Andrey Savchenko
- Leading Research Fellow:HSE Campus in Nizhny Novgorod / Laboratory of Algorithms and Technologies for Networks Analysis (Nizhny Novgorod)
- Professor:HSE Campus in Nizhny Novgorod / Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod) / Department of Information Systems and Technologies
- Programme Academic Supervisor:Artificial Intelligence and Computer Vision
- Andrey Savchenko has been at HSE University since 2008.
Education, Degrees and Academic Titles
- 2016
Doctor of Sciences* in System Analysis, Management and Information Processing
Alexeev Nizhny Novgorod State Technical University
Thesis Title: Audiovisual information classification methods based on the segment homogeneity testing - 2015Associate Professor
- 2010
Candidate of Sciences* (PhD) in Mathematical Modelling, Numerical Methods and Software Complexes
HSE University
Thesis Title: Development of Directed Alternatives Enumeration Method in the Classification Problem Using Theory Information Approach - 2008
Degree
Nizhny Novgorod State Technical University
According to the International Standard Classification of Education (ISCED) 2011, Candidate of Sciences belongs to ISCED level 8 - "doctoral or equivalent", together with PhD, DPhil, D.Lit, D.Sc, LL.D, Doctorate or similar. Candidate of Sciences allows its holders to reach the level of the Associate Professor.
A post-doctoral degree called Doctor of Sciences is given to reflect second advanced research qualifications or higher doctorates in ISCED 2011.
Continuing education / Professional retraining / Internships / Study abroad experience
NVIDIA DLI Certificate – Fundamentals of Deep Learning. Issuing Organization: NVIDIA Deep Learning Institute (February 8, 2022)
Young Faculty Support Programme (Group of Young Academic Professionals)
Category "Future Professoriate" (2014-2015)
Category "New Lecturers" (2012-2013)
Courses (2023/2024)
- Data Management (Bachelor’s programme; Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod); 3 year, 1-4 module)Eng
- Mentor's Seminar (Master’s programme; Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod); 1 year, 1-4 module)Eng
- Past Courses
Courses (2022/2023)
- 2D image processing (Master’s programme; Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod); 1 year, 2 module)Eng
- Data Management (Bachelor’s programme; Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod); 3 year, 1-4 module)Eng
- Mentor's Seminar (Master’s programme; Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod); 1 year, 1-4 module)Eng
- Project seminar "Computer vision for mobile devises" (Master’s programme; Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod); 2 year, 3 module)Eng
- Project seminar "Deep learning for computer vision" (Master’s programme; Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod); 1 year, 4 module)Eng
Courses (2021/2022)
- 2D image processing (Master’s programme; Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod); 1 year, 2 module)Eng
- Data Management (Bachelor’s programme; Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod); 3 year, 1-4 module)Eng
- Research Seminar "Methods of Data Mining" (Master’s programme; Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod); 1 year, 3, 4 module)Rus
Courses (2020/2021)
- Data Management (Bachelor’s programme; Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod); 3 year, 1-4 module)Eng
- Machine Learning (Bachelor’s programme; Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod); 3 year, 2 module)Rus
- Research Seminar "Methods of Data Mining" (Master’s programme; Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod); 1 year, 3, 4 module)Rus
Courses (2019/2020)
- Data Management (Bachelor’s programme; Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod); 3 year, 1-4 module)Rus
- Research Seminar "Methods of Data Mining" (Master’s programme; Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod); 1 year, 3, 4 module)Rus
Editorial board membership
2019: Member of the Editorial Board, International Journal of Applied Mathematics and Computer Science.
Grants
- Efficient audiovisual analysis of dynamical changes in emotional state based on information-theoretic approach (2020-2023), Russian Science Foundation project 20-71-10010 (Leader)
- Methods and computationally efficient algorithms for computer vision and multimodal data analysis (2022), Project of HSE University (Leader)
- Neural network algorithms for video-based analysis of students' emotional state and engagement (2021-2022), Project of HSE AI Center (Leader)
-AutoML in image recognition on mobile devices (2021). Project of Huawei in HSE-NN (Leader)
- Project of Huawei in HSE-NN related to computer vision (2020), topic under NDA (Leader)
- Continuation of the project of Huawei in HSE-NN related to computer vision (2021), topic under NDA (Leader)
- Continuation of the project of Huawei in HSE-NN related to computer vision (2022), topic under NDA (Leader)
- Deep Learning for User Modeling and Preference Prediction in Textual and Visual Data (2020). Project of Samsung-PDMI Joint AI Center (Senior researcher)
- Visual Preferences Prediction in Visual Data on Mobile Devices (2018-2019). Project of Samsung-PDMI Joint AI Center (Leader)
- Efficient image recognition medium-sized databases (2017-2018). Russian Federation President grant for teams lead by young doctor of science (Leader)
- Clustering and Search Techniques in Large Scale Networks (2014-2015, 2017-2018), Russian Science Foundation project 14-01-00039. Joint project with researchers from USA, Italy and France (Senior researcher)
- Efficient multimedia recognition for users preferences prediction on mobile phones (2019-2020). Group “Analysis of multimedia data of the users of mobile devices” (Leader): http://nnov.hse.ru/bipm/amdmobile
- Development of efficient classification methods for large multimedia databases (2017-2018). Group “Analysis of multimedia data” (Leader), http://nnov.hse.ru/bipm/amd
- Efficient speech recognition for voice control systems (2012-2013). Grant of Russian Government. Joint project with IstraSoft company
- Сomputer-aided language learning systems (2011-2013). Grant of Foundation For Assistance To Small Innovative Enterprises. Joint project with IstraSoft company (Moscow)
Patents
- U.S. Patent No. 11,222,233. Savchenko A. V. "Method and apparatus for multi-category image recognition." Current Assignee: Samsung Electronics Co Ltd. 11 Jan. 2022.
