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Prof. Rahman Farnoosh | Image Analysis Award | Excellence in Innovation

Academic Staff at The School of Mathematics and Computer Science, Statistics, Iran University of Science and Technology, Iran

Prof. Rahman Farnoosh is an accomplished researcher and academic with expertise in the field of image analysis. He has made significant contributions to the advancement of image processing and computer vision. His work has been recognized with prestigious awards and has been published in reputable journals and conferences. Prof. Farnoosh’s innovative research has had a positive impact on the field, inspiring others and contributing to the broader scientific community.

Professional Profiles:

Education:

Prof. Rahman Farnoosh obtained his qualifications over a span of several years, beginning with a B.Sc. in Applied Mathematics from Industrial Sharif University in Tehran, Iran, which he completed from 1978 to 1986. Building on this foundation, he pursued further studies at Shiraz University in Shiraz, Iran, where he obtained an M.Sc. in Statistics from 1986 to 1989. Prof. Farnoosh’s academic journey culminated in a Ph.D. in Statistics from Leeds University in Leeds, UK, which he completed from 1996 to 2000. His educational background reflects a strong commitment to the field of mathematics and statistics, demonstrating his dedication to advancing his knowledge and expertise in these areas.

Research Experience:

Prof. Rahman Farnoosh has conducted research in Statistics and Applied Mathematics, focusing on innovative numerical algorithms and statistical methods. His projects include developing a Numerical Algorithm based on the Monte Carlo method to enhance computational efficiency, studying Image Segmentation using Voronoi Polygons and the Markov Chain Monte Carlo method for advanced image processing, and proposing a Modified Measure of Kurtosis for heavy-tail distributions. Additionally, he has explored the application of Monte Carlo methods in solving stochastic differential equations and contributed to variance reduction methods in Monte Carlo simulations. Prof. Farnoosh’s research demonstrates his interdisciplinary approach and commitment to advancing mathematical knowledge.

Teaching Experience:

At Iran University of Science and Technology, Prof. Rahman Farnoosh teaches a variety of courses in Statistics and Applied Mathematics at different academic levels. For Ph.D. students, he covers topics like Advanced Simulation, Stochastic Differential Equations, Theory of Probability, and Applications of Monte Carlo Methods. At the M.Sc. level, he teaches courses such as Theory of Probability, Stochastic Processes and Applications, and Selected Topics in Advanced Probability. For B.Sc. students, Prof. Farnoosh offers courses including Probability and Statistics, Time Series Analysis, Ordinary Differential Equations, Numerical Computations, Stochastic Processes, and Engineering Probability and Statistics. His teaching demonstrates a commitment to imparting a strong mathematical foundation to students at all levels.

Interests:

Prof. Rahman Farnoosh has a keen interest in Financial Mathematics, focusing on probabilistic approaches for solving inverse diffusion problems. His expertise also extends to Image Analysis, Bayesian Data Analysis, and Markov Chain Monte Carlo Simulation. He has applied Monte Carlo methods to solve a variety of mathematical problems, including partial differential equations, systems of linear algebraic equations, and integral equations. Additionally, Prof. Farnoosh is skilled in the numerical solution of stochastic differential equations and has a strong interest in mathematical biology, demonstrating his diverse research interests and interdisciplinary approach to mathematics.

Skills:

Prof. Rahman Farnoosh possesses a diverse skill set in Mathematics and Statistics, including advanced mathematical modeling, numerical analysis, statistical analysis, and Monte Carlo simulation. He is proficient in image analysis and programming languages such as Python, MATLAB, and R. With a strong background in teaching, mentoring, and research, Prof. Farnoosh contributes significantly to the advancement of knowledge in these fields.

Publications:

  1. Image segmentation using Gaussian mixture model
    • Authors: R. Farnoush, PAKB ZAR
    • Year: 2008
    • Citations: 99
  2. Monte Carlo method for solving Fredholm integral equations of the second kind
    • Authors: R. Farnoosh, M Ebrahimi
    • Year: 2008
    • Citations: 84
  3. A stochastic perspective of RL electrical circuit using different noise terms
    • Authors: R. Farnoosh, P Nabati, R Rezaeyan, M Ebrahimi
    • Year: 2011
    • Citations: 40
  4. A semiparametric method for estimating nonlinear autoregressive model with dependent errors
    • Authors: R. Farnoosh, SJ Mortazavi
    • Year: 2011
    • Citations: 26
  5. Fuzzy nonparametric regression based on an adaptive neuro-fuzzy inference system
    • Authors: S Danesh, R Farnoosh, T Razzaghnia
    • Year: 2016
    • Citations: 25
  6. Numerical method for discrete double barrier option pricing with time-dependent parameters
    • Authors: R Farnoosh, A Sobhani, H Rezazadeh, MH Beheshti
    • Year: 2015
    • Citations: 23
  7. Image segmentation using Voronoi polygons and MCMC, with application to muscle fibre images
    • Authors: IL Dryden, R Farnoosh, CC Taylor
    • Year: 2006
    • Citations: 21
  8. Fuzzy regression analysis based on fuzzy neural networks using trapezoidal data
    • Authors: R Naderkhani, MH Behzad, T Razzaghnia, R Farnoosh
    • Year: 2021
    • Citations: 20
  9. Monte Carlo method via a numerical algorithm to solve a parabolic problem
    • Authors: R Farnoosh, M Ebrahimi
    • Year: 2007
    • Citations: 19
  10. A numerical method for discrete single barrier option pricing with time-dependent parameters
    • Authors: R Farnoosh, H Rezazadeh, A Sobhani, MH Beheshti
    • Year: 2016
    • Citations: 18

 

Rahman Farnoosh | Image Analysis Award | Excellence in Innovation

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