School of Science, Engineering and Information Technology

Taheri, Sona (Dr) - Research

Publications/conferences

Books

  1. Bagirov A.M., Karmitsa N., Taheri S. (2019) Partitional Clustering via Nonsmooth Optimization,Springer, in press
  2. Bagirov A.M., Gaudioso M., Karmitsa N., Makela M.M., Taheri S. (eds.) (2019) Numerical Nonsmooth Optimization, State-of-the-Art Algorithms, Springer, in production
  3. Taheri S., Roomi V., Seifollahi S. (2005) Calculus I, Soroosh-e-Danesh Publications (in Persian)
  4. Taheri S., Seifollahi S. (2005) Numerical Analysis I, Adena Publications (in Persian)

Book chapters

  1. Bagirov A.M., Karmitsa N., Taheri S., (2019) Discrete Gradient Methods, In: Bagirov A.M., Gaudioso, M., Karmitsa N., Makela M.M., Taheri S. (eds.) Numerical Nonsmooth Optimization, State-of-the-Art Algorithms, Springer, in press.
  2. Bagirov A.M., Taheri S., Bai F., Wu Z. (2019) An Approximate ADMM for Solving Linearly Constrained Nonsmooth Optimization Problems with Two Blocks of variables, In: Hosseini S., Mordukhovich B., Uschmajew A. (eds.) Nonsmooth Optimization and Its Applications, International Series of Numerical Mathematics, Birkhauser, Cham, Vol. 170, pp. 17–44
  3. Bagirov A.M., Sultanova N., Al Nuaimat A., Taheri S. (2018) Solving minimax problems: Local Smoothing Versus Global Smoothing, In: M. Al-Baali, L. Grandinetti, A. Purnama (eds.) Numerical Analysis and Optimization, Springer Proceedings in Mathematics and Statistics book series PROMS, Vol. 235, pp. 23–43, 2018

Journal articles

  1. Bagirov A.M., Taheri S., Asadi, S. (2019) A Difference of Convex Optimization Algorithm for Piecewise Linear Regression, Journal of Industrial and Management Optimization (JIMO), 15(2), 909–932
  2. Karmitsa N., Bagirov A.M., Taheri S. (2018) Clustering in Large Data sets with the Limited Memory Bundle Method, Pattern Recognition, 83, 245–259
  3. Joki K., Bagirov A.M., Karmitsa N., Makela M.M., Taheri S. (2018) Double Bundle Method for Finding Clarke Stationary Points in Nonsmooth DC-Programming, SIAM Journal on Optimization, 28(2), 1892–1919
  4. Karmitsa N., Bagirov A.M., Taheri S. (2017) New Diagonal Bundle Method for Clustering Problems in Large Data Sets, European Journal of Operational Research (EJOR), 263 (2), 367–379
  5. Bagirov A.M., Taheri S. (2017) An Algorithm based on Optimization for Clustering Data using an L1-norm, International Journal of Operations Research (IJOR), 8(2), 2–24
  6. Bagirov A.M., Taheri S. (2016) DC Programming Algorithm for Clusterwise Linear L1-Regression, Journal of the Operations Research Society of China (JORC), 5(2), 233–256
  7. Bagirov A.M., Taheri S., Ugon, J. (2016) Nonsmooth DC Programming Approach to the Minimum Sum-of-Squares Clustering Problems, Pattern Recognition, 53, 12–24
  8. Taheri S., Mammadov M. (2015) Structure Learning of Bayesian Networks using Global Optimization with Applications in Data Classification. Optimization Letters, 9(5), 931–948
  9. Taheri S., Yearwood J., Mammadov M., Seifollahi S. (2014) Attribute Weighted Naive Bayes Classifier Using a Local Optimization, Neural Computing and Applications, 24(5), 995–1002
  10. Taheri S., Mammadov M. (2013) Learning Naive Bayes Classifier with Optimization Models, International Journal of Applied Mathematics and Computer Science, 23(4), 787–795
  11. Taheri S., Mammadov M. (2012) Solving Systems of Nonlinear Equations using a Globally Convergent Optimization Algorithm, Global Journal of Technology and Optimization, 3,132–138
  12. Taheri S., Mammadov M., Seifollahi S. (2012) Globally Convergent Optimization Methods for unconstrained Problems, Optimization: A Journal of Mathematical Programming and Operations Research, 64(2), 249–263.

