{"id":1481,"date":"2024-10-10T14:11:22","date_gmt":"2024-10-10T12:11:22","guid":{"rendered":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/?page_id=1481"},"modified":"2024-11-07T13:02:27","modified_gmt":"2024-11-07T12:02:27","slug":"diip-projects","status":"publish","type":"page","link":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/projects\/diip-projects\/","title":{"rendered":"diiP Projects"},"content":{"rendered":"

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From 2021, the Data Intelligence Institute of Paris (diiP) selected 74 interdisciplinary projects<\/strong> using data science and machine learning.<\/p>\n

[\/et_pb_text][et_pb_search exclude_pages=”on” exclude_posts=”off” include_categories=”14,12,20″ placeholder=”Search by author, discipline, keyword, or project type” placeholder_color=”#3255c9″ _builder_version=”3.22.1″ text_orientation=”center” z_index_tablet=”500″][\/et_pb_search][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.22.1″][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/cosmologie-amas-de-galaxies-intelligence-artificielle\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Cosmologie \u2013 amas de galaxies \u2013 intelligence artificielle<\/h3>\n

Nicolas Cerardi \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ transform_translate=”-4px|-4px” custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/exploration-intelligente-de-lames-histologiques\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-7px|-7px”]<\/p>\n

Exploration intelligente de lames histologiques.<\/h3>\n

Zhuxian Guo \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ transform_translate=”-4px|-4px” custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/prediction-of-demographic-indicators-from-remote-sensing-images\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-7px|-7px”]<\/p>\n

Prediction of demographic indicators from remote sensing images<\/h3>\n

Basile Rousse \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/dark-energy-studies-with-the-vera-rubin-observatory-lsst-euclid-developing-a-combined-cosmic-shear-analysis-with-bayesian-neural-networks\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Dark energy studies with the Vera Rubin observatory LSST & Euclid-developing a combined cosmic shear analysis with bayesian neural networks<\/h3>\n

Justine Zeghal \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.22.1″][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/statistical-and-machine-learning-methods-for-survival-data-prediction-performance-assessment-and-interpretability\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Statistical and machine learning methods for survival data: prediction, performance assessment and interpretability<\/h3>\n

Ariane Cwiling \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/design-principles-of-property-graph-languages\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Design principles of property graph languages<\/h3>\n

Alexandra Rogova \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/leveraging-multivariate-geophysical-and-geochemical-time-series-for-monitoring-volcanic-systems-can-we-use-machine-learning\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Leveraging multivariate geophysical and geochemical time series for monitoring volcanic systems: can we use machine learning?<\/h3>\n

Matthieu Nougaret \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/learning-the-magneto-ionic-side-of-the-turbulence-in-the-interstellar-medium-in-radio-astronomy\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Learning the magneto-ionic side of the turbulence in the interstellar medium in radio-astronomy<\/h3>\n

Jack Berat \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.22.1″][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/metamorphoses-and-optimal-transport-for-the-multimodal-registration-of-brain-tumor-images\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Metamorphoses and optimal transport for the multimodal registration of brain tumor images<\/h3>\n

Guillaume Serieys \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/deeply-learning-from-neutrino-interactions-with-the-km3net-neutrino-telescope\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Deeply Learning from Neutrino Interactions with the KM3NeT neutrino telescope<\/h3>\n

Santiago Pena Martinez \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.22.1″][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/digital-pathology-when-ai-meets-with-anatomo-pathology\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Digital Pathology: when AI meets with anatomo-pathology<\/h3>\n

Nicolas Lom\u00e9nie \u2794<\/p>\n

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PARKER \u2014 Planetary lidAR seeKing for lifE signatuRe<\/h3>\n

Antoine Lucas \u2794<\/p>\n

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Autoimmunity\/inflammation Through RNAseq Analysis at the single Cell level for Therapeutic Innovation \u2013 ATRACTion<\/h3>\n

Micka\u00ebl M\u00e9nager \u2794<\/p>\n

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Inferring cultural transmission of reproductive success through machine learning methods<\/h3>\n

Fr\u00e9d\u00e9ric Austerlitz \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.22″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/large-image-time-series-analysis-for-updating-vineyard-geographic-databases\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Large image time series analysis for updating vineyard geographic databases<\/h3>\n

