• Strong foundations in theoretical computer science, applied mathematics, and mathematical information technology coupled with the knowledge in electrical engineering and electronics
• Extensive practical work in system modeling, analysis and design

MIT in Decision Support Systems:

• Detection of abrupt changes in dynamical system models
• Linear and nonlinear programming
• Sequential methods in statistical hypotheses testing and pattern recognition

MIT in Data Processing and Control Systems:

• Ordinary and total least squares
• Digital signal spectra analysis
• Discrete time multi-input multi-output control systems
• Stochastic optimal linear estimation and control
• Adaptive filtering and control systems
• Robust methods of estimation and control

MIT in system modeling and analysis:

• Regression, pseudoinverse and recurrent estimation
• Stochastic system models estimation
• Combined navigation systems: error budget analysis and component optimization

MIT in computation:

• Traditional linear algebra computation methods
• Modern linear algebra computation methods: factorization, orthogonalization, and parallelization

Computer Science:

• Discrete Mathematics
• Computer engineering: computer organization, algorithms, and digital automata design

University Education:

• Curricula design in applied mathematics and computer-based education
• Computed-aided education methods

• Typesetting any sort of documents of high typographical quality: books, booklets, papers, reports, memos and so on including course or diploma works / theses or presentations (made in Beamer or Prosper classes)