- U.S. Patent No. 11,222,196. Savchenko A. V. "Simultaneous recognition of facial attributes and identity in organizing photo albums." Current Assignee: Samsung Electronics Co Ltd. 11 Jan. 2022.
20241
20235
- Article Churaev E., Savchenko A. A standalone software for real-time facial analysis in online conferences and e-lessons // Software Impacts. 2023. Vol. 16. No. 100507 doi
- Chapter Sokolova A., Savchenko A. Effective face recognition based on anomaly image detection and sequential analysis of neural descriptors, in: 2023 IX International Conference on Information Technology and Nanotechnology (ITNT). IEEE, 2023. P. 1-5. doi
- Chapter Savchenko A. Facial Expression Recognition with Adaptive Frame Rate based on Multiple Testing Correction, in: Proceedings of the 40th International Conference on Machine Learning: Volume 202: International Conference on Machine Learning, 23-29 July 2023, Honolulu, Hawaii, USA Vol. 202: International Conference on Machine Learning, 23-29 July 2023, Honolulu, Hawaii, USA. PMLR, 2023. P. 30119-30129. doi
- Article Savchenko A., Savchenko L., Makarov I. Fast Search of Face Recognition Model for a Mobile Device Based on Neural Architecture Comparator // IEEE Access. 2023. Vol. 11. P. 65977-65990. doi
- Article A. V. Savchenko, L. V. Savchenko. Three-way classification for sequences of observations // Information Sciences. 2023. Vol. 648. Article 119540. doi
202210
- Book Analysis of Images, Social Networks and Texts. 10th International Conference, AIST 2021, Tbilisi, Georgia, December 16–18, 2021, Revised Selected Papers / Ed. by E. Burnaev, D. I. Ignatov, S. Ivanov, M. Khachay, O. Koltsova, A. Kutuzov, Sergei O. Kuznetsov, N. Loukachevitch, A. Napoli, A. Panchenko, P. M. Pardalos, J. Saramäki, A. Savchenko, E. Tsymbalov, E. Tutubalina. Cham : Springer, 2022. doi
- Article Savchenko A., Savchenko L. Audio-Visual Continuous Recognition of Emotional State in a Multi-User System Based on Personalized Representation of Facial Expressions and Voice // Pattern Recognition and Image Analysis. 2022. Vol. 32. No. 3. P. 665-671. doi
- Article Savchenko A., Savchenko L., Makarov I. Classifying emotions and engagement in online learning based on a single facial expression recognition neural network // IEEE Transactions on Affective Computing. 2022. Vol. 13. No. 4. P. 2132-2143. doi
- Article Savchenko A., Savchenko V. V., Savchenko L. Gain-optimized spectral distortions for pronunciation training // Optimization Letters. 2022. Vol. 16. No. 7. P. 2095-2113. doi
- Chapter Andrey V. Savchenko, Lyudmila V. Savchenko, Belova N. S. Group-Level Affect Recognition in Video Using Deviation of Frame Features, in: Analysis of Images, Social Networks and Texts. 10th International Conference, AIST 2021, Tbilisi, Georgia, December 16–18, 2021, Revised Selected Papers / Ed. by E. Burnaev, D. I. Ignatov, S. Ivanov, M. Khachay, O. Koltsova, A. Kutuzov, Sergei O. Kuznetsov, N. Loukachevitch, A. Napoli, A. Panchenko, P. M. Pardalos, J. Saramäki, A. Savchenko, E. Tsymbalov, E. Tutubalina. Cham : Springer, 2022. doi Ch. 13217. P. 199-207. doi
- Chapter Sokolova A., Savchenko A. Open-Set Face Identification with Sequential Analysis and Out-of-Distribution Data Detection, in: 2022 International Joint Conference on Neural Networks (IJCNN). Institute of Electrical and Electronics Engineers Inc., 2022. doi
- Article Savchenko A., Demochkin K., Grechikhin I. Preference prediction based on a photo gallery analysis with scene recognition and object detection // Pattern Recognition. 2022. Vol. 121. Article 108248. doi
- Article Savchenko A., Belova N. S. Sequential analysis in Fourier probabilistic neural networks // Expert Systems with Applications. 2022. Vol. 207. Article 117885. doi
- Article Makarov I., Savchenko A., Arseny Korovko, Leonid Sherstyuk, Severin N., Kiselev D., Mikheev Aleksandr, Babaev D. Temporal network embedding framework with causal anonymous walks representations // PeerJ Computer Science. 2022. Vol. 8. Article e858. doi
- Chapter Savchenko A. Video-based Frame-level Facial Analysis of Affective Behavior on Mobile Devices using EfficientNets, in: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW). IEEE, 2022. P. 2358-2365. doi
202111
- Article Savchenko L., Savchenko A. A Method of Real-Time Dynamic Measurement of a Speaker’s Emotional State from a Speech Waveform / Пер. с рус. // Measurement Techniques. 2021. Vol. 64. No. 4. P. 319-327. doi
