Pablo Mesejo Santiago
35
Documents
Presentation
My name is Pablo Mesejo Santiago and I currently hold a Marie Curie Experienced Researcher position at the University of Granada (Spain), one of the [top institutions in computer science and engineering](http://www.shanghairanking.com/Shanghairanking-Subject-Rankings/computer-science-engineering.html).
My principal research areas of interest are computer vision, machine learning and computational intelligence methods applied to image analysis problems (mainly in the biomedical domain). Typical tools I use in my research are stochastic optimization algorithms, deep and shallow neural networks, and ensemble classifiers. During my career I have tackled numerous challenging problems, e.g. the automatic segmentation of anatomical structures in biomedical images (PhD at University of Parma, performed as a Marie Curie Early Stage Researcher, 2010-13), the classification of gastrointestinal lesions from endoscopic videos (postdoc at University of Auvergne Clermont-Ferrand I, 2013-14), the estimation of biophysical parameters from fMRI signals (postdoc at Inria, 2014-16), and the integration of deep learning into probabilistic generative models for visual and audio recognition in human-robot interaction (starting researcher position at Inria, 2016-18).
More information about me and my publications can be found in the following links: [Google Scholar](https://scholar.google.com/citations?user=dUlIWxcAAAAJ), [ORCID](http://orcid.org/0000-0001-9955-2101), [Linkedin](https://fr.linkedin.com/pub/pablo-mesejo-santiago/54/348/71b/en), [DBLP](http://dblp.uni-trier.de/pers/hd/m/Mesejo:Pablo), [ResearchGate](https://www.researchgate.net/profile/Pablo_Mesejo) and [ResearcherID](http://www.researcherid.com/ProfileView.action?SID=V24kpAPPEKmBvYjOeqa&returnCode=ROUTER.Success&queryString=KG0UuZjN5WkwsNoH4O%252BEmmn%252FPULU3%252FDZxELZtBub7fk%253D&SrcApp=CR&Init=Yes).
My name is Pablo Mesejo Santiago and I currently hold a Marie Curie Experienced Researcher position at the University of Granada (Spain), one of the [top institutions in computer science and engineering](http://www.shanghairanking.com/Shanghairanking-Subject-Rankings/computer-science-engineering.html).
My principal research areas of interest are computer vision, machine learning and computational intelligence methods applied to image analysis problems (mainly in the biomedical domain). Typical tools I use in my research are stochastic optimization algorithms, deep and shallow neural networks, and ensemble classifiers. During my career I have tackled numerous challenging problems, e.g. the automatic segmentation of anatomical structures in biomedical images (PhD at University of Parma, performed as a Marie Curie Early Stage Researcher, 2010-13), the classification of gastrointestinal lesions from endoscopic videos (postdoc at University of Auvergne Clermont-Ferrand I, 2013-14), the estimation of biophysical parameters from fMRI signals (postdoc at Inria, 2014-16), and the integration of deep learning into probabilistic generative models for visual and audio recognition in human-robot interaction (starting researcher position at Inria, 2016-18).
More information about me and my publications can be found in the following links: [Google Scholar](https://scholar.google.com/citations?user=dUlIWxcAAAAJ), [ORCID](http://orcid.org/0000-0001-9955-2101), [Linkedin](https://fr.linkedin.com/pub/pablo-mesejo-santiago/54/348/71b/en), [DBLP](http://dblp.uni-trier.de/pers/hd/m/Mesejo:Pablo), [ResearchGate](https://www.researchgate.net/profile/Pablo_Mesejo) and [ResearcherID](http://www.researcherid.com/ProfileView.action?SID=V24kpAPPEKmBvYjOeqa&returnCode=ROUTER.Success&queryString=KG0UuZjN5WkwsNoH4O%252BEmmn%252FPULU3%252FDZxELZtBub7fk%253D&SrcApp=CR&Init=Yes).
