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My research interests are monotonic regression and interpolation, data mining and machine learning methods (primarily decision trees, kernel smoothing, neural networks), data mining and machine learning in public health, machine learning for telecommunication systems, bootstrap methods, computational statistics, optimization. My main ongoing projects are data mining and machine learning in public health (focus on personalized medicine) and machine learning methods for efficient, reliable and scalable telecommunication systems.

My journal publications:

  • Lee EJ, Gawel D, Lilja S, Li X, Schäfer S, Sysoev O, Zhang H and Benson M. . Analysis of expression profiling data suggests explanation for difficulties in finding biomarkers for nasal polyps. Accepted to Rhinology (2020).
  • Björnsson B, Borrebaeck C, Elander N, Gasslander T, Gawel DR, Gustafsson M, Jörnsten R, Lee EJ, Li X, Lilja S, Martínez-Enguita D, Matussek A, Sandström P, Schäfer S, Stenmarker M, Sun XF, Sysoev O, Zhang H, Benson M. Digital twins to personalize medicine. (2020) Genome Med 12, 4, doi: 10.1186/s13073-019-0701-3
  • Källestål C., Blandón E.Z., Peña R., Peréz W., Contreras M., Persson L.Å., Sysoev O. Selling, K.E.: Assessing the Multiple Dimensions of Poverty. Data Mining Approaches to the 2004–14 Health and Demographic Surveillance System in Cuatro Santos, Nicaragua. Frontiers in Public Health (2020) vol 7, pp 409. doi: 10.1186/s12939-019-1054-7
  • Källestål C., Blandón E.Z., Peña R., Peréz W., Contreras M., Persson L.Å., Sysoev O. Selling, K.E.: Predicting poverty. Data mining approaches to the health and demographic surveillance system in Cuatro Santos, Nicaragua. International Journal for Equity in Health (2019) vol 18, no 165. doi: 10.3389/fpubh.2019.00409
  • Svefors P., Sysoev O., Ekström E.C., Person L.Å., El Arifeen S., Naved R., Rahman A., Islam Khan A., Ekholm Selling K.: Relative importance of prenatal and postnatal determinants of stunting: data mining approaches to the MINIMat cohort, Bangladesh. BMJ Open (2019) vol 9:e025154. doi: 10.1136/bmjopen-2018-025154
  • Sysoev. O, Bartoszek K., Ekstrom EC and Ekholm Selling K. PSICA: decision trees for probabilistic subgroup identification with categorical treatments. Statistics in Medicine (2019), pp. 1– 17. doi : 10.1002/sim.8308
  • Sysoev, O., Burdakov, O. A smoothed monotonic regression via l2 regularization. Knowledge and Information Systems (2018), 1-22.
  • Burdakov, O., Sysoev, O. A Dual Active-Set Algorithm for Regularized Monotonic Regression. Journal of Optimization Theory and Applications 172.3 (2017): 929-949.
  • Kalish, M.L, Dunn J.C., Burdakov O. and Sysoev O.: A statistical test of the equality of latent orders Journal of mathematical psychology (2016), vol 70, pp 1-11.
  • Sysoev, O., Grimvall, A., and Burdakov, O..: Bootstrap confidence intervals for large-scale multivariate monotonic regression problems. Statistics-Simulation and Computation (2015), pp 1-16.
  • Sysoev, O., Grimvall, A., and Burdakov, O.: Bootstrap estimation of the variance of the error term in monotonic regression models. Journal of Statistical Computation and Simulation 83.4 (2013): pp 627-640.
  • Sysoev, O., Burdakov, O., Grimvall, A.: A Segmentation-Based Algorithm for Large-Scale Monotonic Regression Problems. Computational Statistics and Data Analysis 55 (2011), pp. 2463-2476
  • Burdakov, A. Grimvall and O. Sysoev. Data preordering in generalized PAV algorithm for monotonic regression. Journal of Computational Mathematics. (2006) 24, No. 6, pp. 771-790.
  • Burdakov O. , Sysoev O. ,Grimvall A. and Hussian M. An O(n2) algorithm for isotonic regression. In: G. Di Pillo and M. Roma (Eds) Large-Scale Nonlinear Optimization. Series: Nonconvex Optimization and Its Applications, Springer-Verlag, (2006) 83, pp. 25-33.
  • Hussian M. ,Grimvall A. ,Burdakov O. and Sysoev O. Monotonic regression for the detection of temporal trends in environmental quality data. MATCH Commun. Math. Comput. Chem. (2005) 54, pp. 535-550.

My conference publications:

  • Svahn, C., Sysoev, O., Cirkic M., Gunnarsson F. and Berglund J.: Inter-frequency radio signal quality prediction for handover, evaluated in 3GPP LTE. 2019 IEEE 89th Vehicular Technology Conference (VTC2019-Spring), Kuala Lumpur, Malaysia (2019), pp. 1-5. doi: 10.1109/VTCSpring.2019.8746369
  • Sysoev, O.: Estimating binary monotonic regression models and their uncertainty by incorporating kernel smoothers. Complex Data Modelling and Computationally Intensive Statistical Methods for Estimation and Prediction conference (2013).
  • O. Burdakov, A. Grimvall and O. Sysoev. Generalized PAV algorithm with block refinement for partially ordered monotonic regression. In: A. Feelders and R. Potharst (Eds.) Proceedings of the Workshop on Learning Monotone Models from Data at the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (2009), pp. 23-37.
  • O. Burdakov, O. Sysoev, A. Grimvall and M. Hussian (2004). An algorithm for isotonic regression problems. In: The Proceedings of the 4th European Congress of Computational Methods in Applied Science and Engineering `ECCOMAS 2004'.
  • M. Hussian, A. Grimvall, O. Burdakov and O. Sysoev (2004). Monotonic regression for trend
    assessment of environmental quality data.
    In: The Proceedings of the 4th European Congress of
    Computational Methods in Applied Science and Engineering `ECCOMAS 2004'

Page responsible: Oleg Sysoev
Last updated: 2020-02-19