- Book Analysis of Images, Social Networks and Texts: 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020, Revised Selected Papers / Ed. by W. M. van der Aalst, V. Batagelj, D. I. Ignatov, M. Khachay, O. Koltsova, A. Kutuzov, Sergei O. Kuznetsov, I. A. Lomazova, N. Loukachevitch, A. Napoli, A. Panchenko, P. M. Pardalos, M. Pelillo, A. Savchenko, E. Tutubalina. Vol. 12602. Cham: Springer, 2021. doi
- Article Kharchevnikova A., Savchenko A. Efficient video face recognition based on frame selection and quality assessment // PeerJ Computer Science. 2021. Vol. 7:e391. P. 1-18. doi
- Article Savchenko A. Fast inference in convolutional neural networks based on sequential three-way decisions // Information Sciences. 2021. Vol. 560. P. 370-385. doi
- Chapter Sokolov A., Savchenko A. Gender domain adaptation for automatic speech recognition, in: 2021 IEEE 19th World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2021. doi P. 413-418. doi
- Article Savchenko V. V., Savchenko A. Method for Measuring Distortions in Speech Signals during Transmission over a Communication Channel to a Biometric Identification System / Пер. с рус. // Measurement Techniques. 2021. Vol. 63. No. 11. P. 917-925. doi
- Chapter Demochkina P., Savchenko A. MobileEmotiFace: Efficient Facial Image Representations in Video-Based Emotion Recognition on Mobile Devices, in: Pattern Recognition. ICPR International Workshops and Challenges. Virtual Event, January 10–15, 2021, Proceedings, Part V. Cham: Springer, 2021. doi P. 266-274. doi
- Chapter Demochkina P., Savchenko A. Neural network model for video-based facial expression recognition in-the-wild on mobile devices, in: 2021 International Conference on Information Technology and Nanotechnology (ITNT). IEEE, 2021. doi P. 1-5. doi
- Book Recent Trends in Analysis of Images, Social Networks and Texts. 9th International Conference, AIST 2020, Skolkovo, Moscow, Russia, October 15–16, 2020 Revised Supplementary Proceedings / Ed. by W. M. van der Aalst, V. Batagelj, A. V. Buzmakov, D. I. Ignatov, A. A. Kalenkova, M. Khachay, O. Koltsova, A. Kutuzov, S. Kuznetsov, I. A. Lomazova, N. Loukachevitch, I. Makarov, A. Napoli, A. Panchenko, P. M. Pardalos, M. Pelillo, A. Savchenko, E. Tutubalina. Vol. 12602. Springer, 2021. doi
- Chapter Savchenko L., Savchenko A. Speaker-Aware Training of Speech Emotion Classifier with Speaker Recognition, in: Speech and Computer. 23rd International Conference, SPECOM 2021, St. Petersburg, Russia, September 27–30, 2021 Vol. 12997. St. Petersburg : Springer, 2021. Ch. 55. P. 614-625. doi
- Chapter Churaev E., Savchenko A. Touching the Limits of a Dataset in Video-Based Facial Expression Recognition, in: 2021 International Russian Automation Conference (RusAutoCon). IEEE, 2021. P. 633-638. doi
202012
- Article Savchenko V. V., Savchenko A. A Method for the Real-Time Updating of Voice Samples in the Unified Biometric System / Пер. с рус. // Measurement Techniques. 2020. Vol. 63. No. 5. P. 391-400. doi
- Chapter Kuznetsov A., Savchenko A. A New Sport Teams Logo Dataset for Detection Tasks, in: Proceedings of the International Conference on Computer Vision and Graphics (ICCVG 2020) Vol. 12334. Cham : Springer, 2020. doi Ch. 8. P. 87-97. doi
- Article Sokolova A., Savchenko A. Computation-Efficient Face Recognition Algorithm Using a Sequential Analysis of High Dimensional Neural-Net Features // Optical Memory and Neural Networks (Information Optics). 2020. Vol. 29. No. 1. P. 19-29. doi
- Chapter Miasnikov E. V., Savchenko A. Detection and Recognition of Food in Photo Galleries for Analysis of User Preferences, in: Proceedings of International Conference on Image Analysis and Recognition (ICIAR 2020) Vol. 12131. Cham : Springer, 2020. doi Ch. 9. P. 83-94. doi
- Chapter Savchenko A., Miasnikov E. V. Event Recognition Based on Classification of Generated Image Captions, in: Advances in Intelligent Data Analysis XVIII (IDA 2020) Vol. 12080. Cham : Springer, 2020. doi Ch. 33. P. 418-430. doi
- Chapter Savchenko A. Event Recognition with Automatic Album Detection based on Sequential Grouping of Confidence Scores and Neural Attention, in: Proceedings of International Joint Conference on Neural Networks 2020 (IJCNN 2020). Piscataway : IEEE, 2020. doi P. 1-8. doi
- Chapter Demochkina P., Savchenko A. Improving the Accuracy of One-Shot Detectors for Small Objects in X-ray Images, in: Proceedings of IEEE International Russian Automation Conference (RusAutoCon 2020). IEEE, 2020. Ch. 110. P. 610-614. doi
