Daniel Krakowczyk

VLDB 2026 Research / reviewers in the wild / expert

dblp:178/4329 · also Daniel G. Krakowczyk · DBLP profile ↗

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9ranked-venue papers

3as first author

8since 2021 · last 2026

0009-0009-5100-0733ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Human-computer interaction and ubiquitous computing · 7 · 3 first-author · 6 since 2021Graphics, computer vision, multimedia, augmented reality and games · 6 · 3 first-author · 6 since 2021Artificial intelligence and machine learning · 2 · 2 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021

Year	Publication	Venue	Position
2026	Preprocessing Eye-Tracking Data for Reading: A Community Survey on Practices, Challenges, and Needs abstract Preprocessing eye-tracking data is a critical yet challenging step in eye-tracking research, particularly in reading studies. Current practices are characterized by limited standardization, a strong reliance on proprietary or inflexible tools, and heterogeneous documentation routines, which hinder reproducibility, comparability, and data reuse. To systematically assess community practices and needs, we conducted a large-scale survey (N = 108) with a focus on reading research. The survey examined preprocessing workflows, tool usage, perceived challenges, data sharing and documentation strategies, and expectations regarding preprocessing tools and outputs. The findings indicate a strong demand for transparent, interoperable, and well-documented preprocessing workflows, as well as practical guidance such as tutorials and user support. Participants emphasized the importance of standardized terminology, flexible automation, and reusable output structures. These results provide an empirical basis for tool development, methodological guidelines, and infrastructure initiatives aimed at improving transparency, standardization, and long-term reusability in eye-tracking research. Marie-Luise Müller, Carlson Moses Büth, Deborah N. Jakobi, Daniel Krakowczyk, Eva Pavlinusic Vilus, Anastassia Shaitarova, Lena A. Jäger	ETRA	4
2025	MultiplEYE: Creating a multilingual eye-tracking-while-reading corpus abstract Contains fulltext : 326363.pdf (Publisher’s version ) (Open Access) Deborah N. Jakobi, Maja Stegenwallner-Schütz, Nora Hollenstein, Cui Ding, Ramune Kaspere, Ana Matic Skoric, Eva Pavlinusic Vilus, Stefan Frank, Marie-Luise Müller, Kristine M. Jensen de López, Nik Kharlamov, Hanne B. Søndergaard Knudsen, Yevgeni Berzak, Ella Lion, Irina A. Sekerina, Cengiz Acartürk, Mohd Faizan Ansari, Katarzyna Harezlak, Pawel Kasprowski, Ana Bautista, Lisa Beinborn, Anna Bondar, Antonia Boznou, Leah Bradshaw, Jana Mara Hofmann, Thyra Krosness, Not Battesta Soliva, Anila Çepani, Kristina Cergol, Ana Dosen, Marijan Palmovic, Adelina Çerpja, Dalí Chirino, Jan Chromý, Vera Demberg, Iza Skrjanec, Nazik Dinçtopal Deniz, Inmaculada Fajardo, Mariola Giménez-Salvador, Xavier Mínguez-López, Maros Filip, Zigmunds Freibergs, Jessica Gomes, Andreia Janeiro, Paula Luegi, João Veríssimo, Sasho Gramatikov, Jana Hasenäcker, Alba Haveriku, Nelda Kote, Muhammad Mohsin Kamal, Hanna Kedzierska, Dorota Klimek-Jankowska, Sara Kosutar, Daniel Krakowczyk, Izabela Krejtz, Marta Lockiewicz, Kaidi Lõo, Jurgita Motiejuniene, Jamal Abdul Nasir, Johanne Sofie Krog Nedergård, Aysegül Özkan, Mikulás Preininger, Loredana Punga, David R. Reich, Chiara Tschirner, Spela Rot, Andreas Säuberli, Jordi Solé i Casals, Ekaterina Strati, Igor Svoboda, Evis Trandafili, Spyridoula Varlokosta, Mila Dimitrova-Vulchanova, Lena A. Jäger	ETRA	55
2025	The More the Merrier: Boost Your Dataset Visibility and Discover Eye-Tracking Datasets with pymovements Daniel Krakowczyk, David R. Reich, Andreas Säuberli, Iza Skrjanec, Isabelle Caroline Rose Cretton, Deborah N. Jakobi, Sergiu Nisioi, Paul Prasse, Lena A. Jäger	ETRA	1
2024	Reporting Eye-Tracking Data Quality: Towards a New Standard abstract Eye-tracking datasets are often shared in the format used by their creators for their original analyses, usually resulting in the exclusion of data considered irrelevant to the primary purpose. In order to increase re-usability of existing eye-tracking datasets for more diverse and initially not considered use cases, this work advocates a new approach of sharing eye-tracking data. Instead of publishing filtered and pre-processed datasets, the eye-tracking data at all pre-processing stages should be published together with data quality reports. In order to transparently report data quality and enable cross-dataset comparisons, we develop data quality reporting standards and metrics that can be automatically applied to a dataset, and integrate them into the open-source Python package pymovements (https://github.com/aeye-lab/pymovements). Deborah N. Jakobi, Daniel Krakowczyk, Lena A. Jäger	ETRA	2
