Research

The actual workgroups  explore  central, modern and  attractive topics in computer science::

They are well embedded in the national and international societies, and participated in several larger research institutes, specially the Exzellenzcluster Cells in Motion, the Sonderforschungsbereich Molekulare kardiovaskuläre Bildgebung(MoBil) and the founding of the European Institute for Molecular Imaging (EIMI) and the Center for Nonlinear Science (CENOS).

Aside of the Institut für Informatik there are several other groups in the WWU with activities in research and teaching on computer science. Topics there belong mostly to the field of applied computer science,  like Wirtschaftsinformatik, Geoinformatik, Bioinformatik and medizinische Informatik:

We work together, many courses from these areas are eligible as interdisciplinary studies for the graduation in Master of Computer Science and thus allow in-depth insights into the applications of computer technology.

For an overview, see Alle Informatiken [de], or the menu "associated groups" on the  Informatik-Homepage.

 

  • Current Projects

    • Safe ILIAS – Safe Integration of Learning In Autonomous cyber-physical Systems ( – )
      Individual Granted Project: DFG - Individual Grants Programme | Project Number: HE 6733/5-1
    • Clavicle-ML – Retrospektive CT-Untersuchungen zur Schlüsselbeinossifikation - Entwicklung eines klinischen Entscheidungshilfesystems mit skalenbasierten Bewertungen und modernen Methoden des maschinellen Lernens zur Verbesserung der Gültigkeit und Zuverlässigkeit forensischer Altersbegutachtungen ( – )
      Individual Granted Project: DFG - Individual Grants Programme | Project Number: LI 1530/31-1; SCHM 1609/8-1
    • FOR 5393: The future smart town ( – )
      Main DFG-Project Hosted at the University of Münster: DFG - Research Unit
    • FOR 5393: The future smart town - Subproject: Efficient and identity-enhancing law enforcement in the municipality of a mid-sized town ( – )
      Subproject in DFG-Joint Project Hosted at the University of Münster: DFG - Research Unit | Project Number: SCHO 1965/1-1
    • InFlame – Else Kröner Medical Scientist Kolleg Münster - Dynamik von Entzündungsreaktionen ( – )
      participations in other joint project: Else Kröner Medical Scientist Kolleg | Project Number: 2021_EKMK.13
    • ReproTrackMS – Centre for Research and Development of Reproductive Scientists ( – )
      participations in bmbf-joint project: Federal Ministry of Education and Research | Project Number: 01GR2303
    • E-Mobilität für LKWs - Prädiktive KI Modelle ( – )
      Individual Granted Project: MAN Truck & Bus SE
    • Interdisciplinary cooperation with the Glorius Group of the Organic Chemistry Institute in the field of machine learning and data analysis ( – )
      Own Resources Project
    • SPP 2363 - Teilprojekt: Neuronale Fingerabdrücke als struktur- und aktivitätssensitive molekulare Darstellungen ( – )
      Subproject in DFG-Joint Project Hosted at the University of Münster: DFG - Priority Programme | Project Number: KO 4689/7-1; RI 2938/3-1
    • InterKIWWU – Interdisziplinäres Lehrprogramm zu maschinellem Lernen und künstlicher Intelligenz ( – )
      Individual Granted Project: Federal Ministry of Education and Research | Project Number: 16DHBKI049
    • maQinto – Maschinell trainierter Qualitätssensor, intelligente Prozessteuerung und ein ML-Framework zur ressourceneffizienten, maßgeschneiderten Kohlenstofffaserherstellung ( – )
      participations in bmbf-joint project: Federal Ministry of Education and Research | Project Number: 01I522020D
    • PPP-DL – Performance, Portabilität und Produktivität für Deep-Learning Anwendungen auf Multi- und Many-Core Architekturen ( – )
      Individual Granted Project: DFG - Individual Grants Programme | Project Number: GO 756/8-1