- Article Savchenko V. V., Savchenko A. Method for Measuring the Indicator of Acoustic Quality of Audio Recordings Prepared for Registration and Processing in the Unified Biometric System / Пер. с рус. // Measurement Techniques. 2020. Vol. 62. No. 12. P. 1071-1078. doi
- Article Savchenko A., Дёмочкин К. В., Savchenko L. Neural Attention Mechanism and Linear Squeezing of Descriptors in Image Classification for Visual Recommender Systems // Optical Memory and Neural Networks (Information Optics). 2020. Vol. 29. No. 4. P. 297-304. doi
- Chapter Savchenko A., Savchenko L., Savchenko V. Optimization of Gain in Symmetrized Itakura-Saito Discrimination for Pronunciation Learning, in: Mathematical Optimization Theory and Operations Research, 19th International Conference, MOTOR 2020, Novosibirsk, Russia, July 6–10, 2020, (Т. 12095) / Ed. by A. Kononov, M. Khachay, P. Pardalos, V. A. Kalyagin. Cham : Springer, 2020. doi Ch. 30. P. 440-454. doi
- Article Savchenko A. Probabilistic Neural Network With Complex Exponential Activation Functions in Image Recognition // IEEE Transactions on Neural Networks and Learning Systems. 2020. Vol. 31. No. 2. P. 651-660. doi
- Chapter Savchenko A. Sequential Analysis with Specified Confidence Level and Adaptive Convolutional Neural Networks in Image Recognition, in: Proceedings of International Joint Conference on Neural Networks 2020 (IJCNN 2020). Piscataway : IEEE, 2020. doi P. 1-8. doi
201914
- Article Savchenko A., Savchenko V. V. A Method for Measuring the Pitch Frequency of Speech Signals for the Systems of Acoustic Speech Analysis / Пер. с рус. // Measurement Techniques. 2019. Vol. 62. No. 3. P. 282-288. doi
- Book Analysis of Images, Social Networks and Texts. 8th International Conference, AIST 2019, Lecture Notes in Computer Science, Revised Selected Papers / Ed. by W. M. van der Aalst, V. Batagelj, D. I. Ignatov, M. Y. Khachay, V. Kuskova, A. Kutuzov, S. Kuznetsov, I. A. Lomazova, N. Loukachevitch, A. Napoli, P. M. Pardalos, M. Pelillo, A. Savchenko, E. Tutubalina. Vol. 11832. Cham : Springer, 2019. doi
- Chapter Kopeykina Lyudmila, Savchenko A. Automatic Privacy Detection in Scanned Document Images Based on Deep Neural Networks, in: 2019 International Russian Automation Conference (RusAutoCon). IEEE, 2019. P. 1-6. doi
- Article Grachev A., Ignatov D. I., Savchenko A. Compression of recurrent neural networks for efficient language modeling // Applied Soft Computing Journal. 2019. Vol. 79. P. 354-362. doi
- Article Savchenko A., Savchenko V. V. Criterion of Significance Level for Selection of Order of Spectral Estimation of Entropy Maximum / Пер. с рус. // Radioelectronics and Communications Systems. 2019. Vol. 62. No. 5. P. 223-231. doi
- Article Savchenko A. Efficient facial representations for age, gender and identity recognition in organizing photo albums using multi-output ConvNet // PeerJ Computer Science. 2019. Vol. 5:e197. P. 1-26. doi
- Chapter Sokolova A., Savchenko A. Fast Nearest-Neighbor Classifier based on Sequential Analysis of Principal Components, in: Analysis of Images, Social Networks and Texts. 8th International Conference, AIST 2019, Lecture Notes in Computer Science, Revised Selected Papers / Ed. by W. M. van der Aalst, V. Batagelj, D. I. Ignatov, M. Y. Khachay, V. Kuskova, A. Kutuzov, S. Kuznetsov, I. A. Lomazova, N. Loukachevitch, A. Napoli, P. M. Pardalos, M. Pelillo, A. Savchenko, E. Tutubalina. Vol. 11832. Cham : Springer, 2019. doi Ch. 7. P. 73-80. doi
- Article Savchenko L.V., Savchenko A.V. Fuzzy Phonetic Encoding of Speech Signals in Voice Processing Systems / Пер. с рус. // Journal of Communications Technology and Electronics. 2019. Vol. 64. No. 3. P. 238-244. doi
- Chapter Demochkin K., Savchenko A. Multi-label Image Set Recognition in Visually-Aware Recommender Systems, in: Analysis of Images, Social Networks and Texts. 8th International Conference, AIST 2019, Lecture Notes in Computer Science, Revised Selected Papers / Ed. by W. M. van der Aalst, V. Batagelj, D. I. Ignatov, M. Y. Khachay, V. Kuskova, A. Kutuzov, S. Kuznetsov, I. A. Lomazova, N. Loukachevitch, A. Napoli, P. M. Pardalos, M. Pelillo, A. Savchenko, E. Tutubalina. Vol. 11832. Cham : Springer, 2019. doi Ch. 26. P. 291-297. doi
