PhD Student @ LMU Munich Munich Center for Machine Learning (MCML) Research in Interpretable ML / Explainable AIhttps://bsky.app/profile/fionaewald.bsky.socialhttps://bsky.app/profile/fionaewald.bsky.social/post/3mfcvjsn3rk2uGreat news 🚀 https://www.linkedin.com/posts/tailor-made-ai_ki-artificialintelligence-maesnchen-activity-7430646327111344128-R_FL?utm_source=share20 Feb 2026 20:00 +0000at://did:plc:fpd2eikstrlqjyo4matfcasv/app.bsky.feed.post/3mfcvjsn3rk2uhttps://bsky.app/profile/fionaewald.bsky.social/post/3ltfbbwmolk22Great experience presenting my work in progress on #RashomonSets for #interpretability and #performance analysis at MCML Munich AI Day last week! A fantastic chance to connect, learn, and share ideas - big thanks to the @munichcenterml.bsky.social for organizing. #AI #MachineLearning #IML #xAI07 Jul 2025 17:02 +0000at://did:plc:fpd2eikstrlqjyo4matfcasv/app.bsky.feed.post/3ltfbbwmolk22https://bsky.app/profile/fionaewald.bsky.social/post/3lpwpsdvet22dThank you so much @daiichisankyo.bsky.social for welcoming me in your office in Munich! [contains quote post or other embedded content]24 May 2025 17:56 +0000at://did:plc:fpd2eikstrlqjyo4matfcasv/app.bsky.feed.post/3lpwpsdvet22dhttps://bsky.app/profile/fionaewald.bsky.social/post/3llgvxz65uk2eThe @munichcenterml.bsky.social recently shared our work: https://mcml.ai/news/2025-03-27-research-insight-ewald/ 🙏🏻 #mcml #ai #interpretability #iml #xAI #blockpost28 Mar 2025 13:24 +0000at://did:plc:fpd2eikstrlqjyo4matfcasv/app.bsky.feed.post/3llgvxz65uk2ehttps://bsky.app/profile/fionaewald.bsky.social/post/3lca7j2eqzk24Need an implementation of a conditional sampler, as required, e.g., in Conditional Feature Importance, for a project in R. Since I don't think it's efficient for everyone to implement their own, I'll ask around: Do you know a good implementation?01 Dec 2024 08:39 +0000at://did:plc:fpd2eikstrlqjyo4matfcasv/app.bsky.feed.post/3lca7j2eqzk24https://bsky.app/profile/fionaewald.bsky.social/post/3lbotk5iqws2qExcited to be part of this platform! My research focuses on global model-agnostic feature importance. Please check our recent paper: "A Guide to Feature Importance Methods for Scientific Inference" (https://link.springer.com/chapter/10.1007/978-3-031-63797-1_22).24 Nov 2024 10:49 +0000at://did:plc:fpd2eikstrlqjyo4matfcasv/app.bsky.feed.post/3lbotk5iqws2q