Student Publications

Below you can find a comprehensive list of published works of CDSE PhD students (last update: October 2021):
Q1 journal publications and A/A*-rank conference proceedings are marked in bold.

2021202020192018201720162015

Ahmadi-Asl, S., Abukhovich, S., Asante-Mensah, M.G., Cichocki, A., Phan, A.H., Tanaka, T., Oseledets, I. (2021). Randomized Algorithms for Computation of Tucker Decomposition and Higher Order SVD (HOSVD). IEEE Access, vol 9, 9350569, pp. 28684-28706.
Burkina, M., Nazarov, I., Panov, M., Fedonin, G., & Shirokikh, B. (2021). Inductive Matrix Completion with Feature Selection. Computational Mathematics and Mathematical Physics, vol. 61(5), 719-732.
Butakov, I. D., Malanchuk, S. V., Neopryatnaya, A. M., Tolmachev, A. D., Andreev, K. V., Kruglik, S. A., … & Frolov, A. A. (2021). High-Dimensional Dataset Entropy Estimation via Lossy Compression. Journal of Communications Technology and Electronics, vol. 66(6), pp. 764-768.
Faizullin, M., Kornilova, A., Akhmetyanov, A., Ferrer, G. (2021). Twist-n-sync: Software clock synchronization with microseconds accuracy using MEMS-gyroscopes. Sensors (Switzerland), volume 21 (1), 68, pp. 1-19.
Fursov, I., Morozov, M., Kaploukhaya, N., Kovtun, E., Rivera-Castro, R., Gusev, G., Zaytsev, A., Burnaev, E. (2021). Adversarial Attacks on Deep Models for Financial Transaction Records. arXiv preprint arXiv:2106.08361.
Gasanov, M, et al. (2021) A New Multi-objective Approach to Optimize Irrigation Using a Crop Simulation Model and Weather History. International Conference on Computational Science. Springer, Cham.
Gusak, J., Daulbaev, T., Ponomarev, E., Cichocki, A., & Oseledets, I. (2021). Reduced-order modeling of deep neural networks. Computational Mathematics and Mathematical Physics, 61(5), pp. 774-785.
Holzbaur, L., Kruglik, S., Frolov, A., Wachter-Zeh, A. (2021). Secrecy and Accessibility in Distributed Storage. IEEE Global Communications Conference, GLOBECOM 2020 – Proceedings, 9322434; Virtual, Taipei; Taiwan.
Holzbaur, L., Kruglik, S., Frolov, A., & Wachter-Zeh, A. (2021). Secure Codes With Accessibility for Distributed Storage. IEEE Transactions on Information Forensics and Security, v.16, pp. 5326-5337.
Illarionova, S., Trekin, A., Ignatiev, V., Oseledets, I. (2021). Neural-Based Hierarchical Approach for Detailed Dominant Forest Species Classification by Multispectral Satellite Imagery. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 14, 2021, 9311828, pp. 1810-1820.
Maksimov I., Rivera-Castro R., Burnaev E. (2021). Addressing Cold Start in Recommender Systems with Hierarchical Graph Neural Networks. Proceedings, 28th IEEE International Conference on Big Data, 937830, pp. 5128 – 513710.
Malovichko, M., Koshev, N., Yavich, N., A. Razorenova and Fedorov, M. (2021). Electroencephalographic Source Reconstruction by the Finite-Element Approximation of the Elliptic Cauchy Problem in IEEE Transactions on Biomedical Engineering, vol. 68 (6), pp. 1811-1819.
Kalinov, A., Bychkov, R., Ivanov, A., Osinsky, A.,Yarotsky, D. (2021). Machine Learning-Assisted PAPR Reduction in Massive MIMO. IEEE Wireless Communications Letters, 10(3), pp. 537-541.
Kardashin A., Uvarov A., Biamonte J. (2021). Quantum Machine Learning Tensor Network States. Frontiers in Physics, vol 8, 586374.
Kheiri, R. (2021). A projective simulation scheme for partially observable multi-agent systems. Quantum Machine Intelligence, vol 3, 11.