    • CRC 1450 A05 - Targeting immune cell dynamics by longitudinal whole-body imaging and mathematical modelling ( – )
      Subproject in DFG-Joint Project Hosted at the University of Münster: DFG - Collaborative Research Centre | Project Number: SFB 1450/1, A05
    • GAIA – Joint Research Training Group: „Trustworthy AI for Seamless Problem solving: Next generation Intelligence Joins Robust Data Analysis“ (Data NInJA) - PhD topic: "Gaußprozesse für automatische und interpretierbare Anomalie-Erkennung" ( – )
      participations in other joint project: MKW - Förderlinie „Künstliche Intelligenz/Maschinelles Lernen“ - Standortübergreifendes Graduiertenkolleg | Project Number: 005-2010-0003
    • CRC 1450 Z01 - Interactive and computational analysis of large multiscale imaging data ( – )
      Subproject in DFG-Joint Project Hosted at the University of Münster: DFG - Collaborative Research Centre | Project Number: SFB 1450/1, Z01
    • CRC 1450 B04 - Multiscale visualisation and analysis of innate immune cell migration at sites of hypoxic inflammation in vivo ( – )
      Subproject in DFG-Joint Project Hosted at the University of Münster: DFG - Collaborative Research Centre | Project Number: SFB 1450/1, B04
    • CRC 1459 C05 - Coherent nanophotonic neural networks with adaptive molecular systems ( – )
      Subproject in DFG-Joint Project Hosted at the University of Münster: DFG - Collaborative Research Centre | Project Number: SFB 1459/1, C05
    • HiRes – HiResHemo: Hemodynamics at High Spatio-temporal Resolution by Comparative Visual Analysis of 4D PC-MRI Data and CFD Simulation Ensembles ( – )
      Individual Granted Project: DFG - Individual Grants Programme | Project Number: LI 1530/28-1; HO 5231/3-1
    • meditrain – Joint project: Modular Virtual Reality training of clinical scenarios with AI driven, interactive patients ( – )
      participations in bmbf-joint project: Federal Ministry of Education and Research | Project Number: 16DHBKI077
    • InChangE – Individualisierung in sich ändernden Umwelten ( – )
      participations in other joint project: MKW - Förderlinie "Profilbildung" | Project Number: PROFILNRW-2020-143-B
    • Friends with benefits? A holistic approach to diffuse mutualism in plant-pollinator interactions ( – )
      participations in other joint project: HFSP - Research Grant - Program | Project Number: RGP0057/2021
    • STATE – SystemC to Timed Automata Transformation Engine (since )
      Own Resources Project
    • Applied IoT Data Analytics (since )
      Own Resources Project
    • IGS – Informatik in der Grundschule (since )
      Own Resources Project
    • Query Processing (since )
      Own Resources Project
    • Metric and Ptolemaic Access Methods (since )
      Own Resources Project
    • Similarity Search (since )
      Own Resources Project

  • Latest Publications

    • Vogel-Heuser B.; Fay A.; Rupprecht B.; Kunze F.C.; Hankemeier V.; Westermann T.; Manca G. . ‘Exploring challenges of alarm root-cause analysis across varying production process types Herausforderungen der Alarm Root-Cause Analyse in verschiedenen Arten von Produktionsprozessen.’ Automatisierungstechnik 72, No. 4: 369–386. doi: https://doi.org/10.1515/auto-2023-0180.
    • Steinhorst, Phil; Duhme, Christof; Jiang, Xiaoyi; Vahrenhold, Jan. . ‘Recognizing Patterns in Productive Failure.’ In Proceedings of the 55th ACM Technical Symposium on Computer Science Education V. 1, edited by Battestilli, Lina; Rebelsky, Samuel; Shoop, Libby, 1293–1299. New York, NY: ACM Press. doi: 10.1145/3626252.3630915.
    • Hellsten EO; Souza A; Lenfers J; Lacouture R; Hsu O; Ejjeh A; Kjolstad F; Steuwer M; Olukotun K; Nardi L. . ‘BaCO: A Fast and Portable Bayesian Compiler Optimization Framework.’ In Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4, edited by Association for Computing Machinery, 19–42. New York, NY, USA: Association for Computing Machinery. doi: 10.1145/3623278.3624770.