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Learning Visual Voice Activity Detection with an Automatically Annotated DatasetICPR 2020 - 25th International Conference on Pattern Recognition, Jan 2021, Milano, Italy. pp.4851-4856, ⟨10.1109/ICPR48806.2021.9412884⟩
Conference papers
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Understanding Priors in Bayesian Neural Networks at the Unit LevelICML 2019 - 36th International Conference on Machine Learning, Jun 2019, Long Beach, United States. pp.6458-6467
Conference papers
hal-02177151v1
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Extended Gaze Following: Detecting Objects in Videos Beyond the Camera Field of ViewFG 2019 - 14th IEEE International Conference on Automatic Face and Gesture Recognition, May 2019, Lille, France. pp.1-8, ⟨10.1109/FG.2019.8756555⟩
Conference papers
hal-02054236v1
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Bayesian neural networks become heavier-tailed with depthNeurIPS 2018 - Thirty-second Conference on Neural Information Processing Systems, Dec 2018, Montréal, Canada. pp.1-7
Conference papers
hal-01950658v1
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Bayesian neural network priors at the level of unitsAABI 2018 - 1st Symposium on Advances in Approximate Bayesian Inference, Dec 2018, Montréal, Canada. pp.1-6
Conference papers
hal-01950659v1
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DeepGUM: Learning Deep Robust Regression with a Gaussian-Uniform Mixture ModelECCV 2018 - European Conference on Computer Vision, Sep 2018, Munich, Germany. pp.205-221, ⟨10.1007/978-3-030-01228-1_13⟩
Conference papers
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Deep Reinforcement Learning for Audio-Visual Gaze ControlIROS 2018 - IEEE/RSJ International Conference on Intelligent Robots and Systems, Oct 2018, Madrid, Spain. pp.1555-1562, ⟨10.1109/IROS.2018.8594327⟩
Conference papers
hal-01851738v1
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Deep Mixture of Linear Inverse Regressions Applied to Head-Pose EstimationIEEE Conference on Computer Vision and Pattern Recognition, Jul 2017, Honolulu, Hawaii, United States. pp.7149-7157, ⟨10.1109/CVPR.2017.756⟩
Conference papers
hal-01504847v1
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Estimating Biophysical Parameters from BOLD Signals through Evolutionary-Based Optimization18th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI’15), Oct 2015, Munich, Germany. pp.528-535, ⟨10.1007/978-3-319-24571-3_63⟩
Conference papers
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An Analysis of Errors in Graph-Based Keypoint Matching and Proposed Solutions13th European Conference on Computer Vision (ECCV’14), Sep 2014, Zurich, Switzerland. pp.138-153, ⟨10.1007/978-3-319-10584-0_10⟩
Conference papers
hal-01221328v1
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Automatic evolutionary medical image segmentation using deformable models16th IEEE Congress on Evolutionary Computation (CEC’14), Jul 2014, Beijing, China. pp.97-104, ⟨10.1109/CEC.2014.6900466⟩
Conference papers
hal-01221343v1
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An experimental study on the automatic segmentation of in situ hybridization-derived images1st International Conference on Medical Imaging using Bio-Inspired and Soft Computing (MIBISOC’13), May 2013, Brussels, Belgium. pp.153-160
Conference papers
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GPU-Based Automatic Configuration of Differential Evolution: A Case Study16th Portuguese Conference on Artificial Intelligence, EPIA 2013, Sep 2013, Azores, Portugal. pp.114-125, ⟨10.1007/978-3-642-40669-0_11⟩
Conference papers
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Algorithm configuration using GPU-based metaheuristics15th Genetic and Evolutionary Computation Conference companion (GECCO’13), Jul 2013, Amsterdam, Netherlands. pp.221-222, ⟨10.1145/2464576.2464682⟩
Conference papers
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Segmentation of histological images using a metaheuristic-based level set approach15th Genetic and Evolutionary Computation Conference companion (GECCO’13), Jul 2013, Amsterdam, Netherlands. pp.1455-1462, ⟨10.1145/2464576.2466808⟩
Conference papers
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A Comparative Study of Three GPU-Based Metaheuristics12th International Conference on Parallel Problem Solving from Nature (PPSN’12), Sep 2012, Taormina, Italy. pp.398-407, ⟨10.1007/978-3-642-32964-7_40⟩
Conference papers
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Automatic segmentation of hippocampus in histological images of mouse brains using deformable models and random forest25th IEEE International Symposium on Computer-Based Medical Systems (CBMS’12), Jun 2012, Rome, Italy. pp.1-4, ⟨10.1109/CBMS.2012.6266318⟩
Conference papers
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Bayesian neural network priors at the level of unitsBayesian Statistics in the Big Data Era, Nov 2018, Marseille, France. pp.1
Conference poster
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Texture Classification of Proteins Using Support Vector Machines and Bio-inspired MetaheuristicsBiomedical Engineering Systems and Technologies, 452, pp.117-130, 2014, 978-3-662-44485-6. ⟨10.1007/978-3-662-44485-6_9⟩
Book sections
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Bayesian neural networks increasingly sparsify their units with depth2018
Preprints, Working Papers, ...
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AUTOMATIC SEGMENTATION OF ANATOMICAL STRUCTURES USING DEFORMABLE MODELS AND BIO-INSPIRED/SOFT COMPUTINGArtificial Intelligence [cs.AI]. University of Parma, 2014. English. ⟨NNT : ⟩
Theses
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