My CV

Contact Details

Feel free to email me (address below) if you need more than just my email.

Education

Post-Doc, 2024–

LMU Munich

• Research topics: Character-level NLP, Low-Resource NLP
• Advisor: Prof. Alexander Fraser

Ph.D. Computational Linguistics, 2019–2023

University of Groningen

• Thesis: The Little Data That Could: Making the Most of Low-Resource Natural Language Processing
• Advisors: Dr. Antonio Toral, Prof. Gertjan van Noord

M.Sc. Artificial Intelligence, 2017–2019

University of Groningen

• Master’s thesis on unsupervised neural machine translation under advisors Dr. Antonio Toral and Dr. Jennifer Spenader

B.Sc. Computer Science, 2016–2017

Indiana University

• Graduated with Highest Honors with a 3.97/4.0 GPA

Computer Science Major, 2012–2016

University of Puget Sound

• Bachelor’s project on automatic reference-finding in the Latin Vulgate

Publications

Google Scholar, if you prefer

• Lukas Edman, Lisa Bylinina, Faeze Ghorbanpour, and Alexander Fraser. Are BabyLMs Second Language Learners?, Proceedings of the BabyLM Challenge at the 28th Conference on Computational Natural Language Learning. 2024.
• Lukas Edman, Helmut Schmid, and Alexander Fraser. CUTE: Measuring LLMs’ Understanding of Their Tokens, Proceedings of the Association for Computational Linguistics: EMNLP 2024.. 2024.
• Lukas Edman, Gabriele Sarti, Antonio Toral, Gertjan van Noord, and Arianna Bisazza. Are Character-level Translations Worth the Wait? Comparing ByT5 and mT5 for Machine Translation, Transactions of the Association for Computational Linguistics: TACL 2024. 2024.
• Lukas Edman, Lisa Bylinina. Too Much Information: Keeping Training Simple for BabyLMs, Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning. 2023.
• Konstantin Chernyshev, Ekaterina Garanina, Duygu Bayram, Qiankun Zheng, and Lukas Edman. LCT-1 at SemEval-2023 Task 10: Pre-training and Multi-task Learning for Sexism Detection and Classification, Proceedings of the 17th International Workshop on Semantic Evaluation (SemEval-2023). 2023.
• Lukas Edman, Antonio Toral, and Gertjan van Noord. Subword-Delimited Downsampling for Better Character-Level Translation, Findings of the Association for Computational Linguistics: EMNLP 2022. 2022.
• Lukas Edman, Antonio Toral, and Gertjan van Noord. Patching Leaks in the Charformer for Efficient Character-Level Generation, arXiv preprint arXiv:2205.14086. 2022.
• Lukas Edman, Antonio Toral, and Gertjan van Noord. The Importance of Context in Very Low Resource Language Modeling, 18th International Conference on Natural Language Processing (ICON2021). 2021.
• Lukas Edman, Ahmet Üstün, Antonio Toral, and Gertjan van Noord. Unsupervised Translation of German–Lower Sorbian: Exploring Training and Novel Transfer Methods on a Low-Resource Language, EMNLP 2021 Sixth Conference on Machine Translation (WMT21). 2021.
  • Achieved first place for Lower Sorbian→German translation
• Lukas Edman, Antonio Toral, and Gertjan van Noord. Low-Resource Unsupervised NMT: Diagnosing the Problem and Providing a Linguistically Motivated Solution, The 22nd Annual Conference of the European Association for Machine Translation (EAMT2020). 2020.
• Lukas Edman, Antonio Toral, and Gertjan van Noord. Data Selection for Unsupervised Translation of German–Upper Sorbian, EMNLP 2020 Fifth Conference on Machine Translation (WMT20). 2020.
• Christian Roest, Lukas Edman, Gosse Minnema, Kevin Kelly, Jennifer Spenader and Antonio Toral. Machine Translation for English–Inuktitut with Segmentation, Data Acquisition and Pre-Training, EMNLP 2020 Fifth Conference on Machine Translation (WMT20). 2020.
• Antonio Toral, Lukas Edman, Galiya Yeshmagambetova, and Jennifer Spenader. Neural Machine Translation for English–Kazakh with Morphological Segmentation and Synthetic Data, ACL 2019 Fourth Conference on Machine Translation (WMT19). 2019.
  • Achieved first place for English→Kazakh translation

Work Experience

Instructor, 2017–2023

University of Groningen

• Gave various lectures on Machine Learning, NLP, Computer Vision, and Audio Processing
• Supervised students on projects for SemEval shared tasks
• Led tutorial and computer lab sessions
• Wrote and graded coursework
• Invigilated and graded exams
• Courses taught (Master):
  • Shared Task Information Science, 2022–23
• Courses taught (Bachelor):
  • Introduction to Machine Learning, 2022–23
  • Machine Learning Project, 2021–22
• Courses assisted (Master):
  • Shared Task Information Science, 2020–22
  • Language Technology Project, 2020–22
  • Natural Language Processing, 2018–19
  • Pattern Recognition, 2018–19
• Courses assisted (Bachelor):
  • Machine Learning Project, 2020–21
  • Advanced Algorithms and Data Structures, 2018–19
  • Artificial Intelligence I, 2017–19

Thesis Supervisor, 2020–2023

University of Groningen

• Supervised Master’s students on their thesis projects.
• Project on low-resourec
• Project on translation of Dutch–Gronings and other Lower Saxon dialects.
• Project on unsupervised NMT for English–Chinese.

Reviewer, 2019–

LMU Munich, University of Groningen

• Regular reviewer for *ACL conferences and affiliated workshops.

Technical Skills

• Programming Languages
  • Currently using: Python, Shell
  • Prior experience with: Java, JavaScript, C
• Software and Libraries
  • Currently using: PyTorch, HuggingFace
  • Prior experience with: TensorFlow