    • Delicaris, Joanna; Stübbe, Jonas; Schupp, Stefan; Remke, Anne. . ‘RealySt: A C++ Tool for Optimizing Reachability Probabilities in Stochastic Hybrid Systems.’ In Performance Evaluation Methodologies and Tools, edited by Kalyvianaki, Evangelia; Paolieri, Marco, 170–182. Cham: Springer. doi: 10.1007/978-3-031-48885-6_11.
    • Beckmann, Daniel; Kockwelp, Jacqueline; Gromoll, Joerg; Kiefer, Friedemann;Risse, Benjamin. . ‘SAM meets Gaze: Passive Eye Tracking for Prompt-based Instance Segmentation.’ Proceedings of Machine Learning Research . [accepted / in Press (not yet published)]
    • Schick, Johannes; Wagner, Marc; Lippe, Wolfram-Manfred. . ‘Graphical and Textual Models Embedded in a Constructor-Driven Transformation.’ In Proceedings of the 18th IEEE International Conference on Semantic Computing, edited by IEEE Computer Society, or the Institute of Electrical and Electronics Engineers, Inc., 226–230. Piscataway, NJ : Wiley-IEEE Computer Society Press. doi: 10.1109/ICSC59802.2024.00065.
    • Evers, Marina; Derstroff, Adrian; Leistikow, Simon; Schneider, Tom; Mallepree, Larissa; Stampke, Jan; Leisgang, Moritz; Sprafke, Sebastian; Schuhl, Melina; Krefft, Niklas; Droese, Felix; Linsen, Lars. . ‘Visual analytics of soccer player performance using objective ratings.’ Information Visualization 1–15. doi: 10.1177/14738716231220539.
    • Dornbusch, Maja; Vahrenhold, Jan. . ‘"In the Beginning, I Couldn't Necessarily Do Anything With It": Links Between Compiler Error Messages and Sense of Belonging.’ In Proceedings of the 2024 ACM Conference on International Computing Education Research (ICER 2024), edited by Denny, Paul; Porter, Leo; Hamilton, Margaret; Morrison, Briana. New York, NY: ACM Press. doi: 10.1145/3632620.3671105. [accepted / in Press (not yet published)]
    • Wortmann, Carolin; Vahrenhold, Jan. . ‘Regulation, Self-Efficacy, and Participation in CS1 Group Work.’ In Proceedings of the 2024 ACM Conference on International Computing Education Research (ICER 2024), edited by Denny, Paul; Porter, Leo; Hamilton, Margaret; Morrison, Briana. New York, NY: ACM Press. doi: 10.1145/3632620.3671115. [accepted / in Press (not yet published)]
    • Tasche P; Monti RE; Drerup SE; Blohm P; Herber P; Huisman M. . ‘Deductive Verification of Parameterized Embedded Systems Modeled in {SystemC}.’ In Verification, Model Checking, and Abstract Interpretation - 25th International Conference, {VMCAI} 2024, edited by Dimitrova, Rayna ; Lahav, Ori; Wolff, Sebastian, 187–209. London: Springer. doi: 10.1007/978-3-031-50521-8\_9.
    • Jarrous-Holtrup, S; Abdinghoff, J; Schamel, F; Gorlatch, S. . ‘Towards an Autoscaling Service for Real-Time Online Interactive Applications on Clouds.’ In Euromicro Conference on Parallel, Distributed and Network-Based Processing, edited by Chis, A; González-Vélez, H., 1–8. Dublin: Wiley-IEEE Press. doi: 10.1109/PDP62718.2024.00024.
    • Tomak, J.; Liermann, A.; Gorlatch, S. . ‘Performance Evaluation of a Legacy Real-Time System: An Improved RAST Approach.’ In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, edited by Guisado-Lizar, JL.; Riscos-Núñez, A.; Morón-Fernández, MJ.; Wainer, G., 18–33. Cham: Springer. doi: 10.1007/978-3-031-57523-5_2.
