orogov

Oleg Rogov

Research Scientist
Laboratory of Computational Intelligence
Center for Artificial Intelligence Technology

Dr. Oleg Y. Rogov is the Senior Research Scientist at the Laboratory of Computational Intelligence. He earned his candidate of sciences degree in physics and mathematics at the Russian Academy of Sciences (Moscow, Russia) in 2020, and Specialist degree in Applied Physics and Mathematics at the Lomonosov Moscow State University (Moscow, Russia) in 2012. Head of the Reliable and Secure Intelligent Systems Group at AIRI where his team focuses on delivering critical solutions in Trustworthy AI.

Prior to joining Skoltech, Dr. Rogov had been a Senior data analyst at the Innoscripta Gmbh. (Munich, Germany), where he had been leading various projects ranging from computer vision and natural language processing to financial risks management. He had been a visiting researcher at the Keldysh Institute of Applied Mathematics of Russian Academy of Sciences where he developed hybrid machine translation solutions. His industrial background also includes a delivery of critical data science solutions at the JSC DOM.RF Bank. 

Dr. Rogov has established a new theoretical, experimental and computational approach for developing ultrachiral metamaterials, resulting in impactful publications in reputable journals, such as Physical Review Letters, Applied Physics Letters and Nature Scientific Reports. His scientific record includes more than 30 peer-reviewed publications and 3 international patents.

 

 

Active projects:

  1. Joint Skoltech-University of Sharjah project focused on the diabetic retinopathy detection.
  2. Trustworthy AI: differential privacy and adversarial robustness.

Startups:

  1. “Vein CV” LLC – Development of the medical AI devices.

Completed projects:

  1. Skoltech-MIT compressed sensing project.
  2. Controlling Dynamics of Complex Systems: Nonlinear Techniques vs Reinforcement Learning.
  3. STRIP Project – VeinCV  – a subcutaneous vein scanner with embedded AI.
  4. CoE IoT Telemedicine – server-based multimodal medical data analytics.
  1. DeepLOC: Deep Learning-based Bone Pathology Localization and Classification in Wrist X-ray Images
    R Dibo, A Galichin, P Astashev, DV Dylov, OY Rogov, accepted to AIST-2023
  2. Detect to Focus: Latent-Space Autofocusing System with Decentralized Hierarchical Multi-Agent Reinforcement Learning
    A Anikina, OY Rogov, DV Dylov, IEEE Access (2023)
  3. Optimal MRI undersampling patterns for ultimate benefit of medical vision tasks,
    A Razumov, O Rogov, DV Dylov, Magnetic Resonance Imaging 103, 37-47
  4. Medical image captioning via generative pretrained transformers, A Selivanov, OY Rogov, D Chesakov, A Shelmanov, I Fedulova, DV Dylov Nature Scientific Reports 13 (1), 4171 (2023)
  5. Optimal MRI Undersampling Patterns for Pathology Localization. Razumov, A., Rogov, O.Y., Dylov, D.V. MICCAI 2022. Lecture Notes in Computer Science (2022)
  6. Autofocusing+: Noise-Resilient Motion Correction in Magnetic Resonance Imaging. Kuzmina, E., Razumov, A., Rogov, O.Y., Adalsteinsson, E., White, J., Dylov, D.V. MICCAI 2022. Lecture Notes in Computer Science (2022)
  7. Adaptive Denoising and Alignment Agents for Infrared Imaging VM Leli, V Shipistin, OY Rogov, A Sarachakov, DV Dylov IEEE Control Systems Letters 2021
  8. Decentralized Autofocusing System with Hierarchical Agents A Anikina, OY Rogov, DV Dylov arXiv preprint arXiv:2108.12842 2021
  9. Deep negative volume segmentation K Belikova, OY Rogov, A Rybakov, MV Maslov, DV Dylov Scientific Reports 11 (1), 1-11 2021
  10. Optimal MRI Undersampling Patterns for Ultimate Benefit of Medical Vision Tasks A Razumov, OY Rogov, DV Dylov arXiv preprint arXiv:2108.04914 2021
  11. Ex-vivo-to-In-vivo Learning in Cardiology AM Zolotarev, OY Rogov, AV Mikhailov, JD Hummel, VV Fedorov, and others MIDL-2021
  12. Near-Infrared-to-Visible Vein Imaging via Convolutional Neural Networks and Reinforcement Learning VM Leli, A Rubashevskii, A Sarachakov, O Rogov, DV Dylov2020 ICARCV 2020
  13. Second Harmonic Generation in Arrays of Nanoholes in a Silver Film IA Kolmychek, EA Mamonov, AA Ezhov, OY Rogov, VV Artemov, and others Journal of Experimental and Theoretical Physics 131 (4), 558-565 2020
  14. LORCK: Learnable Object-Resembling Convolution Kernels E Lazareva, O Rogov, O Shegai, D Larionov, DV Dylov arXiv preprint arXiv:2007.05103 2020
  15. On the influence of foreign media on the Russian stock market: Text analysis E Fedorova, I Demin, D Afanasyev, O Rogov Ekonomika i matematicheskie metody 56 (2), 77-89 2020
  16. Second Harmonic Generation in Chiral Nanoholes IA Kolmychek, EA Mamonov, NV Mitetelo, AA Ezhov, OY Rogov, and others Frontiers in Optics, JTu4A. 77 2020
  17. Applications of the sentiment tonality lexicons for textual analysis Demin I.S., Fedorova E.A., Rogov O.Y. Applied Informatics 14 (1), 5-16 2019
  18. On the impact of news tonality in international media on the Russian ruble exchange rate: Textual analysis D Afanasyev, E Fedorova, O Rogov HSE Economic Journal 23 (2), 264-289 2019
  19. Chiral visible light metasurface patterned in monocrystalline silicon by focused ion beam MV Gorkunov, OY Rogov, AV Kondratov, VV Artemov, RV Gainutdinov, and others Scientific reports 8 (1), 1-10 2018
  20. The impact of news on the MICEX Oil & Gas index: textual analysis Fedorova E.A., Rogov O.Y., Kluchnikov V.A. The Moscow University Economics Bulletin 4 (1), 79-99 2018
  21. Assessment of the Quality of Education in Russian Regions EA Fedorova, SO Musienko, FY Fedorov, OY Rogov Regional Economics: Theory and Practice 16 (2), 249-262 2018
  22. Latent Dirichlet allocation assisted time series of financial news sentiments OY Rogov, EA Fedorova, IS Demin XIX April International Academic Conference On Economic and Social Development 2018
  23. FIB‐fabricated complex‐shaped 3D chiral photonic silicon nanostructures OY Rogov, VV Artemov, MV Gorkunov, AA Ezhov, DN Khmelenin Journal of microscopy 268 (3), 254-258 2017
  24. AFM reconstruction of complex-shaped chiral plasmonic nanostructures AV Kondratov, OY Rogov, RV Gainutdinov Ultramicroscopy 181, 81-85 2017
  25. Assessment of the quality of health in the Russian Federation regions EA Fedorova, LI Chernikova, OY Rogov, Regional Economics: Theory and Practice, 11 (446),  2017
  26. 3D-chiral transparent single-crystal silicon metasurface for visible light MV Gorkunov, OY Rogov, AV Kondratov, VV Artemov, AA Ezhov 2017 11th International Congress on Engineered Materials Platforms for Novel 2017
  27. Atomic force microscopy reconstruction of complex-shaped chiral plasmonic nanostructures AV Kondratov, OY Rogov, RV Gainutdinov arXiv preprint arXiv:1608.04648 2016
  28. Extreme optical chirality of plasmonic nanohole arrays due to chiral Fano resonance AV Kondratov, MV Gorkunov, AN Darinskii, RV Gainutdinov, OY Rogov, and others, Physical Review B 93 (19), 195418 2016
  29.  Plasmonic hole arrays with extreme optical chirality in linear and nonlinear regimes MV Gorkunov, AV Kondratov, AN Darinskii, VV Artemov, OY Rogov, and others Metamaterials X 9883, 98830E 2016
  30. Plasmonic nature of extreme optical chirality of subwavelength hole arrays AV Kondratov, MV Gorkunov, RV Gainutdinov, OY Rogov 2015 9th International Congress on Advanced Electromagnetic Materials 2015
  31. Fabrication of complex shape 3D photonic nanostructures by FIB lithography OY Rogov, VV Artemov, MV Gorkunov, AA Ezhov, SP Palto 2015 (IEEE-NANO), 136-139 2015
  32. Implications of the causality principle for ultra chiral metamaterials MV Gorkunov, VE Dmitrienko, AA Ezhov, VV Artemov, OY Rogov Scientific reports 5 (1), 1-5 2015
  33. Optical activity and circular dichroism of 3D-chiral holes: Symmetry, causality, reciprocity and reversibility aspects MV Gorkunov, AA Ezhov, VV Artemov, OY Rogov, SP Palto 2014 8th International Congress on Advanced Electromagnetic Materials 2014
  34. Causality relations for materials with strong artificial optical chirality MV Gorkunov, VE Dmitrienko, AA Ezhov, VV Artemov, OY Rogov arXiv preprint arXiv:1408.4977 2014
  35. Extreme optical activity and circular dichroism of chiral metal hole arrays MV Gorkunov, AA Ezhov, VV Artemov, OY Rogov, SG Yudin Applied Physics Letters 104 (22), 221102 2014
  • The Lomonosov Moscow State Univeristy (2012) – Physics
  • Higher School of Economics (2016) – Quantitative finance
  • The Russian Academy of Sciences (2020) – Candidate of sciences

Skoltech:

Kaggle competition medal from the American Society of Heating, Refrigerating and Air-Conditioning Engineers

Past:

I место на конкурсе научных работ – ИК им. А.В. Шубникова (2018)
II место на конкурсе научных работ студентов и аспирантов – ФУ при правительстве РФ (2017)
II place diploma – Microscopy of Semiconducting Materials XX (Numerical methods section), Oxford, UK (2017)