Statistics and Probability

 The program is given in Hebrew

General Information

There are two main tracks for graduate studies in statistics:

  1. Applied and theoretical statistics
  2. Probability and stochastic processes

Program Academic Staff

The Technion has a varied group of Probability and Statistics researchers. Their fields of study include:

  • Statistical theory, multiple comparisons, applied statistics;
  • Queue theory, service systems, and data processing as it relates to the analysis and planning of these systems;
  • Stochastic processes in a number of contexts:
    1. Stochastic processes and their uses in physics;
    2. Gaussian processes;
    3. Markov processes and regenerative processes;
    4. Stochastic systems;
    5. Stochastic analysis (stochastic partial differential equations);
    6. Interacting particle systems;
    7. Diffusion measurement systems and their uses.

After Graduation

M.Sc. graduates of either track can expect to find work in industry either as statisticians or in stochastic operations research (e.g., in mathematical finance, service systems planning and control, or combinations of the above with large data systems). Graduates who excel in their master’s studies will be eligible to continue towards a doctorate, should they so wish, with a view to pursuing a career in academia.

Program Length and Structure

The M.Sc. program comprises two years of studies, held in the morning, and combines coursework with submission of a research thesis or final project, depending on the chosen track. The tracks and program requirements are described in more detail under M.Sc. Studies, below. 

Research Topics  

The Faculty encourages interdisciplinary research and cross-field cooperation. A student from any study field may choose an academic supervisor from the faculty staff.

  • Artificial Intelligence (neural networks, computational learning, natural language processing…)
  • Electronic Markets and Electronic Commerce
  • Viral Processes (viral, ideas..) in human or computer communities
  • Cognitive Robots (artificial intelligence and robotics)
  • Game Theory and Decision Making
  • Software Engineering (complex systems modeling and analysis)
  • Knowledge and Data (information) Management
  • Mathematical Logic and program verification
  • Probability and Stochastic Processes
  • Service Engineering (queue systems in hospitals, banks etc.)
  • Data-based Operation Research
  • Efficient Optimization
  • Behavioral Economics
  •  Marketing and Strategic Theories
  •  Supply Chain in Dynamic and Multi-data environment.
  •  Various subjects in Information and Data Science. 
  • Network algorithms
  • Distributed algorithms
  • Self stabilizing systems
  • Optimization in the face of uncertainty
  • Network security and fault tolerance