    • Garanina, N.; Gorlatch, S. . ‘KNOWLEDGE ACQUISITION IN MULTI-AGENT SYSTEMS: A FORMALIZATION OF THE ELEUSIS CARD GAME.’ Journal of Mathematical Sciences 281, No. 2. doi: 10.1007/s10958-024-07107-y.
    • Rasch, Ari. . ‘(De/Re)-Composition of Data-Parallel Computations via Multi-Dimensional Homomorphisms.’ ACM Transactions on Programming Languages and Systems Just Accepted. doi: 10.1145/3665643.
    • Xiao J, Zhong Y, Jia Y, Wang Y, Jiang X, Wang S. . ‘A novel deep ensemble model for imbalanced credit scoring in internet finance.’ International Journal of Forecasting 40, No. 1: 348–372.
    • Xiao J, Wen Z, Jiang X, Yu L, Wang S. . ‘Three-stage research framework to assess and predict the financial risk of SMEs based on hybrid method.’ Decision Support Systems 177: 114090.
    • Chen J, Pi D, Jiang X, Xu Y, Chen Y, Wang X. . ‘Denosieformer: A transformer based approach for single-channel EEG artifact removal.’ IEEE Transactions on Instrumentation and Measurement 73: 1–16.
    • Eminaga o, Saad F, Tian Z, Wolffgang U, Karakiewicz P, Ouellet V, Azzi F, Spieker T, Helmke B, Graefen M, Jiang X, Xing L, Witt J, Trudel D, Leyh-Bannurah SM. . ‘Artificial intelligence unravels interpretable malignancy grades of prostate cancer on histology images.’ npj Imaging 2: 6.
    • Zhang Q, Jiang X. . ‘Classification performance boosting for interpolation kernel machines by training set pruning using genetic algorithm.’ In Prof. of ICPRAM, edited by M. Castrillon-Santana, M. De Marsico, A. Fred, 428–435. Rome: SciTePress - Science and and Technology Publications.
    • Hegselmann S, Shen Z, Gierse F, Agrawal M, Sontag D, Jiang X. . ‘A data-centric approach to generate faithful and high quality patient summaries with large language models.’ In Conference on Health, Inference, and Learning (CHIL). [accepted / in Press (not yet published)]
    • Vahrenhold, Jan. . „Das systematische Vorgehen im Fokus.“ In Wirksamer Informatikunterricht. Unterrichtsqualität: Perspektiven von Expertinnen und Experten, herausgegeben von Komm, Dennis, 198–206. Baltmannsweiler: Schneider Verlag Hohengehren.
    • Niehage, Mathis; Remke, Anne. . ‘The Best of Both Worlds: Analytically-Guided Simulation of HPnGs for Optimal Reachability.’ In Performance Evaluation Methodologies and Tools - 16th EAI International Conference, VALUETOOLS 2023, Crete, Greece, September 6–7, 2023, Proceedings, edited by Kalyvianaki, Evangelia; Paolieri, Marco, 61–81. Cham: Springer. doi: 10.1007/978-3-031-48885-6_5.
    • Herber, Paula; Wijs, Anton (Eds.): . 18th International Conference on integrated Formal Methods, {iFM} 2023. Leiden: Springer. doi: 10.1007/978-3-031-47705-8.
    • Adelt Julius; Gebker Julian; Herber Paula. . ‘Reusable formal models for concurrency and communication in custom real-time operating systems.’ International Journal on Software Tools for Technology Transfer 26, No. 2: 229–245. doi: 10.1007/S10009-024-00743-4.
    • Bodden, E; Felderer, M; Hasselbring, W; Herber, P; Koziolek, H; Lilienthal, C; Matthes, F; Prechelt, L; Rumpe, B; Schaefer, I (Eds.): . Ernst Denert Software Engineering Award 2022. Cham, Switzerland: Springer Nature. doi: 10.1007/978-3-031-44412-8_1.
    • Bender, Magnus; Braun, Tanya; Möller, Ralf; Gehrke, Marcel. . ‘Unsupervised Estimation of Subjective Content Descriptions in an Information System.’ International Journal of Semantic Computing 1. doi: 10.1142/S1793351X24410034.