Camille Kurtz \u2794<\/p>\n

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Smoothing of incomplete air pollution regions of interest from satellite observations<\/h3>\n

Laurent Wendling \u2794<\/p>\n

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Combining visual and textual informations for enhancing image retrieval systems in radiological practices<\/h3>\n

Florence Cloppet \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/automatic-production-of-environmental-indicators-from-freely-available-remote-sensing-data-from-a-global-to-a-local-scale\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Automatic production of environmental indicators from freely available remote sensing data: from a global to a local scale<\/h3>\n

Sylvain Lobry \u2794<\/p>\n

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Digital Pathology: when AI meets with anatomo-pathology<\/h3>\n

Nicolas Lom\u00e9nie \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/machine-learning-model-of-volcanic-lava-properties-helps-understanding-the-dynamics-of-volcanic-eruptions\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Machine learning model of volcanic lava properties helps understanding the dynamics of volcanic eruptions<\/h3>\n

Charles Le Losq \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/complexneuroviz-complexity-visualisation-for-neural-machine-translation\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

ComplexNeuroViz: Complexity Visualisation for Neural Machine Translation<\/h3>\n

Nicolas Ballier \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/machine-learning-for-the-study-of-eeg-data-recorded-during-general-anesthesia\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Machine learning for the study of EEG data recorded during general anesthesia<\/h3>\n

Laurent Oudre \u2794<\/p>\n

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Influence of blood pressure and aqueous humor dynamics on the response to glaucoma medication: a data-driven computational study<\/h3>\n

Marcela Szopos \u2794<\/p>\n

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Machine Learning techniques applied to eye movement analysis for early screening of learning disorders in young children<\/h3>\n

Zo\u00ef Kapoula \u2794<\/p>\n

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Artificial Intelligence for source deblending in the next generation of astrophysical big data imaging surveys \u2013 Combining Euclid and LSST<\/h3>\n

Marc Huertas-Company \u2794<\/p>\n

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Malvasia \u2013 MAchine Learning to VAlue Single Interferometer Analysis<\/h3>\n

Agata Trovato \u2794<\/p>\n

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Study the ability of teenagers to spot fakes news over their usage time on the social networks<\/h3>\n

Salima Benbernou \u2794<\/p>\n

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Language-Independent Massive Network Attitudinal Embedding<\/h3>\n

Pedro Ramaciotti Morales \u2794<\/p>\n

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Learning from deep sea light with KM3NeT<\/h3>\n

Joao Coelho \u2794<\/p>\n

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Optimization of a physical force-field for simulations of non-coding RNA molecules<\/h3>\n

Samuela Pasquali \u2794<\/p>\n

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The diffusion of technology during the last five millennia<\/h3>\n

Johannes Boehm \u2794<\/p>\n

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Monitoring the seismic activity of Mayotte through image processing of fiber optic signals<\/h3>\n

Lise Retailleau \u2794<\/p>\n

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Automatic detection and location of hydro-acoustic signals linked to Mayotte submarine eruption<\/h3>\n

Jean-Marie Saurel \u2794<\/p>\n

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Investigating regulatory B cell differentiation and their therapeutic effect in neuroinflammatory disease through single cell analyses and computational biology<\/h3>\n

Simon Fillatreau \u2794<\/p>\n

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Transcriptomic Analysis using Intensive Randomization<\/h3>\n

Dorota Desaulle \u2794<\/p>\n

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Random projections for the reduction of gravitational wave template banks<\/h3>\n

Eric Chassande-Mottin \u2794<\/p>\n

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\u201cSearch for features in astrophysical objects close to cosmic neutrinos\u201d. An indirect approach to cosmic neutrino association with astrophysical objects<\/h3>\n

Yvonne Becherini \u2794<\/p>\n

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Multiple imputation for heterogeneous biological data<\/h3>\n

Matthieu Resche-Rigon\u2794<\/p>\n

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Tracking auto-immune diseases in electronic health record<\/h3>\n

Maud De Dieuleveult \u2794<\/p>\n

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Modeling genetic pleiotropy using machine learning to understand the human genetic architecture<\/h3>\n