Korotin, A.A., V’Yugin, V.V., Burnaev, E.V. (2021). Online algorithm for aggregating experts’ predictions with unbounded quadratic loss. Russian Mathematical Surveys, vol 75 (5), pp. 974-977.
Kozitsin, V., Katser, I., Lakontsev, D. (2021). Online Forecasting and Anomaly Detection Based on the ARIMA Model. Applied Sciences, 11(7), 3194.
Kruglik, S., Frolov, A. (2021). An Information-Theoretic Approach for Reliable Distributed Storage Systems. Journal of Communications Technology and Electronics, vol 65 (12), pp. 1505-1516.
Lebedeva, O., Osinsky, A., Petrov, S. (2021). Low-Rank Approximation Algorithms for Matrix Completion with Random Sampling. Computational Mathematics and Mathematical Physics, 61, pp. 799-815.
Leli, V. M., Osat, S., Tlyachev, T., Dylov, D. V., & Biamonte, J. D. (2021). Deep learning super-diffusion in multiplex networks. Journal of Physics: Complexity, vol. 2(3), 035011.
Matveev, S.A., Oseledets, I.V., Ponomarev, E.S. et al. (2021) Overview of Visualization Methods for Artificial Neural Networks. Computational Mathematics and Mathematical Physics, 61, pp. 887–899.
Mazyavkina, N., Moustafa, S., Trofimov, I., & Burnaev, E. (2021). Optimizing the Neural Architecture of Reinforcement Learning Agents. In Intelligent Computing pp. 591-606. Springer, Cham.
Nesteruk, S., Shadrin, D., Pukalchik, M., Somov, A., Zeidler, C., Zabel, P., Schubert, D. (2021). Image Compression and Plants Classification Using Machine Learning in Controlled-Environment Agriculture: Antarctic Station Use Case. IEEE Sensors Journal, 2021.
Olaleke, O., Oseledets, I., & Frolov, E. (2021). Dynamic Modeling of User Preferences for Stable Recommendations. In Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization, pp. 262-266.
Osinsky, A., Ivanov, A., Lakontsev D., Yarotsky, D. (2021). Lower Performance Bound for Beamspace Channel Estimation in Massive MIMO. IEEE Wireless Communications Letters, 10(2), pp. 311-314.
Osipenko, S., Botashev, K., Nikolaev, E., Kostyukevich, Y. (2021). Transfer learning for small molecule retention predictions. Journal of Chromatography A, vol. 1644, 462119.
Rabchinskii, M. K., … Kislenko V.A., Pavlov S.V., … & Brunkov, P. N. (2021). Hole-matrixed carbonylated graphene: Synthesis, properties, and highly-selective ammonia gas sensing. Carbon, 172, 236-247.
Sarycheva, A., Grigoryev, A., Sidorchuk, D., Vladimirov, G., Khaitovich, P., Efimova, O., Gavrilenko, O., Stekolshchikova, E., Nikolaev, E.N., Kostyukevich, Y. (2021). Structure-preserving and perceptually consistent approach for visualization of mass spectrometry imaging datasets. Analytical Chemistry, vol 93 (3), pp. 1677-1685.
Stasenko, N., Chernova E., Shadrin, D., Ovchinnikov, G., Krivolapov I., & Pukalchik, M. (2021) Deep Learning for improving the storage process: Accurate and automatic segmentation of spoiled areas on apples 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2021, pp. 1-6.
Talitckii, A., Anikina, A., Kovalenko, E., Mayora, O., Osmani, V., Zimniakova, O., Semenov, M., Bril, E., Dylov, D., Somov, A. (2021). Data-driven analysis of parkinson’s disease and its detection at an early stage. ACM International Conference Proceeding Series, 3421953, pp. 419-422, 14th EAI International Conference on Pervasive Computing Technologies for Healthcare, PervasiveHealth 2020; Virtual, Online; United States.
Talitckii, A., Kovalenko, E., Anikina, A., Zimniakova, O., Semenov, M., Bril, E., Shcherbak, A., Dylov, D.V., Somov, A. (2021). Avoiding Misdiagnosis of Parkinson’s Disease with the Use of Wearable Sensors and Artificial Intelligence. IEEE Sensors Journal, volume 21(3), 9208800, pp. 3738-3747.