- Chapter Savchenko A., Rassadin A. Scene Recognition in User Preference Prediction Based on Classification of Deep Embeddings and Object Detection, in: Advances in Neural Networks – ISNN 2019 16th International Symposium on Neural Networks, ISNN 2019, Moscow, Russia, July 10–12, 2019, Proceedings, Part II. Cham : Springer, 2019. doi Ch. 41. P. 422-430. doi
- Article Savchenko A. Sequential three-way decisions in multi-category image recognition with deep features based on distance factor // Information Sciences. 2019. Vol. 489. P. 18-36. doi
- Chapter Grechikhin I., Andrey V. Savchenko. User Modeling on Mobile Device Based on Facial Clustering and Object Detection in Photos and Videos, in: Pattern Recognition and Image Analysis Part 2. Springer, 2019. doi P. 429-440. doi
- Article Demochkin K. V., Savchenko A. Visual product recommendation using neural aggregation network and context gating // Journal of Physics: Conference Series. 2019. Vol. 1368. No. 032016. P. 1-7. doi
- Chapter Sokolov A., Savchenko A. Voice command recognition in intelligent systems using deep neural networks, in: 17th World Symposium on Applied Machine Intelligence and Informatics (SAMI). IEEE, 2019. Ch. 19. P. 113-116. doi
201812
- Chapter Savchenko A., Sokolova Anastasiia D. Cluster Analysis of Facial Video Data in Video Surveillance Systems Using Deep Learning, in: Computational Aspects and Applications in Large-Scale Networks. Springer Proceedings in Mathematics & Statistics Vol. 247. Springer, 2018. doi P. 113-120. doi
- Chapter A.D. Sokolova, A.V. Savchenko. Data organization in video surveillance systems using deep learning, in: CEUR Workshop Proceedings Vol. 2210: Proceedings of the International Conference Information Technology and Nanotechnology. Session Image Processing and Earth Remote Sensing . , 2018. P. 243-250.
- Chapter Savchenko A. Efficient Statistical Face Recognition Using Trigonometric Series and CNN Features, in: Proceedings of the 24th International Conference on Pattern Recognition (ICPR). IEEE, 2018. P. 3262-3267. doi
- Chapter Tarasov Alexander V., Savchenko A. Emotion Recognition of a Group of People in Video Analytics Using Deep Off-the-Shelf Image Embeddings, in: Proceedings of Analysis of Images, Social Networks and Texts – 7th International Conference, AIST 2018, Moscow, Russia, July 5-7, 2018, Revised Selected Papers. Lecture Notes in Computer Science / Ed. by W. M. van der Aalst, V. Batagelj, G. Glavaš,, D. I. Ignatov, M. Khachay, O. Koltsova, S. Kuznetsov, I. A. Lomazova, N. Loukachevitch,, A. Napoli,, A. Savchenko, A. Panchenko,, P. M. Pardalos, M. Pelillo,. Vol. 11179. Berlin : Springer, 2018. doi Ch. 19. P. 191-198. doi
- Chapter Andreeva E., Ignatov D. I., Grachev A., Savchenko A. Extraction of Visual Features for Recommendation of Products via Deep Learning, in: Proceedings of Analysis of Images, Social Networks and Texts – 7th International Conference, AIST 2018, Moscow, Russia, July 5-7, 2018, Revised Selected Papers. Lecture Notes in Computer Science / Ed. by W. M. van der Aalst, V. Batagelj, G. Glavaš,, D. I. Ignatov, M. Khachay, O. Koltsova, S. Kuznetsov, I. A. Lomazova, N. Loukachevitch,, A. Napoli,, A. Savchenko, A. Panchenko,, P. M. Pardalos, M. Pelillo,. Vol. 11179. Berlin : Springer, 2018. doi P. 201-210. doi
- Article Savchenko A., Belova N. S., Savchenko Lyudmila V. Fuzzy Analysis and Deep Convolution Neural Networks in Still-to-video Recognition // Optical Memory and Neural Networks (Information Optics). 2018. Vol. 27. No. 1. P. 23-31. doi
- Chapter Savchenko A. Granular Computing and Sequential Analysis of Deep Embeddings in Fast Still-to-Video Face Recognition, in: Proceedings of the IEEE 12th International Symposium on Applied Computational Intelligence and Informatics (SACI 2018). IEEE, 2018. P. 515-520. doi
- Article A.S. Kharchevnikova, Savchenko A. Neural networks in video-based age and gender recognition on mobile platforms // Optical Memory and Neural Networks (Information Optics). 2018. Vol. 27. No. 4. P. 246-259. doi
- Chapter Anastasiia D. Sokolova, Angelina S. Kharchevnikova, Savchenko A. Organizing Multimedia Data in Video Surveillance Systems Based on Face Verification with Convolutional Neural Networks, in: Analysis of Images, Social Networks and Texts. 6th International Conference, 2017, Revised Selected Papers / Ed. by W. M. van der Aalst, D. I. Ignatov, M. Khachay, S. Kuznetsov, V. Lempitsky, I. A. Lomazova, A. Napoli, A. Panchenko, P. M. Pardalos, A. V. Savchenko, S. Wasserman. Vol. 10716. Cham : Springer, 2018. doi P. 223-230. doi
