Artem SokolovView profile
Professor
Artem Sokolov serves as an Honorary Professor in the Department of Computational Linguistics at Heidelberg University and as a Research Scientist at Google Berlin. His primary research focuses on machine translation and structured prediction within natural language processing. Previously, he held positions at Amazon, the Statistical NLP Group at Heidelberg University led by Prof. Stefan Riezler, LIMSI, and Orange Labs in France, contributing to advancements in statistical and neural machine translation systems. He earned his PhD in Computer Science and Artificial Intelligence from the IRTCITS research center in Kyiv. His doctoral thesis investigated randomized algorithms for locality-sensitive embeddings of the Levenstein edit distance, establishing foundational work for efficient string similarity search in computational linguistics and intrusion detection systems. Dr. Sokolov's research expertise spans machine translation, imitation learning, bandit algorithms, and weakly supervised learning. He has pioneered methods for learning from partial feedback in structured prediction tasks, particularly addressing exposure bias in sequence generation and multi-facet evaluation of translation systems. His work bridges theoretical machine learning with practical NLP applications, emphasizing robustness against noisy data and scalable optimization techniques for real-world deployment. Analysis of his recent publications reveals trends toward scalable influence functions for model interpretability, multi-attribute control in machine translation, and rigorous auditing of multilingual datasets. His research consistently intersects natural language processing, machine learning optimization, and data quality assessment, with increasing emphasis on ethical AI considerations and efficient learning from weak supervision signals. Scientific awards include: 1st place at ECML/PKDD Discovery Challenge 2010 (English quality task) 2nd place at ECML/PKDD Discovery Challenge 2010 (general task) 2nd place in Semi-Supervised Feature Learning Challenge at NIPS 2011 3rd place in Semi-Supervised Feature Learning Challenge at NIPS 2011 As co-Principal Investigator for the 2015-2017 grant "Weakly Supervised Learning of Cross-Lingual Systems", Dr. Sokolov developed techniques for learning cross-lingual rankings from weakly supervised data sources like patent citations and Wikipedia hyperlinks. He has mentored students through teaching advanced courses including Imitation Learning, Stochastic Learning, and Statistical Machine Translation at Heidelberg University, supervising seminar projects on structured prediction and optimization algorithms. Dr. Sokolov is an active member of the Statistical NLP Group at Heidelberg University and collaborates with research teams at Google Berlin. His current work focuses on advancing production-scale machine translation systems through scalable inverse reinforcement learning and robust training methodologies, building on his extensive background in both academic research and industrial applications.