    • Bender, Magnus; Braun, Tanya; Möller, Ralf; Gehrke, Marcel. . ‘ReFrESH – Relation-preserving Feedback-reliant Enhancement of Subjective Content Descriptions.’ In ICSC-24 Proceedings of the 18th IEEE International Conference on Semantic Computing. New York: Wiley-IEEE Computer Society Press. [accepted / in Press (not yet published)]
    • Luttermann, Malte; Braun, Tanya; Möller, Ralf; Gehrke, Marcel. . ‘Colour Passing Revisited: Lifted Model Construction with Commutative Factors.’ In AAAI-24 Proceedings of the 38th AAAI Conference on Artificial Intelligence.: AAAI Press. [accepted / in Press (not yet published)]
    • Hartwig, Mattis; Möller, Ralf; Braun, Tanya. . ‘An Extended View on Lifting Gaussian Bayesian Networks.’ Artificial Intelligence . [accepted / in Press (not yet published)]
    • Luttermann, Malte; Hartwig, Mattis; Braun, Tanya; Möller, Ralf; Gehrke, Marcel. . ‘Lifted Causal Inference in Relational Domains.’ In CLeaR-24 Proceedings of the 3rd Conference on Causal Learning and Reasoning.: MLResearchPress. [accepted / in Press (not yet published)]

    • Adelt J.; Bruch S.; Herber P.; Niehage M.; Remke A. . ‘Shielded Learning for Resilience and Performance Based on Statistical Model Checking in Simulink.’ In Bridging the Gap Between AI and Reality - First International Conference, AISoLA 2023, Crete, Greece, October 23–28, 2023, Proceedings, edited by Steffen, Bernhard, 94–118. Cham: Springer. doi: 10.1007/978-3-031-46002-9_6.
    • Borrelli, Gabriel; Hagemann, Lars; Steinkühler, Jannik;Derstroff, Adrian;Evers, Marina;Huesmann, Karim;Leistikow, Simon;Rave, Hennes;Gol, Reyhaneh Sabbagh;Linsen, Lars. . ‘2022 IEEE Scientific Visualization Contest Winner: Multifield Analysis of Vorticity-Driven Lateral Spread in Wildfire Ensembles.’ IEEE Computer Graphics and Applications 44, No. 1: 40–49. doi: 10.1109/MCG.2023.3310298.
    • Delicaris, Joanna; Schupp, Stefan; Ábrahám, Erika; Remke, Anne. . ‘Maximizing Reachability Probabilities in Rectangular Automata with Random Clocks.’ In Theoretical Aspects of Software Engineering, edited by David, Cristina; Sun, Meng, 164–182. Cham: Springer. doi: 10.1007/978-3-031-35257-7_10.
    • Schilling, M.; Cruse, H. . ‘neuroWalknet, a controller for hexapod walking allowing for context dependent behavior.’ PLoS Computational Biology 19, No. 1: e1010136. doi: 10.1371/journal.pcbi.1010136.
    • Schilling, M.; Hammer, B.; Ohl, F.W.; Ritter, H.; Wiskott, L. . ‘Modularity in Nervous Systems—a Key to Efficient Adaptivity for Deep Reinforcement Learning.’ Cognitive Computation in press. doi: 10.1007/s12559-022-10080-w.
    • Rasch, Ari; Schulze, Richard; Shabalin, Denys; Elster, Anne; Gorlatch, Sergei; Hall, Mary. . ‘(De/Re)-Compositions Expressed Systematically via MDH-Based Schedules.’ In CC 2023: Proceedings of the 32nd ACM SIGPLAN International Conference on Compiler Construction, edited by Verbrugge, Clark, 61–72. New York: ACM Press. doi: 10.1145/3578360.3580269.
    • Garanina, N; Gorlatch, S. . ‘Autotuning Parallel Programs by Model Checking.’ Automatic Control and Computer Sciences 56, No. 7: 634–648. doi: 10.3103/S0146411622070045.