2023	Bridging the Gap: Gaze Events as Interpretable Concepts to Explain Deep Neural Sequence Models abstract Recent work in XAI for eye tracking data has evaluated the suitability of feature attribution methods to explain the output of deep neural sequence models for the task of oculomotric biometric identification. These methods provide saliency maps to highlight important input features of a specific eye gaze sequence. However, to date, its localization analysis has been lacking a quantitative approach across entire datasets. In this work, we employ established gaze event detection algorithms for fixations and saccades and quantitatively evaluate the impact of these events by determining their concept influence. Input features that belong to saccades are shown to be substantially more important than features that belong to fixations. By dissecting saccade events into sub-events, we are able to show that gaze samples that are close to the saccadic peak velocity are most influential. We further investigate the effect of event properties like saccadic amplitude or fixational dispersion on the resulting concept influence. Daniel Krakowczyk, Paul Prasse, David R. Reich, Sebastian Lapuschkin, Tobias Scheffer, Lena A. Jäger	ETRA	1
2023	pymovements: A Python Package for Eye Movement Data Processing abstract We introduce pymovements: a Python package for analyzing eye-tracking data that follows best practices in software development, including rigorous testing and adherence to coding standards. The package provides functionality for key processes along the entire preprocessing pipeline. This includes parsing of eye tracker data files, transforming positional data into velocity data, detecting gaze events like saccades and fixations, computing event properties like saccade amplitude and fixational dispersion and visualizing data and results with several types of plotting methods. Moreover, pymovements also provides an easily accessible interface for downloading and processing publicly available datasets. Additionally, we emphasize how rigorous testing in scientific software packages is critical to the reproducibility and transparency of research, enabling other researchers to verify and build upon previous findings. Daniel Krakowczyk, David R. Reich, Jakob Chwastek, Deborah N. Jakobi, Paul Prasse, Assunta Süss, Oleksii Turuta, Pawel Kasprowski, Lena A. Jäger	ETRA	1
2023	Quantus: An Explainable AI Toolkit for Responsible Evaluation of Neural Network Explanations and Beyond abstract The evaluation of explanation methods is a research topic that has not yet been explored deeply, however, since explainability is supposed to strengthen trust in artificial intelligence, it is necessary to systematically review and compare explanation methods in order to confirm their correctness. Until now, no tool with focus on XAI evaluation exists that exhaustively and speedily allows researchers to evaluate the performance of explanations of neural network predictions. To increase transparency and reproducibility in the field, we therefore built Quantus—a comprehensive, evaluation toolkit in Python that includes a growing, well-organised collection of evaluation metrics and tutorials for evaluating explainable methods. The toolkit has been thoroughly tested and is available under an open-source license on PyPi (or on https://github.com/understandable-machine-intelligence-lab/Quantus/). Anna Hedström, Leander Weber, Daniel Krakowczyk, Dilyara Bareeva, Franz Motzkus, Wojciech Samek, Sebastian Lapuschkin, Marina M.-C. Höhne	J. Mach. Learn. Res.	3
2022	Detection of ADHD Based on Eye Movements During Natural Viewing Shuwen Deng, Paul Prasse, David R. Reich, Sabine Dziemian, Maja Stegenwallner-Schütz, Daniel Krakowczyk, Silvia Makowski, Nicolas Langer, Tobias Scheffer, Lena A. Jäger	ECML/PKDD (6)	6
2020	Ring-based finger tracking using capacitive sensors and long short-term memory abstract We present a ring-shaped interaction device, called PeriSense, utilizing capacitive sensing in order to enable finger tracking. The finger angles and its adjacent fingers are sensed by measuring capacitive proximity between electrodes and human skin. To map the capacitive measurements to the finger angles, we use long short-term memory (LSTM). By wearing the ring on the middle finger, the angles of the index, middle and ring finger can be determined from the capacitive measurements. We collected sample data from 17 users using a Leap Motion camera, which provided the reference data. In a leave-one-user-out cross-validation test, we revealed a mean absolute error of 13.02 degrees over all finger angles. The motion of the little finger and the thumb are out of the capacitive measurement range or covered by the index and ring finger, respectively. With natural finger movements, the LSTM estimates the angles of the little finger and thumb based on the movement pattern of the other fingers. Mathias Wilhelm 0002, Jan-Peter Lechler, Daniel Krakowczyk, Sahin Albayrak	IUI	3