Publications
- . . ‘Proceedings of the OHBM Brainhack 2022.’ Aperture Neuro 2014//4. doi: 10.52294/001c.92760.
- . . ‘Accelerating Finite-Difference Frequency-Domain Simulations for Inverse Design Problems in Nanophotonics using Deep Learning .’ Journal of the Optical Society of America B 41, No. 4: 1039–1046. doi: 10.1364/JOSAB.506159.
- . . ‘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.
- . . ‘A Systematic Evaluation of Machine Learning–Based Biomarkers for Major Depressive Disorder.’ JAMA Psychiatry 2024/4/81. doi: 10.1001/jamapsychiatry.2023.5083.
- . . ‘SAM meets Gaze: Passive Eye Tracking for Prompt-based Instance Segmentation.’ Proceedings of Machine Learning Research . [accepted / in Press (not yet published)]
- . . ‘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)]
- . . ‘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)]
- . . ‘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.
- . . ‘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)]
- . . ‘Therapy-induced modulation of tumor vasculature and oxygenation in a murine glioblastoma model quantified by deep learning-based feature extraction.’ Scientific Reports 14, No. 1: 2034. doi: https://doi.org/10.1038/s41598-024-52268-0.
- . . ‘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.
- . ‘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.
- . . „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.
- . . ‘Inverse Design of Nanophotonic Devices using Dynamic Binarization.’ Optics Express 31, No. 10: 15747–15756. doi: 10.1364/OE.484484.
- . . ‘Coherent dimension reduction with integrated photonic circuits exploiting tailored disorder.’ Journal of the Optical Society of America B 40, No. 3: B35–B40.
- . . ‘CATER: Combined Animal Tracking & Environment Reconstruction.’ Science advances 9, No. 16: eadg2094.
- . . ‘An overview and a roadmap for artificial intelligence in hematology and oncology.’ Journal of Cancer Research and Clinical Oncology 15: 1–10.
- . . ‘A Universal Approach to Nanophotonic Inverse Design through Reinforcement Learning.’ In CLEO 2023, paper STh4G.3, edited by , STh4G.3. San Jose: Optica Publishing Group. doi: 10.1364/CLEO_SI.2023.STh4G.3.
- . . ‘A Novel Approach to Nanophotonic Black-Box Optimization Through Reinforcement Learning.’ In Q 30 Nano-optics, edited by , 1. Hannover: Deutsche Physikalische Gesellschaft.
- . . ‘EyeGuide - From Gaze Data to Instance Segmentation.’ Contributed to the The British Machine Vision Conference (BMVC), Aberdeen.
- . . ‘Adaptive Photo-Chemical Nonlinearities for Optical Neural Networks.’ Advanced Intelligent Systems 5, No. 12: 2300229. doi: 10.1002/aisy.202300229 .
- . . ‘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)]
- . . ‘Event-driven adaptive optical neural network.’ Science advances 9, No. 42: eadi9127. doi: 10.1126/sciadv.adi9127.
- . ‘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.
- . ‘Activation Functions in Non-Negative Neural Networks.’ contributed to the Machine Learning and the Physical Sciences Workshop, NeurIPS, New Orleans, .
- . ‘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, .
- . . Sustainable research software for high-quality computational research in the Earth System Sciences: Recommendations for universities, funders and the scientific community in Germany FIG GEO-LEO e-docs. doi: 10.23689/fidgeo-5805.
- . . ‘Adaptive Photochemical Nonlinearities for Optical Neural Networks.’ Advanced Intelligent Systems 5, No. 12. doi: 10.1002/aisy.202300229.
- . ‘Combinatorial Optimization via Memory Metropolis: Template Networks for Proposal Distributions in Simulated Annealing applied to Nanophotonic Inverse Design.’ contributed to the Neural Information Processing Systems (NeurIPS) Workshop on AI for Accelerated Materials Design (AI4Mat-2023), New Orleans, .
- . . ‘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.
- . ‘Virtual Reality based teaching – a paradigm shift in education?’ contributed to the 73. Jahrestagung Deutsche Gesellschaft für Neurochirurgie, Köln, . doi: 10.3205/22DGNC538.
- . . ‘Volumetric imaging reveals VEGF-C-dependent formation of hepatic lymph vessels in mice.’ Frontiers in cell and developmental biology 10: 949896. doi: 10.3389/fcell.2022.949896.
- . . ‘An Uncertainty-Aware, Shareable and Transparent Neural Network Architecture for Brain-Age Modeling.’ Science advances 8, No. 1: eabg9471. doi: 10.1126/sciadv.abg9471.
- . . ‘Perspectives in machine learning for wildlife conservation.’ Nature Communications 13, No. 1: 792–807. doi: 10.1038/s41467-022-27980-y.
- . . ‘Towards VR Simulation-Based Training in Brain Death Determination.’ In 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW), edited by , 287–292. 2022 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops (VRW): Wiley-IEEE Press.
- . . ‘Narrowing Attention in Capsule Networks.’ In 26th International Conference on Pattern Recognition, edited by , 2679–2685. 26th International Conference on Pattern Recognition (ICPR): Wiley-IEEE Press.
- . . ‘Cell Selection-based Data Reduction Pipeline for Whole Slide Image Analysis of Acute Myeloid Leukemia.’ In The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), edited by , 1825–1834. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition: Wiley-IEEE Press.
- . ‘Development of a nanophotonic nonlinear unit for optical artificial neural networks.’ contributed to the DPG Springmeeting 2022, Erlangen, .
- . . ‘Inverse Design of Nanophotonic Devices based on Reinforcement Learning.’ In Q 38 Photonics II, edited by , 2. Erlangen: Deutsche Physikalische Gesellschaft.
- . . ‘appreci8: a pipeline for precise variant calling integrating 8 tools.’ Bioinformatics 2018/24/34. doi: 10.1093/bioinformatics/bty518.
- . . A Multi-Purpose Worm Tracker Based on FIM bioRxiv.
Talks
- Ernsting, Jan (): ‘An uncertainty-aware, shareable, and transparent neural network architecture for brain-age modeling’. Virtual St. Andrews-Bonn meeting: Early career focus - focused on techniques rather than results, Online, .
- Risse, Benjamin (): „KI als anthropologische Herausforderung: Durchbrüche und Grenzen ak- tueller KI Entwicklungen“. Seminar in Humanities, University of Münster, Münster, Germany, .
- Risse, Benjamin (): ‘From the Lab to the Field - Novel Computer Vision and Machine Learning Approaches to Quantify the Behaviour of Animals Across Scales’. NC3 Symposium, University of Bielefeld, Bielefeld, Germany, .
- Risse, Benjamin; Hiltmann, Thorsten (): „How deep learning based image analysis and cultural historical heraldry benefit from each other“. Konference: Digital Humanities Deutschland (DHD2020), University of Paderborn, Paderborn, Germany, .
- Risse, Benjamin (): ‘The Benefits of Interdisciplinary Machine Learning & Computer Vision Research’. Center for Reproductive Medicine and Andrology, University Hospital Münster, Münster, Germany, .
- Risse, Benjamin (): „Machine Learning & Computer Vision in Interdisciplinary Research“. eScience Symposium, University of Münster, Münster, Germany, .
- Risse, Benjamin (): ‘Is Science Mostly Driven by Ideas or By Tools?’ Semiar: Institute of Physiological Chemistry and Pathobiochemistry, University of Münster, Münster, Germany, .
- Risse, Benjamin (): ‘Interdisciplinary Research at the Interface of Behavioural Biology and Com- puter Vision & Machine Learning’. Invited Talk MPI Bonn, MPI Bonn, Bonn, Germany, .
- Risse, Benjamin (): ‘"Artificial intelligence" - The quintessence of scientific narrow-mindedness?’ Lecture series on Digitisation, Privacy and AI, University of Münster, Münster, Germany, .
- Haalck, Lars; Risse, Benjamin (): ‘Quantifying the Behavioural Dynamics Behind the Sixth Mass Extinction of Insects – A Progress Report’. EnviroInfo 2019, Kassel, Deutschland, .
- Risse, Benjamin (): ‘Machine Learning: Yet Another Quantitative Research Tool’. CiM Symposium, University of Münster, Münster, Germany, .
- Risse, Benjamin (): ‘Visual Tracking of Tiny Insects Using a Freely Moving Camera While Reconstructing Their Environment’. Conference: Association for the Study of Animal Behaviour (ASAB), University of Konstanz, Konstanz, Germany, .
- Risse, Benjamin (): ‘Automatic Recognition of Wildlife Animals in Camera Trap Images’. Hoge Veluwe National Park Open Day, Hoge Veluwe National Park, Netherlands, .
- Risse, Benjamin (): ‘Machine Learning for Image Analysis’. GI at School, University of Münster, Münster, Germany, .
- Risse, Benjamin (): ‘Informatik Studieren?’ Berufsinformationstag BIBO 2019, Oelde, Germany, .
- Risse, Benjamin (): ‘Detecting and Tracking Animals in Complex Natural Environments’. Invitation of the BBC Manchester, BBC Manchester, Manchester, UK, .
- Risse, Benjamin (): ‘Tracking the Untrackable: Detecting Tiny Objects in Heavily Cluttered Environments’. Seminar in the University Hospital Münster , University Hospital Münster, Münster, Germany, .
- Risse, Benjamin (): ‘Tracking Tiny Objects While Reconstructing Their Natural Environment Using Hand-held Cameras and Drones’. Seminar at the Institute of Landscape Ecology, WWU Münster, Münster, Deutschland, .
- Risse, Benjamin (): ‘Machine Learning and Computer Vision - Tools & Technical Challenges’. BASF Scientific Meeting, Mannheim, Germany, .
- Risse, Benjamin (): ‘Machine Learning & Computer Vision for In-Vial Tracking’. Young Academy Retreat of the Cells in Motion Cluster of Excellence, WWU Münster, .
- Risse, Benjamin (): ‘Possibilities, Constraints and Limitations of Image-based Animal Tracking in Natural Environments’. Measuring Behavior Conference, Manchester, UK, .
- Risse, Benjamin (): ‘From Hand-held Cameras to Drones: Tracking Tiny Objects while Recon- (est.) structing the Environment’. Geoinformatik Münster, Münster, Germany, .
- Risse, Benjamin (): ‘Computer Vision in the Wild’. Laboratory of Geo-Information Science and Remote Sensing, Wageningen, Netherlands, .
- Risse, Benjamin (): ‘Computer Vision in the Wild’. Laboratory of Geo-Information Science and Remote Sensing, Wageningen, Niederlande, .
- Risse, Benjamin (): ‘Interdisciplinary Research at the Interface of Biology and Computer Vision / Pattern Recognition’. Institute for Evolution and Biodiversity, Münster, Germany, .
- Risse, Benjamin (): ‘Visual Tracking of Small Animals in Cluttered Natural Environments Using a Freely Moving Camera’. International Conference on Computer Visioin (ICCV), Venice, Italy, .
- Risse, Benjamin (): ‘Imaging and Tracking in Neurobiology: Acquiring Locomotion Trajectories of Small and Translucent Animals’. Cells in Motion: New Horizons in Experimental Medicine, Münster, Germany, .
- Risse, Benjamin (): ‘Multimodal and adaptive behaviour in insects and robots’. School of Computer Science, Lincoln, UK, .
- Risse, Benjamin (): ‘Imaging and Tracking of Semi-Translucent Animals like Worms or Larvae Using the FIM Multi-Purpose Setup ’. Centre of Integrative Physiology, Edinburgh, UK, .
- Risse, Benjamin (): ‘Is Science Mostly Driven by Tools or by Ideas?’ Center for Integrative Biology, Toulouse, France, .
- Risse, Benjamin (): ‘Insect Robotics’. Living Machines Workshop Satellite Presentation, Edinburgh, UK, .
- Risse, Benjamin (): ‘Imaging and Tracking in Neurobiology’. IPAB Workshop, Edinburgh, UK, .
- Risse, Benjamin (): ‘Acquiring Locomotion Trajectories of Drosophila melanogaster’. European Neuroscience Institute (ENI), Göttingen, Germany, .
- Risse, Benjamin (): ‘Tracking and Imaging in Neurobiology using FIM’. Leibniz Institute for Neurobiology (LIN), Magdeburg, Germany, .
- Risse, Benjamin (): ‘Imaging Modalities for Semi-Translucent Animals and Their Impact on Quantitative Analysis’. Visual Obervation and Analysis of Vertebrate and Insect Behavior Workshop, ICPR 2014, Stockholm, Sweden, .
- Risse, Benjamin (): ‘FIM: Acquiring Locomoton Trajectories of Drosophila melanogaster’. Drosophila larval development and locomotion meeting, Münster, Germany, .
- Risse, Benjamin (): ‘Imaging and Tracking in Neurobiology’. Imaging and Mathematics (Münster Cambridge Meeting), Münster, Germany, .
- Risse, Benjamin (): ‘Acquiring Locomotion Trajectories of Drosophila melanogaster’. Southwestern University of Finance and Economics, Sichuan, China, .
