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

    • 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
    • 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
    • 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

    • Krause, Maurice; Greefrath, Gilbert; Forthmann, Boris; Kremer, Fabienne E.; Reer, Felix; Laumann, Daniel; Masemann, Dörthe; Denz, Cornelia; Heinicke, Susanne; Leibrock, Barbara; Marohn, Annette; Quandt, Thorsten; Souvignier, Elmar; Ubben, Malte; Heusler, Stefan. . ‘Effects of student-owned and provided mobile devices on mathematical modeling competence: investigating interaction effects with problematic smartphone use and fear of missing out.’ Frontiers in Education 9: 1167114. doi: 10.3389/feduc.2024.1167114.
    • Kockwelp, Jacqueline; Thiele, Sebastian; Bartsch, Jannis; Haalck, Lars; Gromoll, Jörg; Schlatt, Stefan; Exeler, Rita; Bleckmann, Annalen; Lenz, Georg; Wolf, Sebastian; Steffen, Björn; Berdel, Wolfgang Eduard; Schliemann, Christoph; Risse, Benjamin; Angenendt, Linus. . ‘Deep learning predicts therapy-relevant genetics in acute myeloid leukemia from Pappenheim-stained bone marrow smears.’ Blood Advances 8, No. 1: 70–79. doi: 10.1182/bloodadvances.2023011076.
    • 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)]
    • Gebauer, Eike; Thiele, Sebastian; Ouvrard, Pierre; Sicard, Adrien; Risse, Benjamin. . ‘Towards a Dynamic Vision Sensor-based Insect Camera Trap.’ Contributed to the Winter Conference on Applications of Computer Vision 2024, Waikoloa, Hawaii. [accepted / in Press (not yet published)]
    • Haalck, Lars; Risse, Benjamin. . ‘Solving the Plane-Sphere Ambiguity in Top-Down Structure-from-Motion.’ Contributed to the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, Hawaii. [accepted / in Press (not yet published)]
    • Young S; Schiffer C; Wagner A; Patz J; Potapenko A; Herrmann L; Nordhoff V; Pock T; Krallmann C; Stallmeyer B; Röpke A; Kierzek M; Biagioni C; Wang T; Haalck L; Deuster D; Hansen JN; Wachten D; Risse B; Behre HM; Schlatt S; Kliesch S; Tüttelmann F; Brenker C; Strünker T. . ‘Human fertilization in vivo and in vitro requires the CatSper channel to initiate sperm hyperactivation.’ Journal of Clinical Investigation 134, No. 1. doi: 10.1172/JCI173564.
    • Schulte L; Butz M; Becker M; Risse B; Schuck C. . ‘Accelerating Finite-Difference Frequency-Domain Simulations for Inverse Design Problems in Nanophotonics using Deep Learning .’ Journal of the Optical Society of America B . doi: 10.1364/JOSAB.506159. [accepted / in Press (not yet published)]
    • 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.
    • 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. New York, NY: Association for Computing Machinery. doi: 10.1145/3626252.3630915. [accepted / in Press (not yet published)]
    • 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 . [accepted / in Press (not yet published)]
    • 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.
    • Winter NR; Blanke J; Leenings R; Ernsting J; Fisch L; Sarink K; Barkhau C; Emden D; Thiel K; Flinkenflügel K; Winter A; Goltermann J; Meinert S; Dohm K; Repple J; Gruber M; Leehr EJ; Opel N; Grotegerd D; Redlich R; Nitsch R; Bauer J; Heindel W; Gross J; Risse B; Andlauer TFM; Forstner AJ; Nöthen MM; Rietschel M; Hofmann SG; Pfarr JK; Teutenberg L; Usemann P; Thomas-Odenthal F; Wroblewski A; Brosch K; Stein F; Jansen A; Jamalabadi H; Alexander N; Straube B; Nenadic I; Kircher T; Dannlowski U; Hahn T. . ‘A Systematic Evaluation of Machine Learning--Based Biomarkers for Major Depressive Disorder.’ JAMA Psychiatry . doi: 10.1001/jamapsychiatry.2023.5083. [accepted / in Press (not yet published)]
    • 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)]

    • Barrie, Robert; Haalck, Lars; Risse, Benjamin; Nowotny, Thomas; Graham, Paul; Buehlman, Cornelia. . ‘Trail using ants follow idiosyncratic routes in complex landscapes.’ Learning and Behavior s13420-023-00615. doi: https://doi.org/10.3758/s13420-023-00615-y.
