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Miguel-Ángel Fernández-Torres

@miguelangelft.bsky.social
46 followers 99 following 8 posts

Assistant Professor at @EPS_UC3M @uc3m. PhD. in Multimedia and Communications. #ComputerVision #MachineLearning #ImageProcessing #RemoteSensing

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Miguel-Ángel Fernández-Torres @miguelangelft.bsky.social · 20/05/2026
🚀 The official schedule for the 𝟱𝘁𝗵 #𝗫𝗔𝗜𝟰𝗖𝗩 𝗪𝗼𝗿𝗸𝘀𝗵𝗼𝗽 𝗮𝘁 @cvprconference.bsky.social is here! 🗓️✨See you in Denver on June 3rd, 2026, 8:00 AM - 1:00 PM (MDT)! 🏔️👋 #CVPR2026 xai4cv-workshop.github.io/xai4cv2026/
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Miguel-Ángel Fernández-Torres @miguelangelft.bsky.social · 31/03/2026
The countdown is on!⏳We are in the last week for rolling submissions to the Non-Proceedings Track. If you are working on #XAI, we want to see your research at our 5th-anniversary #XAI4CV workshop at @cvprconference.bsky.social #CVPR2026 xai4cv-workshop.github.io/xai4cv2026/
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Miguel-Ángel Fernández-Torres @miguelangelft.bsky.social · 10/03/2026
Celebrating 5 years of the #XAI4CV Workshop at @cvprconference.bsky.social! The Non-Proceedings Track is open until April 7. Don't miss your chance to submit your latest research in #XAI—from interpretable-by-design to multimodal models and join us #CVPR2026 xai4cv-workshop.github.io/xai4cv2026/
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Miguel-Ángel Fernández-Torres @miguelangelft.bsky.social · 19/02/2026
The #XAI4CV workshop lineup has been revealed!🚀Celebrate 5 years at @cvprconference.bsky.social with researchers at the forefront of #XAI + a tutorial on concept-based interpretability in foundation models. Papers&demos across 2 tracks accepted! #CVPR2026 xai4cv-workshop.github.io/xai4cv2026/
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Miguel-Ángel Fernández-Torres @miguelangelft.bsky.social · 22/01/2026
We are thrilled to bring the #XAI4CV Workshop back to @cvprconference.bsky.social , celebrating its 5th Anniversary!🚀Submit your latest research in #XAI. We accept papers & demos across 2 tracks! #CVPR2026 xai4cv-workshop.github.io/xai4cv2026/ Please repost🔁
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Image and Signal Processing • ISP @isp-uv-es.bsky.social · 16/10/2025
🎓 We’re proud to announce that Jordi Cortés Andrés, one of our PhD students, just defended his thesis: "Regularization in Machine Learning: Advances for Earth Sciences". 🥳 Congratulations on reaching this important milestone in your research journey!
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ELLIS unit Jena @ellisunitjena.bsky.social · 02/09/2025
🌍 @ellis.eu #SummerSchool2025 in Jena on AI for Earth & Climate Sciences is underway 👩🏼‍🎓140 PhD students, 👨🏻‍💻13 keynote speakers & 🧑🏼‍🏫 23 tutors are diving into keynotes, hands-on sessions & collaborative projects—bridging #ML & geosciences 🌿🤖 www.ellis-jena.eu/summer-schoo...
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National Center for Disaster Preparedness, Columbia University @ncdp.bsky.social · 30/05/2025
✨ Despite #AI technological advances, persistent challenges persist in creating usable data for decision-makers in practice. Dr. Adams-Fuller, Dr. Glenn, & Dr. Fernández-Torres discuss challenges in weather preparedness. ncdp.columbia.edu/ncdp-perspec... @climate.columbia.edu @leapstc.bsky.social
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Image and Signal Processing • ISP @isp-uv-es.bsky.social · 23/06/2025
🗣️Yesterday we kicked off #LPS25 with"Advanced AI for Extreme Event Analysis: Hands-on with the AIDE Toolbox" (María González & @miguelangelft.bsky.social). 🌍 About this tool: 💾 github.com/IPL-UV/AIDE/ 📜 tr.ee/AIDEToolbox ℹ️ aidextremes.readthedocs.io/en/latest/
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Image and Signal Processing • ISP @isp-uv-es.bsky.social · 23/05/2025
🆕📰 New paper out that studies the #Calibration and the #Uncertainty of Deep Learning Models (DLM) for #Drought. ➡️ "Calibration and uncertainty quantification for deep learning-based drought detection" 🔗 Read it here: tr.ee/CalibrationAndUncertaintyQuantification
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LEAP-STC @leapstc.bsky.social · 18/04/2025
📣 IN ONE WEEK: Please join us for the next LEAP Wallerstein Panel Series on #AI + Extreme Weather Preparedness! ✨ "DATA + AI FOR DECISION-SUPPORT AND POLICY" ✨ 📆 Fri., 4/25/25 || 🕙 10a EST 📍 Virtual ℹ️ Info + RSVP: www.eventbrite.com/e/1286345206... #community #climateadaptation #climateresilience
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Sukrut Rao @sukrutrao.bsky.social · 02/03/2025
Submit your latest work (papers, demos) in #XAI to the 4th Explainable AI for Computer Vision (XAI4CV) Workshop at #CVPR2025! Details: xai4cv.github.io/workshop_cvp... Submission Site: cmt3.research.microsoft.com/XAI4CV2025 @cvprconference.bsky.social @xai-research.bsky.social
Call for papers for the XAI4CV Workshop at CVPR 2025. Submission deadline on March 10, 2025 for the Proceedings Track.
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Ivica Obadic @obadic.bsky.social · 27/11/2024
Curious about the recent trends, key challenges, and novel research directions in #explainableAI for #remotesensing? Then check our review paper "Opening the Black Box: A Systematic Review on Explainable Artificial Intelligence in Remote Sensing" (lnkd.in/et9dvHpZ) published at the IEEE #GRSM.
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Image and Signal Processing • ISP @isp-uv-es.bsky.social · 25/02/2025
🚨 New paper out in @naturecomms.bsky.social We review how #AI is transforming modeling & understanding extreme weather & climate events like floods, heatwaves, and wildfires🌍🔥💨 🔗Read it! isp-uv.short.gy/AIforExtreme...
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Nature Geoscience @natgeosci.nature.com · 05/02/2025
Comment: Explainability can foster trust in the use of AI in geoscience @jesper.drams.ch @ecmwf.bsky.social @aifraunhoferhhi.bsky.social www.nature.com/articles/s41...
nature.com
Explainability can foster trust in artificial intelligence in geoscience - Nature Geoscience
Uptake of explainable artificial intelligence (XAI) methods in geoscience is currently limited. We argue that such methods that reveal the decision processes of AI models can foster trust in their res...
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Image and Signal Processing • ISP @isp-uv-es.bsky.social · 29/01/2025
🌍 New paper: Generative networks for spatio-temporal #gap_filling of #Sentinel2 reflectances @esa.int. 🆓 Free access the first 50 days to the #ISPRS paper @elsevierconnect.bsky.social: isp-uv.short.gy/GapFillingSe... Work developed in the context of Xaida Project. #AI #EarthObservation
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Miguel D. Mahecha @miguelmahecha.bsky.social · 25/01/2025
🌍 Climate extremes, such as compound heatwaves and droughts, are increasing in both frequency and intensity, yet predicting their impacts on terrestrial ecosystems remains a challenge. Machine learning holds promise, but traditional datasets often struggle to represent these rare events effectively.
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