Conference articles

  1. Taheri S., Gondal, I., Bagirov A.M., Harkness, G., Brown, S., Chi, Ch. (2019) Multi-Source Cyber-Attacks Detection using Machine Learning, 20th IEEE International Conference on Industrial Technology, Melbourne, Australia
  2. Taheri S., Mammadov M. (2012) Structure Learning of Bayesian Networks using a New Unrestricted Dependency Algorithm, 2nd International Conference on Social Eco-Informatics (SOTICS), Venice, Italy
  3. Taheri S., Mammadov M., Bagirov A.M. (2011) Improving Naive Bayes Classifier using Conditional Probabilities, 9th Australian Data Mining Conference (AusDM), University of Ballarat, Australia
  4. Taheri S., Mammadov M. (2011) Tree Augmented Naive Bayes Classifier based on Optimization, 42nd Annual Iranian Mathematics Conference, Vali-e-Asr University of Rafsanjan, Iran
  5. Mammadov M., Taheri S. (2010) Globally Convergent Optimization Algorithm for Systems of Non-Linear Equations, 3rd Global Conference on Power Control and Optimization, Gold Coast, Australia
  6. Mirnia M.K., Taheri S. (2008) Solving Nonlinear Integro-differential Equations by using Artificial Neural Networks, 39th Annual Iranian Mathematics Conference, Kerman, Iran

Technical reports

  1. Bagirov A.M., Taheri S., Joki K., Karmitsa N., Makela M.M. (2019) A New Subgradient Method for Nonsmooth DC Programming, TUCS Technical Report, No. 1201, Turku Centre for Computer Science, Turku, Finland
  2. Karmitsa N., Taheri S., Bagirov A.M., Makinena P. (2018) Clusterwise Linear Regression based Missing Value Imputation for Data Preprocessing, TUCS Technical Report, No. 1193, Turku Centre for Computer Science, Turku, Finland
  3. Joki K., Bagirov A.M., Karmitsa N., Makela, M.M., Taheri S. (2017) New Bundle Method for Clusterwise Linear Regression utilizing Support Vector Machines, TUCS Technical Report, No 1190, Turku Centre for Computer Science, Turku, Finland
  4. Karmitsa N., Bagirov A.M., Taheri S. (2016) Limited Memory Bundle Method for Solving Large Clusterwise Linear Regression Problems, TUCS Technical Report, No. 1172 Turku Centre for Computer Science, Turku, Finland
  5. Karmitsa N., Bagirov A.M., Taheri S. (2016) MSSC Clustering of Large Data using the Limited Memory Bundle Method, TUCS Technical Report, No. 1164, Turku Centre for Computer Science, Turku, Finland
  6. Karmitsa N., Bagirov A.M., Taheri S. (2016) Diagonal Bundle Method for Solving the Minimum Sum-of-Squares Clustering Problems, TUCS Technical Report, No. 1156, Turku Centre for Computer Science, Turku, Finland

Submitted or in preparation

Books

  1. Bagirov A.M., Joki K., Karmitsa N., Makela M.M., Taheri S., Nonsmooth DC Optimization, in preparation

Articles

  1. Bagirov A.M., Taheri S., Joki K., Karmitsa N., Makela M.M. Aggregate Subgradient Method for Nonsmooth DC Optimization,  submitted
  2. Taheri S., Bagirov A.M., Gondal, I., Brown, S. Cyber-Attack Triage using Incremental Clustering for Intrusion Detection Systems, submitted
  3. Joki K., Bagirov A.M., Karmitsa N., Makela M.M., Taheri S., Clusterwise support vector linear regression, submitted
  4. Bagirov A.M., Taheri S., Karmitsa N., Joki K., Makela M.M., Adaptive piecewise Piecewise Linear  Support Vector Regression, in preparation
  5. Karmitsa N., Taheri S., Bagirov A.M., Makinen, P., Missing Value Imputation in Large Data Sets via Nonsmooth Clusterwise Linear Regression, in preparation
  6. Bagirov A.M., Cimen, E., Taheri S., A DC Optimization Algorithm for Clusterwise Linear Regression, in preparation
  7. Karmitsa N., Bagirov A.M., Taheri S., Joki K., Limited Memory Bundle Method for Clusterwise Linear Regression, in preparation
  8. Bagirov A.M., Taheri S., Asadi, S., Trust Region Method for Nonsmooth Optimization using Piecewise Linear Approximations, in preparation
  9. Bagirov A.M., Taheri S., Karmitsa, N., Sultanova, N., Asadi, S., Piecewise Linear Regreesion, A DC Optimization Approach, in preparation
  10. Bagirov A.M., Sultanova N., Taheri S., Ozturk, G., Subgradient Smoothing Method for Nonsmooth Nonconvex Optimization, in preparation
  11. Bagirov A.M., Ordin, B., Taheri S., Study of Incremental k-Medians Clustering Algorithm, in preparation
  12. Astorino, A., Fuduli, F., Joki K., Karmitsa N., Makela M.M., Taheri S., Semi Supervised SVM based on DC Programming, in preparation