    • Hätscher Ole , Junga Anna , Schulze Henriette , Kockwelp Pascal , Risse Benjamin , Back D. Mitja , Marschall Bernhard.Zusammenhang von Persönlichkeitsvariablen und Leistung in der virtuellen medizinischen Ausbildung.’ contributed to the Jahrestagung der Gesellschaft für medizinische Ausbildung (GMA) 2023, Osnabrück, . doi: 10.3205/23GMA273.
    • Engelbertz, Christiane; Feld, Jannik; Makowski, Lena; Lange, Stefan A.; Guenster, Christian; Droege, Patrik; Ruhnke, Thomas; Gerss, Joachim; Reinecke, Holger; Koeppe, Jeanette. . ‘Contemporary secondary prevention in survivors of ST-elevation myocardial infarction with and without chronic kidney disease: a retrospective analysis.’ CKJ: Clinical Kidney Journal 16, No. 11. doi: 10.1093/ckj/sfad219.
    • Angenent, Holger; Müller, Daniel; Vogl, Raimund. . ‘Bringing HPC clusters into Science Mesh.’ In Proceedings of European University Information Systems Congress 2023, edited by Desnos, Jean-François; López Nores, Martín, 249–256. online: EasyChair. doi: 10.29007/gfjd.
    • Makowski, L; Engelbertz, C; Köppe, J; Dröge, P; Ruhnke, T; Günster, C; Gerß, J; Freisinger, E; Malyar, N; Reinecke, H; Feld, J. . ‘Contemporary Treatment and Outcome of Patients with Ischaemic Lower Limb Amputation: A Focus on Sex Differences.’ European Journal of Vascular and Endovascular Surgery 66, No. 4: 550–559. doi: 10.1016/j.ejvs.2023.06.018.
    • Schüftan, Erik;.Chromatoid body architect TDRD6 - impact on spermatogenesis and male infertility.’ contributed to the European Testis Workshop, Montreux, .
    • Plagwitz L; Vogelsang T; Doldi F; Bickmann L; Fujarski M; Eckardt L; Varghese J. . ‘The Necessity of Multiple Data Sources for ECG-Based Machine Learning Models.Studies in Health Technology and Informatics 302: 33–37. doi: 10.3233/SHTI230059.
    • Fujarski M; Porschen C; Plagwitz L; Stroth D; Van Alen CM; Sadjadi M; Weiss R; Zarbock A; Von Groote T; Varghese J. . ‘DeepTSE: A Time-Sensitive Deep Embedding of ICU Data for Patient Modeling and Missing Data Imputation.Studies in Health Technology and Informatics 302: 237–241. doi: 10.3233/SHTI230110.
    • Kuehnemund, L; Lange, SA; Feld, J; Padberg, JS; Fischer, AJ; Makowski, L; Engelbertz, C; Dröge, P; Ruhnke, T; Guenster, C; Gerß, J; Freisinger, E; Reinecke, H; Koeppe, J. . ‘Sex disparities in guideline-recommended therapies and outcomes after ST-elevation myocardial infarction in a contemporary nationwide cohort of patients over an eight-year period.’ Atherosclerosis 375: 30–37. doi: 10.1016/j.atherosclerosis.2023.05.007.
    • Mergen, M; Junga, Anna, Risse, Benjamin; Valkov, Dimitar; Graf, Norbert; Marschall, Bernhard; Medical Tr.AI.Ning Consortium. . „Immersive training of clinical decision making with AI driven virtual patients – a new VR platform called medical tr.AI.ning.“ GMS Journal for Medical Education 40, No. 2. doi: 10.3205/ZMA001600.
    • 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.
    • Brix T.J.; Berentzen M.; Becker L.; Storck M.; Varghese J. . ‘Development of a Command Line Interface for the Analysis of Result Sets from Automated Queries to Literature Databases.’ Studies in Health Technology and Informatics 302: 162–166. doi: 10.3233/SHTI230095.
