School of Engineering
Showing 101-110 of 502 Results
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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 HAI
BioChristopher Manning is the inaugural Thomas M. Siebel Professor of 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). His research goal is computers that can intelligently process, understand, and generate human languages. Manning was an early leader in applying Deep Learning to Natural Language Processing (NLP), with well-known research on the GloVe model of word vectors, attention, machine translation, question answering, self-supervised model pre-training, tree-recursive neural networks, machine reasoning, dependency parsing, sentiment analysis, and summarization. He also focuses on computational linguistic approaches to parsing, natural language inference and multilingual language processing, including being a principal developer of Stanford Dependencies and Universal Dependencies. Manning has coauthored leading textbooks on statistical approaches to NLP (Manning and Schütze 1999) and information retrieval (Manning, Raghavan, and Schütze, 2008), as well as linguistic monographs on ergativity and complex predicates. His online CS224N Natural Language Processing with Deep Learning videos have been watched by hundreds of thousands of people. He is an ACM Fellow, a AAAI Fellow, and an ACL Fellow, and a Past President of the ACL (2015). His research has won ACL, Coling, EMNLP, and CHI Best Paper Awards, and an ACL Test of Time Award. He has a B.A. (Hons) from The Australian National University and a Ph.D. from Stanford in 1994, and an Honorary Doctorate from U. Amsterdam in 2023, and he held faculty positions at Carnegie Mellon University and the University of Sydney before returning to Stanford. He is the founder of the Stanford NLP group (@stanfordnlp) and manages development of the Stanford CoreNLP and Stanza software.
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Andrew J. Mannix
Assistant Professor of Materials Science and Engineering
Current Research and Scholarly InterestsAtomically thin 2D materials incorporated into van der Waals heterostructures are a promising platform to deterministically engineer quantum materials with atomically resolved thickness and abrupt interfaces across macroscopic length scales while retaining excellent material properties. Because 2D materials exhibit a wide range of electronic characteristics with properties that often rival conventional electronic materials — e.g., metals, semiconductors, insulators, and superconductors — it is possible to combine them in virtually infinite variety to achieve diverse heterostructures. Furthermore, the van der Waals interface enables interlayer twist engineering to modify the interlayer symmetry, periodic potential (moiré superlattice), and hybridization, which has resulted in novel quantum states of matter. Many of these heterostructures, especially those involving specific interlayer twist angles, would be otherwise infeasible through direct growth.
The Mannix Group is developing a unique set of in-house capabilities to systematically elucidate the fundamental structure-property relationships underpinning the growth of 2D materials and their inclusion into van der Waals heterostructures. Greater understanding will allow us to provide a platform for engineering the properties of matter at the atomic scale and offer guidance for emerging applications in novel electronics and in quantum information science.
To accomplish this, we employ: precise growth techniques such as chemical vapor deposition and molecular beam epitaxy; automated van der Waals assembly; and atomically-resolved microscopy including cryo-STM/AFM.
