in windows OS

    Nir Nissim;Robert Moskovitch;Lior Rokach;Yuval Elovici

  • Classification of multivariate time series via temporal abstraction and time intervals mining

    Robert Moskovitch;Yuval Shahar

  • Fast time intervals mining using the transitivity of temporal relations

    Robert Moskovitch;Yuval Shahar

  • Method and system for detecting malicious behavioral patterns in a computer, using machine learning

    Robert Moskovitch;Dima Stopel;Zvi Boger;Yuval Shahar

  • Applying Machine Learning Techniques for Detection of Malicious Code in Network Traffic

    Yuval Elovici;Asaf Shabtai;Robert Moskovitch;Gil Tahan

  • DEGEL: A Hybrid, Multiple-Ontology Framework for Specification and Retrieval of Clinical Guidelines

    Yuval Shahar;Ohad Young;Erez Shalom;Alon Mayaffit

  • Unknown malcode detection and the imbalance problem

    Robert Moskovitch;Dima Stopel;Clint Feher;Nir Nissim

  • Detecting unknown computer worm activity via support vector machines and active learning

    Nir Nissim;Robert Moskovitch;Lior Rokach;Yuval Elovici

  • Continuous Verification Using Keystroke Dynamics

    Tomer Shimshon;Robert Moskovitch;Lior Rokach;Yuval Elovici

  • Identity theft, computers and behavioral biometrics

    Robert Moskovitch;Clint Feher;Arik Messerman;Niklas Kirschnick

Frequent Co-Authors

Yuval Elovici
Yuval Elovici Ben-Gurion University of the Negev
Yuval Shahar
Yuval Shahar Ben-Gurion University of the Negev
Lior Rokach
Lior Rokach Ben-Gurion University of the Negev
George Hripcsak
George Hripcsak Columbia University
Asaf Shabtai
Asaf Shabtai Ben-Gurion University of the Negev
Cesare Gessler
Cesare Gessler ETH Zurich
Ilaria Pertot
Ilaria Pertot University of Trento
Shlomi Dolev
Shlomi Dolev Ben-Gurion University of the Negev
Jian Pei
Jian Pei Duke University
Carol Friedman
Carol Friedman Columbia University

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