Marie Verbanck \u2794<\/p>\n

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Veracity assessment framework for discovering social activities in urban big datasets<\/h3>\n

Soror Sahri \u2794<\/p>\n

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Combining visual and textual information for enhancing pathologic case retrieval systems in radiological practices<\/h3>\n

Florence Cloppet \u2794<\/p>\n

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Deep Learning-based EEG Epilepsy Detection and Analysis<\/h3>\n

Jerome Cartailler \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/multimodal-assessment-of-the-depth-of-sedation-of-severely-ill-patients-in-intensive-care-unit\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Multimodal assessment of the depth of sedation of severely ill patients in intensive care unit<\/h3>\n

Laurent Oudre \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/semantic-coherence-integration-for-optimizing-imaging-retrieval-systems-in-radiology\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Semantic coherence integration for optimizing imaging retrieval systems in radiology<\/h3>\n

Florence Cloppet<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/deep-learning-based-eeg-epilepsy-detection-and-analysis-2\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Deep Learning-based EEG Epilepsy Detection and Analysis<\/h3>\n

Qitong Wang \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row make_equal=”on” _builder_version=”3.22.1″ z_index_tablet=”500″][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/openstreetmap-and-sentinel-2-data-for-the-production-of-environmental-indices-for-demographic-studies\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

OpenStreetMap and Sentinel-2 data for the production of environmental indices for demographic studies<\/h3>\n

Sylvain Lobry \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/mining-molecular-dynamics-open-data\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Mining molecular dynamics open data<\/h3>\n

Pierre Poulain \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/prediction-of-protein-carbohydrate-binding-sites-using-deep-learning-methods\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Prediction of protein-carbohydrate binding sites using deep learning methods<\/h3>\n

Tatiana Galochkina \u2794<\/p>\n

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Enhancing earthquake location with domain adapation<\/h3>\n

Leonard Seydoux \u2794<\/p>\n

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Exploration of press articles related to Covid-19 at the European level within the Covid-19 Museum<\/h3>\n

Yves Rozenholc \u2794<\/p>\n

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Machine Learning for Photometric redshift estimation of LSST galaxies<\/h3>\n

Simona Mei \u2794<\/p>\n

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Deep learning to model genetic pleiotropy to understand the human genetic architecture<\/h3>\n

Marie Verbanck \u2794<\/p>\n

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Generalization of a method enabling to update vineyard geographic databases from satellite data<\/h3>\n

Camille Kurtz \u2794<\/p>\n

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Diffusion Models Based Unpaired Image-to-Image Translation to Reveal Subtle Phenotypes<\/h3>\n

Val\u00e9rie Mezger \u2794<\/p>\n

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Diffusion Models Based Visual Counterfactual Explanations<\/h3>\n

Valerie Mezger \u2794<\/p>\n

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EStimation of Pollutant Emissions from REmote sensing data and deep Learning (ESPEREL)<\/h3>\n

Ga\u00eblle Dufour \u2794<\/p>\n

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A Simulated body approach to MRI-based fetal monitoring<\/h3>\n

Jean-Baptiste Masson \u2794<\/p>\n

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Accelerate Discoveries (boosting) Astroparticle Physics (analysis) Techniques – ADAPT<\/h3>\n

Yvonne Becherini \u2794<\/p>\n

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Maintaining fairness for decision-making under social considerations<\/h3>\n

Soror Sahri \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/?p=1508&preview=true” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Quantifying the interaction between grammatical gender and social gender roles at a worldwide scale<\/h3>\n

Marc Allassonni\u00e8re-Tang \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/deep-learning-based-prediction-of-protein-carbohydrate-interactions\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Deep learning-based prediction of protein-carbohydrate interactions<\/h3>\n

Tatiana Galochkina \u2794<\/p>\n

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Automated segmentation and clustering of spICP-ToF-MS time series<\/h3>\n

Mickael Tharaud \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/dna-methylation-in-patients-a-new-meta-analysis-of-epic-data-across-borders\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

DNA methylation in patients: A new meta-analysis of EPIC data across borders<\/h3>\n

Maud De Dieuleveult \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/identification-of-image-circulation-by-ai-in-large-collections-of-historical-photographs\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Identification of image circulation by AI in large collections of historical photographs<\/h3>\n

