Understandable Machine Intelligence Lab

📍 Now @anna_hedstroem is a Postdoctoral Fellow at the @ETH_AI_Center, working with the @ivia_lab and Learning & Adaptive Systems (LAS) group. Anna's focus ahead: evaluation-centric interpretability, LLM steering, and AI safety. ✨🧠💻☕️ More info: annahedstroem.com.

Congrats @anna_hedstroem on an outstanding PhD journey! 🎓🧠💻☕️ Full list of works here: 👉 scholar.google.com/citations?user… Website: annahedstroem.com

🔊 Not to miss …. last month @anna_hedstroem defended her PhD “Evaluation-centric advances in neural model interpretability” at TU Berlin — with distinction! ✨🧠💻☕️ Here’s a thread of a selection of Anna’s evaluation-centric interpretability work + what comes next. 🧵

Happy to share that our PRISM paper has been accepted at #NeurIPS2025 🎉 In this work, we introduce a multi-concept feature description framework that can identify and score polysemantic features. 📄 Paper: arxiv.org/abs/2506.15538 #NeurIPS #MechInterp #XAI

📚 During his PhD, Kirill co-authored 11 papers spanning interpretability, neuron analysis & robust explanations. You can find all of them on his Google Scholar: 👉 scholar.google.com/citations?view… Once again, congrats @kirill_bykov on an outstanding PhD journey! 🎓✨

🧐 DORA: Exploring Outlier Representations in Deep Neural Networks (TMLR 2023)
A framework for analyzing & detecting learned representations in neural networks. 👉 arxiv.org/abs/2206.04530

🎉 Huge congratulations to @kirill_bykov, the very first PhD student of our lab, who successfully defended his thesis “Explaining Representations in Deep Neural Networks” this Monday with summa cum laude! 👏 🧵 In the next tweets, we’ll highlight some of his key works:

Our latest paper is out! 🚀

If you're at #AAAI2025 don't miss our poster today (alignment track)! Paper 📘: arxiv.org/pdf/2502.15403 Code 👩‍💻: github.com/annahedstroem/… Team work with @eirasf and @Marina_MCV