Participations to conferences and workshops

  • EURO 2019, 30th European Conference on Operational Research, Dublin, Ireland (2019), Adaptive Piecewise Linear Support Vector Regression
  • University of Turku, Turku, Finland (2019) Clustering via Nonsmooth Optimization
  • University of Calabria, Rende, Italy (2018) Nonsmooth DC Optimization Algorithm for Solving Clustering Problems
  • ISMP 2018, The 23rd International Symposium on Mathematical Programming, Bordeaux, France (2018) Piecewise Linear Regression via Nonsmooth DC Optimization
  • RMITOpt, RMIT University, Melbourne, Australia (2018) Optimization based Clustering
  • Algorithm and its Application in Cyber Security
  • Variational Analysis Down Under Conference (VADU2018), Federation University Australia (2018) An Approximate ADMM for Solving Linearly Constrained Nonsmooth Problems with Two Blocks of Variables
  • The Second Pacific Optimization Conference (POC2017), Curtin University, Perth, Australia (2017) A Nonsmooth DC Optimization Algorithm for Piecewise Linear Regression
  • WoMBat, Workshop in Metric Bounds and Transversality, RMIT University, Melbourne, Australia (2017) A DC Optimization Algorithm for Piecewise Linear Regression
  • SIAM Conference on Optimization, Vancouver, Canada (2017) Nonsmooth DC Optimization Algorithm for Clusterwise Linear L1-Regression
  • Invited Speaker in HCM Workshop, Nonsmooth Optimization and its Applications, Bonn, Germany (2017) A DC Optimization Algorithm for Clusterwise Linear Regression
  • The 10th international Conference of Iranian Operations Research Society, Babolsar, Iran (2017) DCA Algorithm for Clusterwise Linear Regression and its Comparison
  • Invited Speaker in Tabriz University, Tabriz, Iran (2017) DC Programming Algorithm for Clusterwise Linear L1-Regression
  • CIAO showcase and workshop, Federation University Australia (2017) DC programming Algorithm for Clusterwise Linear L1-Regression
  • 15th Birthday Celebration Workshop, Federation University Australia (2016) A DC Optimization Algorithm for Clusterwise Linear Regression
  • Workshop on Continuous Optimization: Theory, Methods and Applications, Federation University Australia (2015) Nonsmooth DC Programming Approach to the Minimum Sum-of-Squares Clustering Problems
  • Second International Conference on Social Eco-Informatics, Venice, Italy (2012) Structure Learning of Bayesian Networks using a New Unrestricted Dependency Algorithm
  • Fifty sixth Annual Meeting, Australian Mathematical Society, Federation University Australia (2012) A New Algorithm to Learn Structure of Bayesian Networks
  • Ninth Australasian Data Mining Conference, AusDM, Federation University Australia (2011) Improving Naive Bayes Classifier using Conditional Probabilities
  • The AMSI Winter School in the Mathematical Sciences, University of Queensland, Brisbane, Australia (2011) Bayesian Network Models based on Optimization
  • Annual research conference, Federation University Australia (2010), Globally Convergent Optimization Algorithm for Systems of Non-Linear Equations
  • Thirty ninth Annual Mathematics Conference, University of Kerman, Iran (2008) Solving Nonlinear Integrodifferential Equations by using Artificial Neural Networks

Research visits

  • University of Turku, Finland (2019, 10 days)
  • University of Turku, Finland (2018-2019, 5 months)
  • University of Turku, Finland (2017, 2 weeks)
  • University of Tabriz, Iran (2017, 1 week)

Academic activities

  • Guest Editor in the Special Issue of "Algorithms" Journal (ISSN 1999-4893) on "Nonsmooth Optimization", 2019
  • Technical program committee Hot Topics in Networking Track in the 29th International Conference on Computer Communications and Networks (ICCCN 2020), Honolulu, Hawaii, USA
  • Program committee 5th International Conference on Dependability in Sensor, Cloud, and Big Data Systems and Applications (DependSys 2019) Guangzhou, China
  • Secretary in IEEE Computational Intelligence Society (CIS), Victorian Section 2018

Professional memberships and associations

  • Women in STEM (Science, Technology, Engineering and Maths)
  • Centre for Informatics and Applied Optimization (CIAO)
  • Society for Industrial and Applied Mathematics (SIAM)
  • Institute of Electrical and Electronics Engineers (IEEE)

Research interests

  • Nonsmooth optimisation
  • Nonconvex optimisation
  • DC optimisation
  • Operations research
  • Data mining
  • Machine learning
  • Deep learning
  • Cyber security

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