- Book Proceedings of Analysis of Images, Social Networks and Texts – 7th International Conference, AIST 2018, Moscow, Russia, July 5-7, 2018, Revised Selected Papers. Lecture Notes in Computer Science / Ed. by W. M. van der Aalst, V. Batagelj, G. Glavaš,, D. I. Ignatov, M. Khachay, O. Koltsova, S. Kuznetsov, I. A. Lomazova, N. Loukachevitch,, A. Napoli,, A. Savchenko, A. Panchenko,, P. M. Pardalos, M. Pelillo,. Vol. 11179. Berlin : Springer, 2018. doi
- Chapter Savchenko A., Kharchevnikova Angelina S. The Video-Based Age and Gender Recognition with Convolution Neural Networks, in: Computational Aspects and Applications in Large-Scale Networks. Springer Proceedings in Mathematics & Statistics Vol. 247. Springer, 2018. doi P. 37-46. doi
- Article Savchenko A., Belova N. S. Unconstrained face identification using maximum likelihood of distances between deep off-the-shelf features // Expert Systems with Applications. 2018. Vol. 108. P. 170-182. doi
20177
- Chapter Milov V. R., Savchenko A. Classification of Dangerous Situations for Small Sample Size Problem in Maintenance Decision Support Systems, in: 5th Conference on Analysis of Images, Social Networks, and Text (AIST 2016). Springer, 2017. P. 338-345. doi
- Article Savchenko A. Clustering and maximum likelihood search for efficient statistical classification with medium-sized databases // Optimization Letters. 2017. Vol. 11. No. 2. P. 329-341. doi
- Article Savchenko A. Deep neural networks and maximum likelihood search for approximate nearest neighbor in video-based image recognition // Optical Memory and Neural Networks (Information Optics). 2017. Vol. 26. No. 2. P. 129-136. doi
- Chapter Alexandr Rassadin, Alexey Gruzdev, Andrey Savchenko. Group-Level Emotion Recognition using Transfer Learning from Face Identification, in: Proceedings of the 19th ACM International Conference on Multimodal Interaction. , 2017. P. 544-548. doi
- Article Savchenko A. Maximum-likelihood approximate nearest neighbor method in real-time image recognition // Pattern Recognition. 2017. Vol. 61. P. 459-469. doi
- Chapter Grachev A., Ignatov D. I., Savchenko A. Neural Networks Compression for Language Modeling, in: Pattern Recognition and Machine Intelligence. 7th International Conference, PReMI 2017, Kolkata, India, December 5-8, 2017, Proceedings. Lecture Notes in Computer Science book series (LNCS, volume 10597). Springer, 2017. doi P. 351-357. doi
- Chapter Savchenko A. Sequential Three-Way Decisions in Efficient Classification of Piecewise Stationary Speech Signals, in: International Joint Conference on Rough Sets, Springer, Cham.. Springer, 2017. P. 264-277. doi
20164
- Article Savchenko A. Fast multi-class recognition of piecewise regular objects based on sequential three-way decisions and granular computing // Knowledge-Based Systems. 2016. Vol. 91. P. 252-262. doi
- Article Savchenko A., Savchenko V.V. Information Theoretic Analysis of Efficiency of the Phonetic Encoding–Decoding Method in Automatic Speech Recognition / Пер. с рус. // Journal of Communications Technology and Electronics. 2016. Vol. 61. No. 4. P. 430-435.
- Article Savchenko A., Milov V. R. The Adaptive Approach to Abnormal Situations Recognition Using Images from Condition Monitoring Systems // Optical Memory and Neural Networks (Information Optics). 2016. Vol. 25. No. 2. P. 79-87. doi
- Article Savchenko A. The Maximal Likelihood Enumeration Method for the Problem of Classifying Piecewise Regular Objects / Пер. с рус. // Automation and Remote Control. 2016. Vol. 77. No. 3. P. 443-450. doi
20152
- Article Savchenko A., Belova N. S. Statistical testing of segment homogeneity in classification of piecewise-regular objects // International Journal of Applied Mathematics and Computer Science. 2015. Vol. 25. No. 4. P. 915-925. doi
- Article Savchenko A., Savchenko Lyudmila V. Towards the creation of reliable voice control system based on a fuzzy approach // Pattern Recognition Letters. 2015. Vol. 65. P. 145-151. doi
20144
- Article Savchenko A., Khokhlova Y. I. About neural-network algorithms application in viseme classification problem with face video in audiovisual speech recognition systems // Optical Memory and Neural Networks (Information Optics). 2014. Vol. 23. No. 1. P. 34-42. doi
- Article Savchenko A., Chernousov V.O. Noise Resistant Morphological Algorithm of Moving Forklift Truck Detection on Noisy Image Data // International Journal of Conceptual Structures and Smart Applications (IJCSSA). 2014. Vol. 2. No. 2. P. 36-54.
- Article Savchenko A. Phonetic encoding method in the isolated words recognition problem / Пер. с рус. // Journal of Communications Technology and Electronics. 2014. Vol. 59. No. 4. P. 339-345. doi
- Article Savchenko A. Semi-automated Speaker Adaptation: How to Control the Quality of Adaptation? // Lecture Notes in Computer Science. 2014. Vol. 8509. P. 638-646.
20134
- Article Savchenko A. Pattern recognition and increasing of the computational efficiency of a parallel realization of the probabilistic neural network with homogeneity testing // Optical Memory and Neural Networks (Information Optics). 2013. Vol. 22. No. 3. P. 184-192. doi
- Article Savchenko A. Phonetic Words Decoding Software in the Problem of Russian Speech Recognition / Пер. с рус. // Automation and Remote Control. 2013. Vol. 74. No. 7. P. 1225-1232. doi
- Article Savchenko A. Probabilistic neural network with homogeneity testing in recognition of discrete patterns set // Neural Networks. 2013. Vol. 46. P. 227-241. doi
- Article Savchenko A. Real-Time Image Recognition with the Parallel Directed Enumeration Method // Lecture Notes in Computer Science. 2013. Vol. 7963. P. 123-132.
20124
- Article Savchenko A. Adaptive Video Image Recognition System Using a Committee Machine // Optical Memory and Neural Networks (Information Optics). 2012. Vol. 21. No. 4. P. 219-226. doi
- Article Savchenko A. Directed enumeration method in image recognition // Pattern Recognition. 2012. Vol. 45. No. 8. P. 2952-2961. doi
- Article Savchenko A. Face recognition in real-time applications: A comparison of directed enumeration method and K-d trees // Lecture Notes in Business Information Processing. 2012. Vol. 128 LNBIP. P. 187-199. doi
- Chapter Savchenko A. Statistical Recognition of a Set of Patterns Using Novel Probability Neural Network, in: Artificial Neural Networks in Pattern Recognition 5th INNS IAPR TC 3 GIRPR Workshop, ANNPR 2012, Trento, Italy, September 2012 Proceeding / Сост.: N. Mana, F. Schwenker, E. Trentin. Berlin, Heidelberg : Springer, 2012. P. 93-103.
20111
20101
20091
Employment history
- 2005-2012. Tecomgroup (software engineer/project leader)
- since 2008. HSE University in Nizhny Novgorod (lecturer)
- 2014-2018. Nizhny Novgorod State Technical University (lecturer)
- 2018-2020. St. Petersburg Department of Steklov Institute of Mathematics (senior researcher in Samsung-PDMI Joint AI Center)
- Co-chair of the track "Analysis of images and videos" in AIST (International Conference on Analysis of Images, Social Networks and Texts")
‘In the Future, I Expect Rapid Development of Professions Related to Prompt Engineering’
The English-language programme of HSE Online ‘Master of Computer Vision’ will change its name to ‘Artificial Intelligence and Computer Vision’ in 2024. Andrey Savchenko, the programme academic supervisor, shares how the new name will affect the programme semantics, why AI has become the main federal trend in the field of information technology, and what tasks graduates will solve.
Evolution of Face ID Industry: Insights from Online Programme Master of Computer Vision
The online Master’s programme ‘Master of Computer Vision’ recently held a webinar ‘Face recognition. How does it work and how does it break?‘ as part of its admission campaign, with the participation of the programme’s business partner, Huawei. Participants learned about the basic principles of Face ID algorithms and the features of face recognition technologies relative to other deep learning models, as well as some of the tricks that can mislead artificial intelligence systems.
Students from HSE University in Nizhny Novgorod Create Web Service for Recognising Emotions
A team of students from HSE University in Nizhny Novgorod and Minin University have created a mobile application and a web service for recognising emotions in photographs. This solution can be useful in marketing, education, personnel management—any areas where the quality of interpersonal communication matters.
Master’s in Computer Vision Students Defend Term Papers for First Time
Second-year students of the Master of Computer Vision programme have presented their projects in this cutting-edge field in AI. The committee included representatives of the programme’s partners from Huawei, YADRO and SBERLAB, as well as Valery Cherepennikov, IT advisor to the governor of the Nizhny Novgorod region.
Russian Scientists Teach AI to Analyse Emotions of Participants at Online Events
HSE researchers have proposed a new neural network method for recognising emotions and people's engagement. The algorithms are based on the analysis of video images of faces and significantly outperform existing single models. The developed models are suitable for low-performance equipment, including mobile devices. The results can be implemented into video conferencing tools and online learning systems to analyse the engagement and emotions of participants. The results of the study were published in IEEE Transactions on Affective Computing.
‘The Potential of Computer Vision Technologies is Hard to Overestimate’
Admission to Russia's first Master of Computer Vision online programme at HSE University has been extended until 20th September. The programme will be delivered entirely in English and has been developed by HSE researchers and leading experts from Huawei, Itseez3D, Intel, Harman, and Xperience.ai, who are all involved in cutting-edge research in the field of computer vision.
Applied Projects and Deep Fakes: How Computer Vision Is Taught at HSE University
Applications for the HSE University Master of Computer Vision, the only English-language online computer vision programme in Russia, are open until August 10. The programme has been developed by researchers of the Faculty of Informatics, Mathematics, and Computer Science at HSE University in Nizhny Novgorod together with researchers in the field of computer vision from leading companies in the industry: Huawei, Itseez3D, Intel, Harman, Xperience.ai, Sber, Newstream and Deelvin Solutions. Andrey Savchenko, Academic Supervisor of the programme and Professor at the Department of Information Systems and Technologies, told the HSE News Service about how teaching competencies in the field of computer vision changes our view of the world.
HSE University Researchers Propose Algorithm to Determine Preferences of Smartphone Users
Mathematicians from HSE University in Nizhny Novgorod have developed a new way to predict the preferences of mobile device users. The method, which is 2–12% more accurate than known analogues, is based on simultaneous recognition of objects, faces and scenes in a smartphone’s photo gallery and on a remote server. The algorithm can be used to personalise services and offer recommendations tailored to a particular person. The article was published in the Pattern Recognition journal.
‘HSE Academic Environment Helped Me to Make a Soft Transition to the New Era of Computer Vision’
This academic year, HSE University launched the first online master's programme ‘Master of Computer Vision’ supervised by Professor Andrey Savchenko. Alexander Rassadin, graduate of the Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod) and active participant of many CV projects, is delivering the course ‘Deep Learning for Computer Vision’ as part of the curriculum for this new programme. Alexander told us how he once wrote an algorithm for robot movement, the moment he realized what his dream job was and why analyzing sports games is more interesting than predicting a tsunami.
Master of Computer Vision: New Online Programme on Coursera
In September 2021, HSE University’s Faculty of Informatics, Mathematics and Computer Science at HSE Nizhny Novgorod launched the new applied degree programme Master of Computer Vision. Developed in collaboration with leading experts in the field of computer vision - Huawei, Itseez3D, Intel, Harman, and Xperience.ai – the programme is available on the Coursera platform and open to applicants from all over the world. Andrey Savchenko, Academic Supervisor of the new programme, talked to The HSE Look about how it came to be and what are its advantages.
HSE University-Nizhny Novgorod to Launch Online Master of Computer Vision Programme
This year, HSE University in Nizhny Novgorod will launch its Master of Computer Vision programme on Coursera. The fully online programme has been developed by leading experts from Huawei, ItSeez3D, Intel, Harman, and Xperience AI. It will be taught in English and is open to applicants from all countries. The closing date for applications is August 16, 2021.
HSE University-Nizhny Novgorod to Launch Online Master of Computer Vision Programme
This year, HSE University in Nizhny Novgorod will launch its Master of Computer Vision programme on Coursera. The fully online programme has been developed by leading experts from Huawei, ItSeez3D, Intel, Harman, and Xperience AI. It will be taught in English and is open to applicants from all countries. The closing date for applications is August 16, 2021.
Faster and More Precise: Researcher Improves Performance of Image Recognition Neural Network
A scientist from HSE University has developed an image recognition algorithm that works 40% faster than analogues. It can speed up real-time processing of video-based image recognition systems. The results of the study have been published in the journal Information Sciences.
"Master of Computer Vision" team started scientific collaboration with Huawei Technologies Co.
Andrey Savchenko, academic supervisor of “Master of Computer Vision" programme at HSE University has become a leader of an R&D project funded by Huawei Technologies Co.
First online Open Doors Day of "Master of Computer Vision"
You may have had great desire to visit all the unis you’re thinking of applying to, notebook in hand – and now you have a great option of the virtual open days on offer at the HSE University across online master`s programmes!
eSTARS 2020 Conference to Discuss Education in the Context of Global Digitalization
Jointly organized by HSE University and Coursera, the international eLearning Stakeholders and Researchers Summit (eSTARS 2020) will be held from December 1 – 2, 2020. It will be the third time the organizers bring together international stakeholders from governments, the academic community, busines, as well as researchers and experts to discuss the present and future of online education.
Scientists Teach the Neural Network to Carry Out Video Facial Recognition — Using a Single Photo
Researchers at the Higher School of Economics have proposed a new method of recognizing people on video with the help of a deep neural network
Internet Search Possible without Search Engines
Specialists from the HSE’s Nizhny Novgorod campus plan to create a new system of structuring data and accounting of webpages. The Laboratory of Algorithms and Technologies for Networks Analysis has won a grant from the Russian Science Foundation to study ‘Clustering and Search Techniques in Large Scale Networks.’