    • Ventura, David; Roll, Wolfgang; Kasper, Hans-Udo; Rahbar, Kambiz; Stegger, Lars. . ‘177Lu-DOTATATE (Lutathera) Therapy in 68Ga-DOTATATE PET/CT-Negative Liver Metastases of a Neuroendocrine Tumor.’ Clinical Nuclear Medicine 48, No. 12. doi: 10.1097/RLU.0000000000004888.
    • Krause, Maurice; Greefrath, Gilbert. . „Zum Interesse an digitalen Aufgaben: Geschlechtsspezifische Unterschiede zwischen Schülerinnen und Schülern.“ In Beiträge zum Mathematikunterricht 2022. 56. Jahrestagung der Gesellschaft für Didaktik der Mathematik, herausgegeben von IDMI-Primar Goethe-Universität Frankfurt, 953–956. Münster: Verlag für wissenschaftliche Texte und Medien. doi: 10.17877/DE290R-23696.
    • 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: Association for Computing Machinery. doi: 10.1145/3578360.3580269.
    • Engelbertz, C; Feld, J; Makowski, L; Kühnemund, L; Fischer, AJ; Lange, SA; Günster, C; Dröge, P; Ruhnke, T; Gerß, J; Freisinger, E; Reinecke, H; Köppe, J. . ‘Contemporary in-hospital and long-term prognosis of patients with acute ST-elevation myocardial infarction depending on renal function: a retrospective analysis.BMC Cardiovascular Disorders 23, No. 1: 62. doi: 10.1186/s12872-023-03084-3.
    • Beuker, C; Köppe, J; Feld, J; Meyer, CL; Dröge, P; Ruhnke, T; Günster, C; Wiendl, H; Reinecke, H; Minnerup, J. . ‘Association of age with 1-year outcome in patients with acute ischaemic stroke treated with thrombectomy: real-world analysis in 18 506 patients.’ Journal of Neurology, Neurosurgery and Psychiatry 94, No. 8: 631–637. doi: 10.1136/jnnp-2022-330506.
    • Alyaydin, E; Sindermann, JR; Köppe, J; Gerss, J; Dröge, P; Ruhnke, T; Günster, C; Reinecke, H; Feld, J. . ‘Depression and Anxiety in Heart Transplant Recipients: Prevalence and Impact on Post-Transplant Outcomes.Journal of personalized medicine 13, No. 5. doi: 10.3390/jpm13050844.
    • Lange SA, Schliemann C, Engelbertz C, Feld J, Makowski L, Gerß J, Dröge P, Ruhnke T, Günster C, Reinecke H, Köppe J. . ‘Survival of Patients with Acute Coronary Syndrome and Hematologic Malignancies-A Real-World Analysis.’ Cancers 15, No. 20. doi: 10.3390/cancers15204966.
    • Lange SA, Schliemann C, Makowski L, Engelbertz C, Feld J, Gerss J, Droege P, Ruhnke J, Guenster T, Reinecke H, Koeppe J. . ‘Acute and long-term outcomes of acute coronary syndrome in patients with hematological malignancies.’ European Heart Journal 44.
    • Makowski L, Feld J, Engelbertz C, Koeppe J, Kuehnemund L, Fischer A, Lange SA, Droege P, Ruhnke T, Guenster C, Malyar N, Gerss J, Freisinger E, Reinecke H. . ‘Sex Disparities in Treatment and Outcome of Patients with Lower Extremity Arterial Disease: A Secondary Data Analysis.’ Gesundheitswesen 85, No. Epub: S127–S134. doi: 10.1055/a-1916-9717.
    • 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.
    • Purk M.; Fujarski M.; Becker M.; Warnecke T.; Varghese J. . ‘Utilizing a tablet-based artificial intelligence system to assess movement disorders in a prospective study.’ Scientific Reports 13, No. 1. doi: 10.1038/s41598-023-37388-3.
    • Purk M.; Fujarski M.; Becker M.; Warnecke T.; Varghese J. . ‘Utilizing a tablet-based artificial intelligence system to assess movement disorders in a prospective study.’ Scientific Reports 13, No. 1. doi: 10.1038/s41598-023-37388-3.
