Oleksandr Kotlyar
Title: Database Coordinator School/office: School of Science and TechnologyEmail: oleksandr.kotlyar@oru.se
Phone: +46 19 303730, +46 19 302117
Room: T2219, B2308
About Oleksandr Kotlyar
I joined Mobile Robotics and Olfaction (MR&O) and Environmental Forensic (EnForce) Laboratories at Örebro University as Database manager in October 2020. Prior this I was Marie S.-Curie MULTIPLY Postdoctoral Research Fellow at Aston Institute of Photonic Technologies, Aston University, Birmingham, UK. Before I worked as a Research Fellow in Department of Theoretical Physics at B. Verkin Institute for Low Temperature Physics and Engineering, Kharkiv, Ukraine. Also, I was a visiting scholar at Pavol Jozef Šafárik University in Košice, Slovak Republic. My higher education was obtained at V.N. Karazin Kharkiv National University, (BSc and MSc in Physics) and B. Verkin Institute for Low Temperature Physics and Engineering (PhD in Theoretical Physics).
My current work is devoted to data analysis and machine learning image processing of materials achieved from “Cell Painting”, a high-content image-based cell profiling experiments. In general, my interest involves (but not limited to) the application of machine learning techniques to the various scientific and engineering tasks.
Research teams
Publications
Articles in journals
- Alijagic, A. , Scherbak, N. , Kotlyar, O. , Karlsson, P. , Wang, X. , Odnevall, I. , Benada, O. , Amiryousefi, A. & et al. (2023). A Novel Nanosafety Approach Using Cell Painting, Metabolomics, and Lipidomics Captures the Cellular and Molecular Phenotypes Induced by the Unintentionally Formed Metal-Based (Nano)Particles. Cells, 12 (2).
- Alijagic, A. , Scherbak, N. , Kotlyar, O. , Karlsson, P. , Persson, A. , Hedbrant, A. , Norinder, U. , Larsson, M. & et al. (2022). Cell Painting unveils cell response signatures to (nano)particles formed in additive manufacturing. Toxicology Letters, 368 (Suppl. 1), S226-S227.
- Kotlyar, O. , Kamalian-Kopae, M. , Pankratova, M. , Vasylchenkova, A. , Prilepsky, J. & Turitsyn, S. (2021). Convolutional long short-term memory neural network equalizer for nonlinear Fourier transform-based optical transmission systems. Optics Express, 29 (7), 11254-11267.
- Kotlyar, O. , Pankratova, M. , Kamalian-Kopae, M. , Vasylchenkova, A. , Prilepsky, J. & Turitsyn, S. (2020). Combining nonlinear Fourier transform and neural network-based processing in optical communications. Optics Letters, 45 (13), 3462-3465.
Conference papers
- Winkler, N. P. , Kotlyar, O. , Schaffernicht, E. , Fan, H. , Matsukura, H. , Ishida, H. , Neumann, P. P. & Lilienthal, A. (2022). Learning From the Past: Sequential Deep Learning for Gas Distribution Mapping. Paper presented at ROBOT2022: Fifth Iberian Robotics Conference, Zaragoza, Spain, November 23-25, 2022.