Daniel Foliard \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/deep-learning-for-estimating-gas-concentration-maps-from-satellite-or-airborne-hyperspectral-images-2\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Deep learning for estimating gas concentration maps from satellite or airborne hyperspectral images<\/h3>\n

Andr\u00e9s Almansa \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row make_equal=”on” _builder_version=”3.22.1″ z_index_tablet=”500″][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/blind-image-deblurring-via-latent-diffusion-models\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Blind Image Deblurring via Latent Diffusion Models<\/h3>\n

Andr\u00e9s Almansa \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/investigating-diffusion-models-for-astronomical-image-deconvolution-boosting-the-synergy-between-euclid-and-lsst\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Investigating Diffusion Models for Astronomical Image Deconvolution – boosting the synergy between Euclid and LSST<\/h3>\n

Alexandre Boucaud \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/biomarker-prediction-from-images-of-histological-slides-of-cancerous-tissues-using-modern-ai-techniques-comparisons-of-different-neural-architectures\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Biomarker prediction from images of histological slides of cancerous tissues
using modern AI techniques: comparisons of different neural architectures<\/h3>\n

Nicolas Lom\u00e9nie \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/compact-representations-to-detect-gravitational-waves-from-extreme-mass-ratio-inspirals\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Compact representations to detect gravitational waves from extreme-mass ratio inspirals<\/span><\/h3>\n

Quentin Baghi \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row make_equal=”on” _builder_version=”3.22.1″ z_index_tablet=”500″][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/towards-design-of-protein-knottin-using-deep-learning\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Towards design of protein knottin using deep learning<\/h3>\n

Jean-Christophe Gelly \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/exploring-the-similarities-of-a-large-bank-of-protein-pockets-in-a-perspective-of-multiple-protein-ligand-interactions-prediction\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Exploring the similarities of a large bank of protein pockets in a perspective of
multiple protein-ligand interactions prediction<\/h3>\n

Anne-Claude Camproux \u2794<\/p>\n

[\/et_pb_text][\/et_pb_column][et_pb_column type=”1_4″ _builder_version=”3.0.47″][et_pb_text _builder_version=”3.22.1″ border_width_top=”8px” border_color_top=”#3255c9″ box_shadow_style=”preset3″ custom_margin=”100px|||” custom_padding=”25px|15px|15px|15px” z_index_tablet=”500″ link_option_url=”https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/deep-mendelian-randomization-explaining-causality-between-different-hereditary-traits-at-genome-wide-scale\/” link_option_url_new_window=”on” transform_styles__hover_enabled=”on” transform_scale__hover_enabled=”on” transform_translate__hover_enabled=”on” transform_rotate__hover_enabled=”on” transform_skew__hover_enabled=”on” transform_origin__hover_enabled=”on” transform_translate__hover=”-4px|-4px”]<\/p>\n

Deep Mendelian Randomization: explaining causality between different hereditary traits at genome-wide scale<\/h3>\n

Marie Verbanck \u2794<\/p>\n

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Modeling conditioned place preference test for evaluation of addictiveness of substances: toward experimental design optimization & smart data analysis<\/h3>\n

Emmanuel Curis \u2794<\/p>\n

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From 2021, the Data Intelligence Institute of Paris (diiP) selected 74 interdisciplinary projects using data science and machine learning. Cosmologie \u2013 amas de galaxies \u2013 intelligence artificielle Nicolas Cerardi \u2794Exploration intelligente de lames histologiques. Zhuxian Guo \u2794Prediction of demographic indicators from remote sensing images Basile Rousse \u2794Dark energy studies with the Vera Rubin observatory LSST… Lire la suite diiP Projects<\/span><\/a><\/p>\n","protected":false},"author":560,"featured_media":0,"parent":1677,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-1481","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/pages\/1481","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/users\/560"}],"replies":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/comments?post=1481"}],"version-history":[{"count":50,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/pages\/1481\/revisions"}],"predecessor-version":[{"id":3249,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/pages\/1481\/revisions\/3249"}],"up":[{"embeddable":true,"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/pages\/1677"}],"wp:attachment":[{"href":"https:\/\/wordpress-test.app.u-pariscite.fr\/diip\/wp-json\/wp\/v2\/media?parent=1481"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}