    • Hagedorn, Bastian; Lenfers, Johannes; Koehler, Thomas; Qin, Xueying; Gorlatch, Sergei; Steuwer, Michel. . ‘Achieving High Performance the Functional Way: Expressing High-Performance Optimizations as Rewrite Strategies.’ Communications of the ACM 66, No. 3: 89–97. doi: 10.1145/3580371.
    • Jarrous-Holtrup, S.; Gorlatch, S.; Dey, M.; Schamel, F. . ‘Multi-Cloud Container Orchestration for High-Performance Real-Time Online Applications.’ In Euromicro Conference on Parallel, Distributed and Network-Based Processing, edited by Montella, R.; Ciaramella, A.; Lapegna, M.; Danelutto, M.; Blanco Heras, D., 307–313. Neapel: Wiley-IEEE Computer Society Press. doi: 10.1109/PDP59025.2023.00054.
    • Jarrous-Holtrup, S.; Gorlatch, S.; Dey, M.; Schamel, M. . ‘An OpenVPN-Based Interconnection in Multi-Clouds with Windows and Linux nodes.’ In Consumer Communications and Networking Conference, CCNC IEEE, edited by Chowdhury, K., 867–870. Las Vegas: Wiley-IEEE Computer Society Press. doi: 10.1109/CCNC51644.2023.10059709.
    • Dewi C, Chen RC, Yu H, Jiang X. . ‘Robust detection method for improving small traffic sign recognition based on spatial pyramid pooling.’ Journal of Ambient Intelligence and Humanized Computing 14, No. 7: 8135–8152.
    • Nienkötter A, Jiang X. . ‘Kernel-based generalized median computation for consensus learning.’ IEEE Transactions on Pattern Analysis and Machine Intelligence 45, No. 5: 5872–5888.
    • Hegselmann S, Buendia A, Lang H, Agrawal M, Jiang X, Sontag D. . ‘TabLLM: Few-shot classification of tabular data with large language models.’ In Proceedings of the 26th International Conference on Artificial Intelligence and Statistics (AISTATS), edited by N.A., 5549–5581. 206th Ed. 2023: MLResearchPress.
    • Tistarelli M, Dubey SR, Singh SK, Jiang X (Eds.): . Computer Vision and Machine Intelligence. Cham, Switzerland: Springer Nature.
    • Eilers F, Jiang X. . ‘Building blocks for a complex-valued transformer architecture.’ In Proc. of ICASSP, edited by N/A, 1–5. N/A: Wiley-IEEE Press.
    • Xiao J, Tian Y, Jia Y, Jiang X, Yu L, Wang S. . ‘Black-box attack-based security evaluation framework for credit card fraud detection models.’ INFORMS Journal on Computing 35, No. 5: 986–1001.
    • Sandmann S, Richter S, Jiang X, Varghese J. . ‘Reconstructing clonal evolution - a systematic evaluation of current bioinformatics approaches.’ International journal of environmental research and public health 20: 5128.
    • Dewi C, Chen RC, Zhuang YC, Jiang X, Yu H. . ‘Recognizing road surface traffic signs based on Yolo models considering image flips.’ Big Data and Cognitive Computing 7: 54.
    • Zhang J, Liu CL, Jiang X. . ‘Quadratic kernel learning for interpolation kernel machine based graph classification.’ In Proc. of Int. Workshop on Graph-Based Representations in Pattern Recognition (GbR), edited by M. Vento, P. Foggia, D. Conte, V. Carletti, 3–14. Cham, Switzerland: Springer.
    • Zhang J, Liu CL, Jiang X. . ‘Interpolation kernel machines: Reducing multiclass to binary.’ In Proc. of Int. Conf. on Computer Analysis of Images and Patterns (CAIP), edited by N. Tsapatsoulis, et al., 174–184. Cham, Switzerland: Springer.
    • Jiang X, Nienkötter A. . ‘Generalized median computation for consensus learning: A brief survey .’ In Proc. of Int. Conf. on Computer Analysis of Images and Patterns (CAIP), edited by N, Tsapatsoulis, et al., 120–130. Cham, Switzerland: Springer.
