A. Savchenko

2.8k total citations · 1 hit paper
125 papers, 1.5k citations indexed

About

A. Savchenko is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Signal Processing. According to data from OpenAlex, A. Savchenko has authored 125 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 62 papers in Computer Vision and Pattern Recognition, 33 papers in Artificial Intelligence and 14 papers in Signal Processing. Recurrent topics in A. Savchenko's work include Face recognition and analysis (24 papers), Face and Expression Recognition (24 papers) and Emotion and Mood Recognition (14 papers). A. Savchenko is often cited by papers focused on Face recognition and analysis (24 papers), Face and Expression Recognition (24 papers) and Emotion and Mood Recognition (14 papers). A. Savchenko collaborates with scholars based in Russia, United States and France. A. Savchenko's co-authors include L. V. Savchenko, Ilya Makarov, В. В. Савченко, Arkady Lyubarsky, Edward N. Pugh, Waixing Tang, Philip M. Smallwood, Carol Cooke, Enrico Maria Surace and Tonia S. Rex and has published in prestigious journals such as Neuron, Journal of Neuroscience and SHILAP Revista de lepidopterología.

In The Last Decade

A. Savchenko

113 papers receiving 1.4k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Classifying Emotions and Engagement in Online Learning Based on a Single Facial Expression Recognition Neural Network

2022 163 citations A. Savchenko, Ilya Makarov et al. profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
A. Savchenko Russia	19	465	403	306	192	159	125	1.5k
Nini Rao China	22	239 0.5×	761 1.9×	129 0.4×	36 0.2×	72 0.5×	86	1.7k
Dileep George United States	18	182 0.4×	113 0.3×	340 1.1×	146 0.8×	25 0.2×	42	1.3k
George Tucker United States	16	191 0.4×	608 1.5×	421 1.4×	28 0.1×	35 0.2×	32	1.8k
Rolf P. Würtz Germany	17	1.3k 2.7×	107 0.3×	322 1.1×	41 0.2×	78 0.5×	55	2.1k
Wieland Brendel Germany	14	494 1.1×	122 0.3×	839 2.7×	139 0.7×	33 0.2×	44	1.9k
Pengyu Hong United States	29	433 0.9×	1.3k 3.1×	272 0.9×	252 1.3×	29 0.2×	100	2.6k
Klaus Obermayer Germany	20	594 1.3×	179 0.4×	859 2.8×	161 0.8×	15 0.1×	85	1.7k
Vı́ctor Robles Spain	15	297 0.6×	579 1.4×	691 2.3×	131 0.7×	8 0.1×	41	1.6k
John C. Dill Canada	20	520 1.1×	238 0.6×	121 0.4×	408 2.1×	20 0.1×	70	1.5k
W. von Seelen Germany	21	580 1.2×	68 0.2×	365 1.2×	115 0.6×	40 0.3×	78	1.5k

Countries citing papers authored by A. Savchenko

Since Specialization

Citations

This map shows the geographic impact of A. Savchenko's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by A. Savchenko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites A. Savchenko more than expected).

Fields of papers citing papers by A. Savchenko

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by A. Savchenko. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by A. Savchenko. The network helps show where A. Savchenko may publish in the future.

Co-authorship network of co-authors of A. Savchenko

This figure shows the co-authorship network connecting the top 25 collaborators of A. Savchenko. A scholar is included among the top collaborators of A. Savchenko based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with A. Savchenko. A. Savchenko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

1.

Gusev, Gleb, et al.. (2025). Multimodal Banking Dataset: Understanding Client Needs through Event Sequences. 6476–6480.

2.

Savchenko, A., et al.. (2025). Simplicial SMOTE: Oversampling Solution to the Imbalanced Learning Problem. 625–635. 1 indexed citations

3.

Savchenko, A., et al.. (2025). HL-EAI: A Multimodal Framework Enabling Emotional Reciprocity in Human-AI Strategic Decision-Making. 13501–13503.

4.

Savchenko, A., et al.. (2025). Learning transactions representations for information management in banks: Mastering local, global, and external knowledge. International Journal of Information Management Data Insights. 5(1). 100323–100323. 1 indexed citations

5.

Golovina, Vera A., et al.. (2025). An iterative strategy to design 4-1BB agonist nanobodies de novo with generative AI models. Scientific Reports. 15(1). 25412–25412. 1 indexed citations

6.

Khrabrov, Kuzma, et al.. (2024). Lost in Translation: Chemical Language Models and the Misunderstanding of Molecule Structures. 12994–13013. 1 indexed citations

7.

Alekseev, Anton, et al.. (2024). Neural Click Models for Recommender Systems. arXiv (Cornell University). 2553–2558. 1 indexed citations

8.

Savchenko, A., et al.. (2024). From Data to Decisions: Streamlining Geospatial Operations with Multimodal GlobeFlowGPT. 649–652. 2 indexed citations

9.

Alekseev, Anton, et al.. (2024). Blending of Predictions Boosts Understanding for Multimodal Advertisements. Journal of Mathematical Sciences. 285(1). 126–141.

10.

Savchenko, A., et al.. (2024). EmotiEffNet and Temporal Convolutional Networks in Video-based Facial Expression Recognition and Action Unit Detection. 4849–4859. 1 indexed citations

11.

Savchenko, A., et al.. (2024). Pose Networks Unveiled: Bridging the Gap for Monocular Depth Perception. 584–587. 1 indexed citations

12.

Savchenko, A., et al.. (2024). Boosting Depth Estimation for Self-Driving in a Self-Supervised Framework via Improved Pose Network. SHILAP Revista de lepidopterología. 6. 109–118.

13.

Savchenko, A., et al.. (2024). Enhancing Autonomous Driving With Spatial Memory and Attention in Reinforcement Learning. IEEE Access. 12. 173316–173324.

14.

Savchenko, A., et al.. (2023). A standalone software for real-time facial analysis in online conferences and e-lessons. Software Impacts. 16. 100507–100507.

15.

Savchenko, A., et al.. (2023). Facial expression recognition based on adaptation of the classifier to videos of the user. Computer Optics. 47(5). 806–815. 1 indexed citations

16.

Savchenko, A. & В. В. Савченко. (2019). A method of the speech signal pitch frequency measurement for acoustic speech analysis systems. Izmeritel`naya Tekhnika. 59–63. 3 indexed citations

17.

Savchenko, A., et al.. (2018). Data organization in video surveillance systems using deep learning. 243–250. 1 indexed citations

18.

Savchenko, A., et al.. (2017). Compressing deep convolutional neural networks in visual emotion recognition. 207–213. 3 indexed citations

19.

Savchenko, A.. (2012). The choice of algorithm parameters in image recognition on the basis of ensemble classifiers and the maximum posterior probability principle. Computer Optics. 36(1). 116–123. 1 indexed citations

20.

Rattner, Amir, Philip M. Smallwood, John C. Williams, et al.. (2001). A Photoreceptor-Specific Cadherin Is Essential for the Structural Integrity of the Outer Segment and for Photoreceptor Survival. Neuron. 32(5). 775–786. 106 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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