Illya BakurovPhd Student

DISCo (Department of Informatics, Systems and Communication)
University of Milan-Bicocca
Viale Sarca 336, Building U14

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Biography

Illya Bakurov obtained the BSc in Information Management and the MSc degree in Data Science and Advanced Analytics from the NOVA University of Lisbon, Portugal, respectively in 2015 and 2018. In 2015 he did an internship at QMetrics. He is currently a PhD student from NOVA University of Lisbon. The main topics of his current research concern Machine Learning and Evolutionary Computation. Recently he started to work in the field of Image Processing and Computer Vision at IVL.

Publications

Journal Articles

1.

General Purpose Optimization Library (GPOL): A Flexible and Efficient Multi-Purpose Optimization Library in Python
(Illya Bakurov, Marco Buzzelli, Mauro Castelli, Leonardo Vanneschi, Raimondo Schettini) In Applied Sciences, volume 11, number 11, 2021.

@article{bakurov2021general,
 author = {Bakurov, Illya and Buzzelli, Marco and Castelli, Mauro and Vanneschi, Leonardo and Schettini, Raimondo},
 year = {2021},
 title = {General Purpose Optimization Library (GPOL): A Flexible and Efficient Multi-Purpose Optimization Library in Python},
 volume = {11},
 number = {11},
 journal = {Applied Sciences},
 url = {https://www.mdpi.com/2076-3417/11/11/4774},
 pdf = {/download/2021c_General_Purpose_Optimization_Library_GPOL_A_Flexible_and_Efficient_Multi-Purpose_Optimization_Library_in_Python.pdf},
 doi = {10.3390/app11114774},
 issn = {2076-3417},
 projectref = {https://gitlab.com/ibakurov/general-purpose-optimization-library}}

Conference Papers

1.

Parameters optimization of the Structural Similarity Index
(Illya Bakurov, Marco Buzzelli, Mauro Castelli, Raimondo Schettini, Leonardo Vanneschi) In London Imaging Meeting, volume 2020, number 1, pp. 19-23, 2020.

@inproceedings{bakurov2020parameters,
 author = {Bakurov, Illya and Buzzelli, Marco and Castelli, Mauro and Schettini, Raimondo and Vanneschi, Leonardo},
 year = {2020},
 pages = {19-23},
 title = {Parameters optimization of the Structural Similarity Index},
 volume = {2020},
 number = {1},
 organization = {Society for Imaging Science and Technology},
 booktitle = {London Imaging Meeting},
 pdf = {/download/2020a_Parameters_optimization_of_the_Structural_Similarity_Index.pdf},
 doi = {10.2352/issn.2694-118X.2020.LIM-13}}