    • Kuhlmann F, Rothaus K, Jiang X, Faatz H, Pauleikhoff D, Gutfleisch M. . ‘3D retinal vessel segmentation in OCTA volumes: Annotated dataset MORE3D and hybrid U-net with flattening transformation.’ In Proc. of DAGM GCPR, edited by U. Köthe and C. Rother, 291–306. Heidelberg: Springer.
    • Dewi C, Chen RC, Yu H, Jiang X. . ‘XAI for image captioning using SHAP .’ Journal of Information Science and Engineering 39: 711–724.
    • Becker, Marlon; Drees, Dominik; Brückerhoff-Plückelmann, Frank; Schuck, Carsten; Pernice, Wolfram; Risse, Benjamin. ‘Activation Functions in Non-Negative Neural Networks.’ contributed to the Machine Learning and the Physical Sciences Workshop, NeurIPS, New Orleans, .
    • Schaefer, J.; Strob, J. . „Wenn das Studieren außer Kontrolle gerät - Entwicklung und Validierung einer deutschsprachigen Adaption der Bergen Study Addiction Scale (BStAS).“ Zeitschrift fur Klinische Psychologie und Psychotherapie 2023. doi: 10.1026/1616-3443/a000684.
    • Steinhorst, Phil; Petersen, Andrew; Simion, Bogdan; Vahrenhold, Jan. . ‘Exploring Barriers in Productive Failure.’ In Proceedings of the 19th ACM Conference on International Computing Education Research (ICER 2023), Vol. I, edited by Fisler, Kathi; Denny, Paul; Franklin, Diana; Hamilton, Margaret, 284–297. New York, NY: ACM Press. doi: 10.1145/3568813.3600111.
    • Sanders, Kate; Vahrenhold, Jan; McCartney, Robert. . ‘How Do Computing Education Researchers Talk About Threats and Limitations?’ In Proceedings of the 19th ACM Conference on International Computing Education Research (ICER 2023), Vol. I, edited by Fisler, Kathi; Denny, Paul; Franklin, Diana; Hamilton, Margaret, 381. New York, NY: ACM Press. doi: 10.1145/3568813.3600114.
    • Parker, Miranda C.; Davidson, Matt J.; Kao, Yvonne S.; Margulieux, Lauren E.; Tidler, Zachary R.; Vahrenhold, Jan. . ‘Toward CS1 Content Subscales: A Mixed-Methods Analysis of an Introductory Computing Assessment.’ In Proceedings of the 23rd Koli Calling International Conference on Computing Education Research (Koli Calling 2023), edited by Mühling, Andreas; Jormanainen, Ilkka, 1–13. New York, NY: ACM Press. doi: 10.1145/3631802.3631828.
    • Cutts, Quintin; Kallia, Maria; Anderson, Ruth; Crick, Tom; Devlin, Marie; Farghally, Mohammed; Mirolo, Claudio; Runde, Ragnhild Kobro; Seppälä, Otto; Urquiza-Fuentes, Jaime; Vahrenhold, Jan. . ‘Arguments for and Approaches to Computing Education in Undergraduate Computer Science Programmes.’ In Proceedings of the 2023 Working Group Reports on Innovation and Technology in Computer Science Education (ITiCSE-WGR 2023), edited by Alshaigy, Bedour; Bouvier, Dennis, 160–195. New York, NY: ACM Press. doi: 10.1145/3623762.3633494.
    • da Silva, Carina; Schupp, Stefan; Remke, Anne. . ‘Optimizing Reachability Probabilities for a Restricted Class of Stochastic Hybrid Automata via Flowpipe-Construction.’ ACM Transactions on Modeling and Computer Simulation 33, No. 4: 1–27. doi: https://doi.org/10.1145/3607197.

Older research reports of the Institute for Computer Science are part of the research reports of the WWU. So look for the report "Department of Mathematics and Computer Science" in the following documents and then for "Institute for Computer Science" to find the appropriate part: