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)]
- . . ‘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.
- . . ‘Interrelated effects of age and parenthood on whole-brain controllability: protective effects of parenthood in mothers.’ Frontiers in Aging Neuroscience 15. doi: 10.3389/fnagi.2023.1085153.
- . . „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.
- . . ‘Genetic, individual, and familial risk correlates of brain network controllability in major depressive disorder.’ Molecular Psychiatry 28, No. 3: 1057–1063. doi: 10.1038/s41380-022-01936-6.
- . . ‘Towards a network control theory of electroconvulsive therapy response.’ PNAS Nexus 2, No. 2. doi: 10.1093/pnasnexus/pgad032.
- . . ‘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: DPG Springmeeting 2023.
- . . ‘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.
- . ‘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.
- . ‘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.
- . . ‘Reach Prediction Using Finger Motion Dynamics.’ In Extended Abstracts of the 2023 CHI Conference on Human Factors in Computing Systems, edited by , 1–8. New York, NY, USA: ACM Press. doi: 10.1145/3544549.3585773.
- . ‘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.
- . . ‘Quantifying Deviations of Brain Structure and Function in Major Depressive Disorder Across Neuroimaging Modalities.’ JAMA Psychiatry 79, No. 9. doi: 10.1001/jamapsychiatry.2022.1780.
- . . ‘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: DPG Springmeeting 2022.
- . . ‘Hierarchical random walker segmentation for large volumetric biomedical images.’ IEEE Transactions on Image Processing 31: 4431–4446. doi: 10.1109/TIP.2022.3185551.
- . . ‘A Bhattacharyya coefficient-based framework for noise model-aware random walker image segmentation.’ In Proc. of GCPR, edited by , 166–181. Cham, Switzerland: Springer Nature.
- . . Efficient Out-of-Core Methods for Biomedical Volume Processing and Analysis Dissertation thesis, University of Münster. ULB Münster.
- . ‘Hopping-transport mechanism for reconfigurable logic in disordered dopant networks.’ Physical Review Applied 17, No. 6: 064025. doi: https://doi.org/10.1103/PhysRevApplied.17.064025.
- . . ‘Resolving colliding larvae by fitting ASM to random walker-based pre-segmentations.’ IEEE/ACM Transactions on Computational Biology and Bioinformatics 18, No. 3: 1184–1194.
- . . ‘Touch Recognition on Complex 3D Printed Surfaces using Filter Response Analysis.’ Contributed to the IEEE VR Workshop on Novel Input Devices and Interaction Techniques (NIDIT), Online. [online first]
- . . ‘The Impact of Activation Sparsity on Overfitting in Convolutional Neural Networks.’ In The Impact of Activation Sparsity on Overfitting in Convolutional Neural Networks.: Springer International Publishing.
- . . ‘Embedded Dense Camera Trajectories in Multi-Video Image Mosaics by Geodesic Interpolation-based Reintegration.’ Contributed to the Winter Conference on Applications of Computer Vision, Waikoloa, Hawaii.
- . . ‘Towards Visual Insect Camera Traps.’ Contributed to the International Conference on Pattern Recognition (ICPR) Workshop on Visual observation and analysis of Vertebrate And Insect Behavior (VAIB), Milan.
- . . ‘PHOTONAI-A Python API for rapid machine learning model development .’ PloS one 16. doi: 10.1371/journal.pone.0254062.
- . . ‘Scalable robust graph and feature extraction for arbitrary vessel networks in large volumetric datasets.’ BMC Bioinformatics 22, No. 1: 346. doi: 10.1186/s12859-021-04262-w.
- . . ‘Rapid methods for the evaluation of fluorescent reporters in tissue clearing and the segmentation of large vascular structures.’ iScience 24, No. 6: 102650. doi: 10.1016/j.isci.2021.102650.
- . . ‘From “Loose Fitting” to High-Performance, Uncertainty-Aware Brain-Age Modelling.’ Brain 144, No. 3: e31. doi: 10.1093/brain/awaa454.
- . . Exploiting the Full Capacity of Deep Neural Networks while Avoiding Overfitting by Targeted Sparsity Regularization. arXiv e-print:2002.09237: CoRR.
- . . ‘PHOTON--A Python API for Rapid Machine Learning Model Development.’ arXiv preprint arXiv:2002.05426 2020.
- . . ‘The Drosophila NCAM homolog Fas2 signals independently of adhesion.’ Development 147, No. 2. doi: 10.1242/dev.181479.
- . . ‘Towards image-based animal tracking in natural environments using a freely moving camera.’ Journal of Neuroscience Methods 330: 108455. doi: 10.1016/j.jneumeth.2019.108455.
- . . ‘From skylight input to behavioural output: a computational model of the insect polarised light compass.’ PLoS Computational Biology 15, No. 7: e1007123. doi: 10.1371/journal.pcbi.1007123.
- . . ‘dOCAL: high-level distributed programming with OpenCL and CUDA.’ The Journal of Supercomputing 65: 1–22. doi: 10.1007/s11227-019-02829-2.
- . . ‘GERoMe – a method for evaluating stability of graph extraction algorithms without ground truth.’ IEEE Access 7: 21744–21755. doi: 10.1109/ACCESS.2019.2898754.
- . . ‘appreci8: a pipeline for precise variant calling integrating 8 tools.’ Bioinformatics 2018/24/34. doi: 10.1093/bioinformatics/bty518.
- . . ‘Automatic non-invasive heartbeat quantification of Drosophila pupae.’ Computers in Biology and Medicine 93: 189–199.
- . . ‘The Sulfite Oxidase Shopper controls neuronal activity by regulating glutamate homeostasis in Drosophila ensheathing glia.’ Nature Communications 9, No. 1: 3514.
- . . A Multi-Purpose Worm Tracker Based on FIM bioRxiv.
- . ‘Possibilities, Constraints and Limitations of Image-based Animal Tracking in Natural Environments.’ contributed to the Measuring Behavior, Manchester, UK, . [online first]
- . . ‘Software to convert terrestrial LiDAR scans of natural environments into photorealistic meshes.’ Environmental Modelling and Software 99: 88–100. doi: 10.1016/j.envsoft.2017.09.018.
- . . ‘Deep distance transform to segment visually indistinguishable merged objects.’ Contributed to the Proc. of 40th German Conference on Pattern Recognition (GCPR), Stuttgart.
- . . Barista - a graphical tool for designing and training deep neural networks. arXiv e-print:1802.04626: CoRR.
- . . ‘Understanding Conformational Dynamics of Complex Lipid Mixtures Relevant to Biology.’ Journal of Membrane Biology 251, No. 5: 609–631.
- . ‘ODM Data Analysis-A tool for the automatic validation, monitoring and generation of generic descriptive statistics of patient data.’ PloS one 13. doi: 10.1371/journal.pone.0199242.
- . . ‘The Ol1mpiad: Concordance of behavioural faculties of stage 1 and stage 3 Drosophila larvae.’ Journal of Experimental Biology 220: 2452–2475. doi: 10.1242/jeb.156646.
- . . ‘FIMTrack: An open source tracking and locomotion analysis software for small animals.’ PLoS Computational Biology 13, No. 5: e1005530. doi: 10.1371/journal.pcbi.1005530.
- . . ‘A FIM-based long-term in-vial monitoring system for Drosophila larvae.’ IEEE Transactions on Biomedical Engineering 64, No. 8: 1862–1874.
- . . ‘Visual Tracking of Small Animals in Cluttered Natural Environments Using a Freely Moving Camera.’ International Conference on Computer Vision (ICCV), Workshop on Visual Wildlife Monitoring, Venice, Italy 2017: 2840–2849.
- . . ‘VIPAR, a quantitative approach to 3D-histopathology applied to lymphatic malformations.’ JCI Insight 2, No. 16: e93424.
- . ‘Interactive Exploration of Cosmological Dark-Matter Simulation Data.’ IEEE Computer Graphics and Applications 37, No. 2: 80–89.
- . . ‘GERoMe - A novel graph extraction robustness measure.’ Contributed to the Proc. of Int. Workshop on Graph-Based Representations in Pattern Recognition (GbR), Anacapri, Italy.
- . . ‘Interactions among Drosophila larvae before and during collision.’ Scientific Reports 11, No. 6: 31564. doi: 10.1038/srep31564.
- . ‘Tracking, Mapping and Reconstruction. Modelling the Visual Perception of Desert Ants.’ contributed to the Animal Movement International Symposium, Lund, Sweden, .
- . ‘Habitat3D: Recreating the History of Visual Experience of Individual Insects.’ contributed to the International Congress of Neuroethology, Montevideo, Uruguay, .
- . ‘HabiTracks: Visual Tracking of Insects in Their Natural Habitat.’ contributed to the International Congress of Neuroethology, Montevideo, Uruguay, .
- . . ‘FIM2c : A Multi-Colour, Multi-Purpose Imaging System to Manipulate and Analyse Animal Behaviour.’ IEEE Transactions on Biomedical Engineering 64: 1–1.
- . ‘Quantifying subtle locomotion phenotypes of Drosophila larvae using internal structures based on FIM images.’ Computers in Biology and Medicine 63, No. null: 269–276. doi: 10.1016/j.compbiomed.2014.08.026.
- . ‘Visualize the Universe: Interactive Exploration of Cosmological Dark Matter Simulation Data.’ Contributed to the IEEE Visualization Conference 2015 October 25-30, Chicago, Il, USA.
- . . ‘Imaging Modalities for Semi-Translucent Animals and Their Impact on Quantitative Analysis.’ In VAIB Workshop, ICPR, 1––4.
- . . ‘FIM imaging and FIMTrack: Two new tools allowing high-throughput and cost effective locomotion analysis.’ Journal of Visualized Experiments 94.
- . . ‘The Drosophila FHOD1-like formin Knittrig acts through Rok to promote stress fiber formation and directed macrophage migration during the cellular immune response.’ Development 141, No. 6: 1366–1380. doi: 10.1242/dev.101352.
- . . ‘Stereo and Motion Based 3D High Density Object Tracking.’ Image and Video Technology 2014: 136–148. doi: 10.1007/978-3-642-53842-1_12.
- . ‘FIM2C and the Analysis of Collision Behavior.’ contributed to the Flies, Worms and Robots: Combining Perspectives on Minibrains and Behavior Conference, Sant Feliu de Guixols, Spain, .
- . . ‘Biomedical Imaging: a Computer Vision Perspective.’ In Computer Analysis of Images and Patterns, edited by , 1–19. Berlin, Heidelberg: Springer VDI Verlag.
- . . ‘FIM: Frustrated Total Internal Reflection Based Imaging for Biomedical Applications.’ ERCIM News 95, No. Image Understanding: 11–12.
- . . ‘Drosophila pupal macrophages - A versatile tool for combined ex vivo and in vivo imaging of actin dynamics at high resolution.’ European Journal of Cell Biology 2013. doi: 10.1016/j.ejcb.2013.09.003.
- . . ‘FIM, a novel FTIR-based imaging method for high throughput locomotion analysis.’ PloS one 8, No. 1: e53963. doi: 10.1371/journal.pone.0053963.
- . . ‘Comparison of two 3D tracking paradigms for freely flying insects.’ EURASIP J Image Video Process 2013, No. 1: 57. doi: 10.1186/1687-5281-2013-57.
- . ‘Tracking of Colliding Larvae.’ contributed to the Conference on Neurobiology of Drosophila, New York, USA, .
- . . ‘Kinesin heavy chain function in Drosophila glial cells controls neuronal activity.’ Journal of Neuroscience 32, No. 22: 7466–7476. doi: 10.1523/JNEUROSCI.0349-12.2012.
- . . ‘3D Trajectory Estimation of Simulated Fruit Flies.’ In Proc 27th IVCNZ, Dunedin 2012.
- . ‘FIM: FTIR Based Image Acquisition and Tracking.’ contributed to the Conference on Behavioral Neurogenetics of Drosophila Larva (Maggot Meeting), Ashburn, USA, .
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, .