Wang, J., Bulanov, S.V., Chen, M., Lei, B., Zhang, Y., Zagidullin, R., Zorina, V., Yu, W., Leng, Y., Li, R.a, Zepf, M., Rykovanov, S.G. (2021). Relativistic slingshot: A source for single circularly polarized attosecond x-ray pulses. Physical Review E, vol 102 (6), 061201.
Zamarashkin, N., Osinsky, A. (2021). On the Accuracy of Cross and Column Low-Rank Maxvol Approximations in Average. Computational Mathematics and Mathematical Physics, 61, pp. 786-798.

Alanov, A., Kochurov, M., Volkhonskiy, D., Yashkov, D., Burnaev, E., & Vetrov, D. (2020). User-Controllable Multi-Texture Synthesis with Generative Adversarial Networks. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 4, pp. 214-221.
Anokhin, I., Solovev, P., Korzhenkov, D., Kharlamov, A., Khakhulin, T., Silvestrov, A., Nikolenko, S., Lempitsky, V., & Sterkin, G. (2020). High-Resolution Daytime Translation Without Domain Labels. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 7485–7494.
Amangeldiuly, N., Karlov, D., Fedorov, M.V. (2020). Baseline Model for Predicting Protein–Ligand Unbinding Kinetics through Machine Learning. Journal of Chemical Information and Modeling, 60 (12), pp. 5946–5956.
Bernstein, A., Burnaev, E. Sharaev, M., Kondrateva, E., Kachan, O. (2020). Topological data analysis in computer vision. In Proceedings of SPIE – The International Society for Optical Engineering, 11433, 114332H.
Brilliantov, N. V., Osinsky, A. I., & Krapivsky, P. L. (2020). Role of energy in ballistic agglomeration. Physical Review E, 102(4), 042909.
Bodrova, A., Osinsky, A. & Brilliantov, N. (2020). Temperature distribution in driven granular mixtures does not depend on mechanism of energy dissipation. Scientific Reports, 10(1), 693.
Boyko, A. I., Matrosov, M. P., Oseledets, I. V., Tsetserukou, D., & Ferrer, G. TT-TSDF: Memory-Efficient TSDF with Low-Rank Tensor Train Decomposition. In IEEE/RSJ Conference on Intelligent Robots and Systems 2020.
Burkov, E., Pasechnik, I., Grigorev, A., & Lempitsky, V. (2020). Neural Head Reenactment with Latent Pose Descriptors. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 13783–13792.
Cui, C., Zhang, K., Daulbaev, T., Gusak, J., Oseledets, I., & Zhang, Z. (2020). Active subspace of neural networks: Structural analysis and universal attacks. SIAM Journal on Mathematics of Data Science, 2(4), 1096-1122.
Daulbaev, T., Katrutsa, A., Markeeva, L., Gusak, J., Cichocki, A., & Oseledets, I. (2020). Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs. Advances in Neural Information Processing Systems, 33.
Dementieva, D., & Panchenko, A. (2020). Fake News Detection using Multilingual Evidence. In 2020 IEEE 7th International Conference on Data Science and Advanced Analytics (DSAA) (pp. 775-776). IEEE.
Ishimtsev, V., Bokhovkin, A., Artemov, A., Ignatyev, S., Niessner, M., Zorin, D., & Burnaev, E. (2020). CAD-Deform: Deformable Fitting of CAD Models to 3D Scans. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12358. Springer, Cham.
Egiazarian, V., Voynov, O., Artemov, A., Volkhonskiy, D., Safin, A., Taktasheva, M., … & Burnaev, E. (2020). Deep Vectorization of Technical Drawings. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12358. Springer, Cham.
Egorova, E.E., Fernandez, M., Kabatiansky, G.A., Miao, Y. (2020). Existence and Construction of Complete Traceability Multimedia Fingerprinting Codes Resistant to Averaging Attack and Adversarial Noise. Problems of Information Transmission, vol 56 (4), pp. 388-398.
Faizullin, M., Kornilova, A., Akhmetyanov, A., & Ferrer, G. (2020). Twist-n-Sync: Software Clock Synchronization with Microseconds Accuracy Using MEMS-Gyroscopes. Sensors, 21(1), 68.
Gasanov, M., Petrovskaia, A., Nikitin, A., Matveev, S., Tregubova, P., Pukalchik, M., & Oseledets, I. (2020). Sensitivity Analysis of Soil Parameters in Crop Model Supported with High-Throughput Computing. In V. V. Krzhizhanovskaya, G. Závodszky, M. H. Lees, J. J. Dongarra, P. M. A. Sloot, S. Brissos, & J. Teixeira (Eds.), Computational Science – ICCS 2020(Vol. 12143, pp. 731–741). Springer International Publishing.
Giannakopoulos, I., Serrallés, J. E., Daniel, L., Sodickson, D., Polimeridis, A., White, J. K., & Lattanzi, R. (2020). Magnetic-resonance-based electrical property mapping using Global Maxwell Tomography with an 8-channel head coil at 7 Tesla: a simulation study. IEEE Transactions on Biomedical Engineering.
Gurina, E., Klyuchnikov, N., Zaytsev, A., Romanenkova, E., Antipova, K., Simon, I., Makarov, V., Koroteev, D. (2020). Application of Machine Learning to accidents detection at directional drilling. Journal of Petroleum Science and Engineering, 184, 106519.
Ivanov, A., Osinsky, A., Lakontsev, D., & Yarotsky, D. (2020). High Performance Interference Suppression in Multi-User Massive MIMO Detector. 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 1–5.
Kardashin, A., Uvarov, A., Yudin, D., & Biamonte, J. (2020). Certified variational quantum algorithms for eigenstate preparation. Physical Review A, 102(5), 052610.
Khrulkov, V., Mirvakhabova, L., Ustinova, E., Oseledets, I., & Lempitsky, V. (2020). Hyperbolic Image Embeddings. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 6417–6427.
Kislenko V.A., Pavlov S.V., Kislenko S.A. (2020). Influence of defects in graphene on electron transfer kinetics: The role of the surface electronic structure. Electrochimica Acta, 341, 136011.
Kondrateva E., Belozerova, P., Sharaev, M., … Samotaeva, I. (2020). Machine learning models reproducibility and validation for MR images recognition. In 12th International Conference on Machine Vision (ICMV 2019). Vol. 11433. International Society for Optics and Photonics, 2020.
Korotin, A., V’yugin, V., & Burnaev, E. (2020). Adaptive hedging under delayed feedback. Neurocomputing, 397, 356-368.
Koshelev, I., Somov, A., Lefkimmiatis, S. and Rodríguez-Sánchez, A. (2020). Deconvolution of Image Sequences with a Learning FFT-based Approach. 2020 IEEE 29th International Symposium on Industrial Electronics (ISIE), Delft, Netherlands, pp. 1381-1386.
Koshev, N., Yavich, N., Malovichko, M., Skidchenko, E., & Fedorov (2020). FEM-based Scalp-to-Cortex EEG data mapping via the solution of the Cauchy problem. Journal of Inverse Ill-Posed Problems 28(4), 517–532.
Kozlovskii, I., & Popov, P. (2020). Spatiotemporal identification of druggable binding sites using deep learning. Communications Biology, 3(1), 618.
Leli V., Rubashevskii, A., Sarachakov, A., Rogov, O., Dylov, D. (2020). Near-Infrared-to-Visible Vein Imaging via Convolutional Neural Networks and Reinforcement Learning. 16th International Conference on Control, Automation, Robotics and Vision (ICARCV).
Menshchikov, A., Lopatkin, D., Tsykunov, E., Tsetserukou, D., & Somov, A. (2020). Realizing Body-Machine Interface for Quadrotor Control Through Kalman Filters and Recurrent Neural Network. In 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA) (Vol. 1, pp. 595-602).
Mirvakhabova, L., Frolov, E., Khrulkov, V., Oseledets, I., & Tuzhilin, A. (2020). Performance of Hyperbolic Geometry Models on Top-N Recommendation Tasks. Fourteenth ACM Conference on Recommender Systems, 527–532.
Morozov, A. D., Popkov, D. O., Duplyakov, V. M., Mutalova, R. F., Osiptsov, A. A., Vainshtein, A. L., Burnaev, E. V., Shel, E. V., & Paderin, G. V. (2020). Data-driven model for hydraulic fracturing design optimization: Focus on building digital database and production forecast. Journal of Petroleum Science and Engineering, 194, 107504.
Osinsky, A. (2020). Low-rank method for fast solution of generalized Smoluchowski equations. Journal of Computational Physics, 422, 109764.
Osinsky, A., Bodrova, A. & Brilliantov, N. (2020). Size-polydisperse dust in molecular gas: Energy equipartition versus nonequipartition. Physical Review E, 101(2).
Osinsky, A. & Brilliantov, N. (2020). Temperature distribution in driven granular mixtures does not depend on mechanism of energy dissipation. Scientific Reports, 10(1), 693.
Osinsky, A., Ivanov, A. & Yarotsky, D. (2020). Theoretical Performance Bound of Uplink Channel Estimation Accuracy in Massive MIMO. IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 4925-4929.
Osinsky, A., Ivanov, A., Lakontsev, D., Bychkov, R., & Yarotsky, D. (2020). Data-Aided LS Channel Estimation in Massive MIMO Turbo-Receiver. 2020 IEEE 91st Vehicular Technology Conference (VTC2020-Spring), 1–5.
Osipenko, S., Bashkirova, I., Sosnin, S., Kovaleva, O., Fedorov, M., Nikolaev, E., & Kostyukevich, Y. (2020). Machine learning to predict retention time of small molecules in nano-HPLC. Analytical and Bioanalytical Chemistry.
Palmieri, A.-M., Kovlakov, E., Bianchi, F., Yudin, D., Straupe, S., Biamonte, J. D. & Kulil, S. (2020). Experimental neural network enhanced quantum tomography. npj Quantum Information, 6.
Pavlov, S. V., Kislenko, V. A., & Kislenko, S. A. (2020). Fast Method for Calculating Spatially Resolved Heterogeneous Electron-Transfer Kinetics and Its Application to Graphene with Defects. The Journal of Physical Chemistry C, 124(33), 18147-18155.
Phan, A.-H., Sobolev, K., Sozykin, K., Ermilov, D., Gusak, J., Tichavský, P., Glukhov, V., Oseledets, I., & Cichocki, A.(2020). Stable Low-Rank Tensor Decomposition for Compression of Convolutional Neural Network. In A. Vedaldi, H. Bischof, T. Brox, & J.-M. Frahm (Eds.), Computer Vision – ECCV 2020 (Vol. 12374, pp. 522–539). Springer International Publishing.
Pronina V., Kokkinos F., Dylov D.V., Lefkimmiatis S. (2020). Microscopy Image Restoration with Deep Wiener-Kolmogorov Filters. In: Vedaldi A., Bischof H., Brox T., Frahm JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science, vol 12365. Springer, Cham.
Rabchinskii, M. K., … Pavlov, S.V., Kislenko V.A., … & Brunkov, P. N. (2020). Unveiling a facile approach for large-scale synthesis of N-doped graphene with tuned electrical properties. 2D Materials, 7(4), 045001.
Razorenova A.M., Chernyshev B.V., Nikolaeva A.Y., Butorina A.V., Prokofyev A.O., Tyulenev N.B., Stroganova T.A. (2020) Rapid Cortical Plasticity Induced by Active Associative Learning of Novel Words in Human Adults. In Frontiers in Neuroscience. 14:895.
Razorenova A., Yavich N., Malovichko M., Fedorov M., Koshev N., Dylov D.V. (2020) Deep Learning for Non-invasive Cortical Potential Imaging. In: Kia S.M. et al. (eds) Machine Learning in Clinical Neuroimaging and Radiogenomics in Neuro-oncology. MLCN 2020, RNO-AI 2020. Lecture Notes in Computer Science, vol. 12449. Springer, Cham.
Safin, A. R., Popov, P. A., Kalyabin, D. V., & Nikitov, S. A. (2020). Synthesizer of Discrete Frequency Spectrum Based on an Antiferromagnetic Spintronic Oscillator. Technical Physics Letters, 46(10), 1016–1019.
