Hi there, I'm Jan Trienes, a PhD student of computer science at the University of Duisburg-Essen, supervised by Prof. Christin Seifert. My research interests include natural language processing and information retrieval. For my PhD research, I focus on text simplification for clinical notes. I am part of the Institute for Artificial Intelligence in Medicine at the University Hospital Essen.

For the fall semester of 2023, I am visiting the University of Texas at Austin to work with Jessy Li on factuality and analysis of medical text simplifications. Prior to my PhD, I worked as machine learning engineer at Nedap Healthcare. During my master studies at the University of Twente, I did a research internship at the University of Stavanger, visiting Prof. Krisztian Balog.

Education

• PhD – Computer Science, University of Duisburg-Essen, Germany (03/2021 – today)
• MSc – Computer Science, University of Twente, Netherlands (2017 – 2019)
• BSc – Software Engineering, Fontys University, Netherlands (2013 – 2017)

Experience

• Visiting Scholar, University of Texas at Austin, United States (fall 2023). Visiting Jessy Li at UT Austin NLP. My stay was supported through the competitive DAAD IFI scholarship.
• Machine Learning Engineer, Nedap Healthcare, Netherlands (2019 – 2021)
• Graduate Intern, Nedap Healthcare, Netherlands (2018 – 2019)
• Research Intern, University of Stavanger, Norway (summer 2018). Visiting IAI Group led by Krisztian Balog.
• Graduate Intern, codecentric AG, Germany (2017)
• DevOps Intern, Capgemini, Germany (2016)

Activities

• Participant at the Mediterranean Machine Learning Summer School (M2L). September 2022.
• Participant at the Eastern European Machine Learning Summer School (EEML). July 2021. ⭐ Best Poster Award.
• Student volunteer at EMNLP 2022.
• Programme committee: ACL (2023), EMNLP (2022), ECIR (2024, 2023, 2022).

Publications

Guidance in Radiology Report Summarization: An Empirical Evaluation and Error Analysis
Jan Trienes, Paul Youssef, Jörg Schlötterer, Christin Seifert
16th International Natural Language Generation Conference (INLG), 2023, ⭐ Nomination for Best Evaluation Award
[pdf] [code] [slides]

From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
Meike Nauta, Jan Trienes, Shreyasi Pathak, Elisa Nguyen, Michelle Peters, Yasmin Schmitt, Jörg Schlötterer, Maurice van Keulen, Christin Seifert
ACM Computing Surveys (CSUR), 2023
[pdf]

Patient-friendly Clinical Notes: Towards a new Text Simplification Dataset
Jan Trienes, Jörg Schlötterer, Hans-Ulrich Schildhaus, Christin Seifert
EMNLP Workshop on Text Simplification, Accessibility, and Readability (TSAR), 2022
[pdf] [code] [slides] [poster]

Generating Synthetic Training Data for Supervised De-Identification of Electronic Health Records
Claudia A. Libbi, Jan Trienes, Dolf Trieschnigg, Christin Seifert
Future Internet, 13(5):136, 2021
[pdf] [code]

Comparing Rule-based, Feature-based and Deep Neural Methods for De-identification of Dutch Medical Records
Jan Trienes, Dolf Trieschnigg, Christin Seifert, Djoerd Hiemstra
1st ACM WSDM Health Search and Data Mining Workshop (HSDM’20), 2020, ⭐ Best Paper Award
[pdf] [code] [slides]

Identifying Unclear Questions in Community Question Answering Websites
Jan Trienes, Krisztian Balog
41st European Conference on Information Retrieval (ECIR’19), 2019
[pdf] [code]

Recommending Users: Whom to Follow on Federated Social Networks
Jan Trienes, Andrés Torres Cano, Djoerd Hiemstra
17th Dutch-Belgian Information Retrieval Workshop (DIR’18), 2018
[pdf]