Computer Science
Showing 1,151-1,200 of 2,210 Results
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Anders Gjølbye Madsen
Graduate Visiting Researcher Student, Computer Science
BioAnders Gjølbye Madsen is a PhD fellow at the Technical University of Denmark. His research focuses on trustworthy machine learning for healthcare, with an emphasis on explainability, interpretability, and reliable evaluation of models in high-stakes settings. He works broadly with modern deep learning methods, including self-supervised learning, and is interested in questions of robustness and alignment. He is the author of PatternLocal, a NeurIPS 2025 paper on reducing false-positive attributions in explanations of non-linear models by refining local explanation approaches. He earned a BSc in Artificial Intelligence and Data from DTU and completed an MSc in Engineering in Applied Mathematics at DTU, including a study exchange in Computational Science and Engineering at ETH Zürich. Anders will spend 2026 as a visiting researcher at Stanford University’s Trustworthy AI Research (STAIR) Lab, working with Professor Sanmi Koyejo.
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Christopher Manning
Thomas M. Siebel Professor of Machine Learning, Professor of Linguistics, of Computer Science and Senior Fellow at the Stanford Institute for Human-Centered AI
On Leave from 10/01/2025 To 06/30/2026BioChristopher Manning is the inaugural Thomas M. Siebel Professor in Machine Learning in the Departments of Linguistics and Computer Science at Stanford University, Director of the Stanford Artificial Intelligence Laboratory (SAIL), and an Associate Director of the Stanford Institute for Human-Centered Artificial Intelligence (HAI). From 2010, Manning pioneered Natural Language Understanding and Inference using Deep Learning, with impactful research on sentiment analysis, paraphrase detection, the GloVe model of word vectors, attention, neural machine translation, question answering, self-supervised model pre-training, tree-recursive neural networks, machine reasoning, dependency parsing, and summarization, work for which he has received two ACL Test of Time Awards and the IEEE John von Neumann Medal (2024). He earlier led the development of empirical, probabilistic approaches to NLP, computational linguistics, and language understanding, defining and building theories and systems for Natural Language Inference, syntactic parsing, machine translation, and multilingual language processing, work for which he won ACL, Coling, EMNLP, and CHI Best Paper Awards. In NLP education, Manning coauthored foundational textbooks on statistical approaches to NLP (Manning and Schütze 1999) and information retrieval (Manning, Raghavan, and Schütze, 2008), and his online CS224N Natural Language Processing with Deep Learning course videos have been watched by hundreds of thousands. In linguistics, Manning is a principal developer of Stanford Dependencies and Universal Dependencies, and has authored monographs on ergativity and complex predicates. He is the founder of the Stanford NLP group (@stanfordnlp) and was an early proponent of open source software in NLP with Stanford CoreNLP and Stanza. He is an ACM Fellow, a AAAI Fellow, and an ACL Fellow, and a Past President of the ACL (2015). Manning has a B.A. (Hons) from The Australian National University, a Ph.D. from Stanford in 1994, and an Honorary Doctorate from U. Amsterdam in 2023. He held faculty positions at Carnegie Mellon University and the University of Sydney before returning to Stanford.