Schutski, R., Khakhulin, T., Oseledets, I., & Kolmakov, D. (2020). Simple heuristics for efficient parallel tensor contraction and quantum circuit simulation. Physical Review A, 102(6), 062614.
Shadrin, D., Pukalchik, M., Kovaleva, E. & Fedorov, M. (2020). Artificial intelligence models to predict acute phytotoxicity in petroleum contaminated soils. Ecotoxicology and Environmental Safety, 194.
Sosnina, E. A., Sosnin, S., Nikitina, A. A., Nazarov, I., Osolodkin, D. I., & Fedorov, M. V. (2020). Recommender Systems in Antiviral Drug Discovery. ACS Omega, 5(25), 15039–15051.
Uvarov, A., Biamonte, J. D., & Yudin, D. (2020). Variational quantum eigensolver for frustrated quantum systems. Physical Review B, 102(7), 075104.
Volkhonskiy, D., Nazarov, I., & Burnaev, E. (2020, January). Steganographic generative adversarial networks. In Proceedings of SPIE – The International Society for Optical Engineering, 11433, art. no. 114333M.
Zagidullin, R. R., Smirnov, A. P., Matveev, S. A., Shestopalov, Y. V., & Rykovanov, S. G. (2020). Hybrid Parallelism in Finite Volume Based Algorithms in Application to Two-Dimensional Scattering Problem Setting. Computational Mathematics and Modeling, 31(3), 355-363.
Zherebker, A., Lechtenfeld, O. J., Sarycheva, A., Kostyukevich, Y., Kharybin, O., Fedoros, E. I., & Nikolaev, E. N. (2020). Refinement of Compound Aromaticity in Complex Organic Mixtures by Stable Isotope Label Assisted Ultrahigh-Resolution Mass Spectrometry. Analytical Chemistry, 92(13), 9032–9038.
Zolotarev, A. M., Hansen, B. J., Ivanova, E. A., Helfrich, K. M., Li, N., Janssen, P. M. L., Mohler, P. J., Mokadam, N. A., Whitson, B. A., Fedorov, M. V., Hummel, J. D., Dylov, D. V., & Fedorov, V. V. (2020). Optical Mapping-Validated Machine Learning Improves Atrial Fibrillation Driver Detection by Multi-Electrode Mapping. Circulation: Arrhythmia and Electrophysiology, 13(10).

Azimi-Tafreshi, N., Osat, S., & Dorogovtsev, S. N. (2019). Generalization of core percolation on complex networks. Physical Review E, 99 (2).
Buyval, A. Gabdullin, A., Sozykin K. & Klimchik, A. (2019) Model Predictive Path Integral Control for Car Driving with Autogenerated Cost map Based on Prior Map and Camera Image. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC), Auckland, New Zealand, 2019, pp. 2109-2114.
Egiazarian, V., Ignatyev, S., Artemov, A., Voynov, O., Kravchenko, A., Zheng, Y., … & Burnaev, E. (2019). Latent-Space Laplacian Pyramids for Adversarial Representation Learning with 3D Point Clouds. In Proceedings of the 15th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, 4, pp. 421-428.
Egorov, E., Neklydov, K., Kostoev, R., & Burnaev, E. (2019, July). Maxentropy pursuit variational inference. In International Symposium on Neural Networks (pp. 409-417). Springer, Cham.
Egorova, E., Fernandez, M., Kabatiansky, G., & Lee, M. H. (2019). Signature codes for weighted noisy adder channel, multimedia fingerprinting and compressed sensing. Designs, Codes and Cryptography, 87(2-3), 455-462.
Egorova, E., Kabatiansky, G., Krouk, E., & Tavernier, C. (2019, February). A new code-based public-key cryptosystem resistant to quantum computer attacks. In Journal of Physics: Conference Series (Vol. 1163, No. 1, p. 012061). IOP Publishing.
Faqeeh, A., Osat, S., Radicchi, F., & Gleeson, J. P. (2019). Emergence of power laws in noncritical neuronal systems. Physical Review E, 100(1), 010401.