    • Mense, Sophie; Höveler, Karina; Blohm, Pauline Anne; Willemsen, Lisa Constanze. . ‘Designing a tool for authoring digital problem-solving tasks in an app – an integrative learning design study.’ In Proceedings of the 13th Congress of the European Society for Research in Mathematics Education (CERME13). [accepted / in Press (not yet published)]
    • Adelt J; Liebrenz T; Herber P. . ‘Formal Verification of Intelligent Hybrid Systems that are modeled with Simulink and the Reinforcement Learning Toolbox.’ In Software Engineering, edited by Gregor Engels; Regina Hebig; Matthias Tichy, 29–30. Paderborn: Gesellschaft für Informatik.
    • Haalck, Lars; Thiele, Sebastian; Risse, Benjamin. . ‘Tracking Tiny Insects in Cluttered Natural Environments using Refinable Recurrent Neural Networks.’ Contributed to the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), Waikoloa, Hawaii. [accepted / in Press (not yet published)]
    • Haalck L; Mangan M; Wystrach A; Clement L; Webb B; Risse B. . ‘CATER: Combined Animal Tracking & Environment Reconstruction.’ Science advances 9, No. 16: eadg2094.
    • Pielage, Leon; Schmidle, Paul; Marschall, Bernhard; Risse, Benjamin.Diffusion Models in Dermatological Education: Flexible High Quality Image Generation for VR-based Clinical Simulations.’ contributed to the NeurIPS'23 Workshop: Generative AI for Education (GAIED), New Orleans, Louisiana, . [accepted / in Press (not yet published)]
    • Wendland D; Becker M; Brückerhoff-Plückelmann F; Bente I; Busch K; Risse B; Pernice WH. . ‘Coherent dimension reduction with integrated photonic circuits exploiting tailored disorder.’ Journal of the Optical Society of America B 40, No. 3: B35–B40.
    • Butz M; Leifhelm A; Becker M; Risse B; Schuck C. . ‘A Universal Approach to Nanophotonic Inverse Design through Reinforcement Learning.’ In CLEO 2023, paper STh4G.3, edited by Optica Publishing Group, STh4G.3. San Jose: Optica Publishing Group. doi: 10.1364/CLEO_SI.2023.STh4G.3.
    • Butz, Marco; Leifhelm, Alexander; Becker, Marlon; Risse, Benjamin; Schuck, Carsten. . ‘A Novel Approach to Nanophotonic Black-Box Optimization Through Reinforcement Learning.’ In Q 30 Nano-optics, edited by DPG, 1. Hannover: DPG Springmeeting 2023.
    • Becker, Marlon; Riegelmeyer, Jan; Seyfried, Maximilian; Ravoo, Bart-Jan; Schuck, Carsten; Risse, Benjamin. . ‘Adaptive Photo-Chemical Nonlinearities for Optical Neural Networks.’ Advanced Intelligent Systems ‎ 5, No. 12: 2300229. doi: 10.1002/aisy.202300229 .
    • Brückerhoff-Plückelmann, Frank; Bente, Ivonne; Becker, Marlon; Vollmar, Niklas; Farmakidis, Nikolaos; Lomonte, Emma; Lenzini, Francesco; Wright, C David; Bhaskaran, Harish; Salinga, Martin; Risse, Benjamin; Pernice, Wolfram HP. . ‘Event-driven adaptive optical neural network.’ Science advances 9, No. 42: eadi9127. doi: 10.1126/sciadv.adi9127.
    • 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, .
    • Valkov, Dimitar; Kockwelp, Pascal; Daiber, Florian; Krüger Antonio. . ‘Reach Prediction Using Finger Motion Dynamics.’ In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, edited by ACM, 1–8. New York, NY, USA: Association for Computing Machinery. doi: 10.1145/3544549.3585773.
    • Garanina, N; Gorlatch, S. . ‘Autotuning Parallel Programs by Model Checking.’ Automatic Control and Computer Sciences 56, No. 7: 634–648. doi: 10.3103/S0146411622070045.
    • 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.

    • Junga, Anna; Kockwelp, Pascal; Valkov, Dimitar; Marschall, Bernhard; Hartwig, Sophia; Stummer, Walter; Risse, Benjamin; Holling, Markus.Virtual Reality based teaching – a paradigm shift in education?’ contributed to the 73. Jahrestagung Deutsche Gesellschaft für Neurochirurgie, Köln, . doi: 10.3205/22DGNC538.

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: