🧑‍🔬 Alexey Svyatkovskiy

🏛️ Affiliations

🏢Google DeepMindLondon · United Kingdom2025 · 1 papercurrent🎓Purdue University West LafayetteWest Lafayette · United States2010–2022 · 436 papers🔬Institute of High Energy PhysicsVienna · Austria2011–2017 · 27 papers🎓Princeton UniversityPrinceton · United States2015–2018 · 26 papersEmployment 2015–2016 · Big Data Analyst, Princeton Institute for Computation Science and EngineeringEmployment 2016–2018 · Research Specialist, Princeton Institute for Computational Science and Engineering🏛️Austrian Academy of SciencesVienna · Austria2011–2017 · 25 papers🎓University of Puerto Rico-MayaguezMayagüez · Puerto Rico (US)2015–2018 · 18 papers🔬European Organization for Nuclear ResearchGeneva · Switzerland2012–2013 · 18 papers🏢MicrosoftRedmond · United States2020–2024 · 15 papers🎓Purdue University NorthwestHammond · United States2014–2017 · 6 papers🎓Peking UniversityBeijing · China2016–2017 · 5 papers

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine. 11 more institutions on single bylines are not listed.

🧭 Fields, topics and concepts

Of its works in our library, with the number of works on each.

📅 Works by year

2020s

📄 Papers

The most cited first (OpenAlex and our library).

1. GraphCodeBERT: Pre-training Code Representations with Data Flow (2021 · 332 citations)
   Most pre-trained models for source code read a program as a plain token sequence. This ignores structural information that shows what the code actually does. GraphCodeBERT adds data flow to the input: a graph in which variables are nodes and directed edges show which variables each value is derived from. The authors…
2. PyMT5: multi-mode translation of natural language and Python code with transformers (2020 · 158 citations)
   The paper tackles joint modeling of Python source code and its natural-language documentation. The authors propose a single sequence-to-sequence transformer, PyMT5, that converts in both directions: it writes method bodies from signatures or docstrings, and it summarizes code into docstrings. Code and text are…
3. ReACC: A Retrieval-Augmented Code Completion Framework (2022 · 125 citations)
   Most code completion models only see the file or project being edited. Programmers, however, often reuse similar code from elsewhere. ReACC adds this external context by searching a large codebase for code that resembles the unfinished snippet and feeding the matches to the generator.

🤝 Co-authors here

10 co-authors with a page, the most papers together first.

• Daya Guo ×2
• Nan Duan ×2
• Shuai Lu ×2
• Dawn Drain
• Duyu Tang
• Long Zhou
• Seung-won Hwang
• Shujie Liu
• Shuo Ren
• Zhangyin Feng

Venues

Where their works here were published, the most works first, with the editions of those that have a page.

• Annual Meeting of the Association for Computational Linguistics (ACL): ACL 2022
• Conference on Empirical Methods in Natural Language Processing (EMNLP): EMNLP 2020

🔗 Cited by

Papers of our library cite their works 246 times (of the 149,439 papers whose bibliographies we read); 40 citing papers with a page here →.

Updated 30 October 2026

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