Georgakis, I. P., Polimeridis, A. G., Lattanzi, R. (2019, May). Ideal Current Patterns for Optimal SNR in Realistic Heterogeneous Head Models. In Proceedings of ISMRM 27th Annual Meeting & Exhibition, Montreal, Canada (p. 1036).
Giannakopoulos, I.I., Serralles, J.E.C., Zhang, B., Daniel, L., White, J.K., Lattanzi, R. (2019). Electrical properties mapping in a tissue mimicking phantom using Global Maxwell Tomography with a realistic coil model at 7 Tesla. In the 2nd International workshop of Mr-based Electrical Properties mapping (IMEP).
Giannakopoulos, I.I., Serrallés, J.E., Zhang, B., Daniel, L., White, J.K. and Lattanzi, R. (2019). Global Maxwell Tomography using an 8-channel radiofrequency coil: simulation results for a tissue-mimicking phantom at 7T. In 2019 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting (pp. 823-824). IEEE.
Giannakopoulos I. I., Litsarev M. S. & Polimeridis A. G. (2019). Memory footprint reduction for the FFT-based volume integral equation method via tensor decompositions. In IEEE Transactions on Antennas and Propagation.
Glebov, A., Matveev, N., Andreev, K., Frolov, A., Turlikov, A. (2019). Achievability Bounds for T-Fold Irregular Repetition Slotted ALOHA Scheme in the Gaussian MAC. In IEEE Wireless Communications and Networking Conference, WCNC.
Groth, S. P., Polimeridis, A. G., Tambova, A., & White, J. K. (2019). Circulant preconditioning in the volume integral equation method for silicon photonics. Journal of the Optical Society of America A, 36(6).
Gruenwald, J., Znobishchev, A., Kapeller, C., Kamada, K., Scharinger, J., & Guger, C. (2019). Time-variant linear discriminant analysis improves hand gesture and finger movement decoding for invasive brain-computer interfaces. Frontiers in Neuroscience, 13, 901, 2019.
Gusak, J., Kholiavchenko, M., Ponomarev, E., Markeeva, L., Blagoveschensky, P., Cichocki, A., & Oseledets, I. (2019). Automated Multi-Stage Compression of Neural Networks. In Proceedings of the IEEE International Conference on Computer Vision Workshops, pp. 0-0.
Iskakov, K., Burkov, E., Lempitsky, V., & Malkov, Y. (2019). Learnable triangulation of human pose. In Proceedings of the IEEE International Conference on Computer Vision, pp. 7718-7727.
Itkin, I., Gromova, A., Sitnikov, A., Legchikov, D., Tsymbalov, E., Yavorskiy, R., Novikov, A. and Rudakov, K. (2019, April). User-Assisted Log Analysis for Quality Control of Distributed Fintech Applications. In 2019 IEEE International Conference On Artificial Intelligence Testing (AITest) (pp. 45-51). IEEE.
Ivanov A., Stoliarenko M., Kruglik S., Novichkov S. and Savinov A. (2019). Dynamic Resource Allocation in LEO Satellite. In 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 930-935.
Ivanov A., Stoliarenko M., Savinov A. and Novichkov S. (2019). Physical Layer Representation in LEO Satellite with a Hybrid Multi-Beamforming. In 2019 15th International Wireless Communications & Mobile Computing Conference (IWCMC), pp. 140-145.
Karlov, D. S., Sosnin, S., Tetko, I. V, & Fedorov, M. V. (2019). Chemical space exploration guided by deep neural networks. RSC Advances, 9(9), 5151–5157.
Karlov, D.S., Popov, P., Sosnin, S., Fedorov, M.V. (2019). Message passing neural networks scoring functions for structure-based drug discovery. In: Tetko, I. V., Kůrková, V., Karpov, P., & Theis, F. (Eds.) Artificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions. Lecture Notes in Computer Science.
Katrutsa, A., Daulbaev, T., & Oseledets, I. (2019). Black-box learning of multigrid parameters. Journal of Computational and Applied Mathematics, 112524.
Katser I., Kozitsin V., Maksimov I. (2019). NPP equipment fault detection methods. Izvestiya vuzov. Yadernaya Energetika, 4.
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