George Cybenko headshot

George Cybenko

Dorothy and Walter Gramm Professor of Engineering

Education

  • BSc, Mathematics, University of Toronto 1974
  • MA, Mathematics, Princeton University 1975
  • PhD, Mathematics, Princeton University 1978

Research Interests

Information systems and theory

Awards

  • SPIE Eric A. Lehrfeld Award, 2016
  • USAF Commander’s Public Service Award, 2016

Courses

  • ENGS 27: Discrete and Probabilistic Systems
  • ENGS 108: Applied Machine Learning

Patents

  • System and methods for encrypted execution of computer programs | 7,296,163

Startups

FlowTraq
Co-Founder and Chief Scientist

Research Projects

  • Process query systems

    Process query systems

    Process query systems have applications that involve using databases or datastreams of events to detect instances of processes. In those applications, events provide evidence that is used to infer the existence and estimate the states of the various processes of interest. Examples of such applications include: network and computer security; network management; sensor network tracking; military situational awareness; and critical infrastructure monitoring and protection.

  • Insider threat

    Insider threat

    Insider threat and deception detection are two areas that focus on user actions and their impacts upon the systems with which they interact. Insider threat aims to understand and prevent malicious activities that are instigated by "trusted" users on complex computer/information systems. Such activities cover a broad spectrum ranging from simple theft of confidential data to the more subtle alteration of system performance and/or information. For the latter, examples can include minor perturbation of a component specification in a manufacturing process resulting in a rippling effect of final component failure to influencing the decision-makers by modifying their information flow and content. The goal is to model insider threat in order to predict behavior and ultimately infer their goals and intentions.

  • Deception detection

    Deception detection

    Deception detection aims to automatically detect and infer the intentions behind deceptive actions. Our objectives are to 1) develop a framework for categorizing and classifying errors that may be committed by an expert, since not all errors are deception; and 2) design algorithms for automatic deception detection capable of providing detailed evidential information and explanation of deception intent, plus analysis of the deception's impact. Like insider threat, deception detection can occur in any number of scenarios and domains, and insider threat and deception detection are often interrelated.

  • Agent-based systems engineering

    Agent-based systems engineering

    Agent-based systems engineering aims to successfully cross-fertilize the fields of systems engineering and artificial intelligence. Systems engineering (control, signal processing and communications) focuses primarily on physical domains that can be characterized by rich mathematical dynamics while artificial intelligence deals with human perception, decision making and action. Goals of such cross-fertilization are to explore the modeling, performance and scientific foundations of software agent systems using ideas from classical systems engineering and computer engineering.

Videos

Seminar: Computational Behavioral Analysis

News