Stanford University
Showing 1-100 of 135 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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Emilin Maria Mathew
Undergraduate, Computer Science
BioScientist-Activist interested in psychiatric research, mental healthcare equity, and biotech.
Portfolio: https://emilinmathew.su.domains/portfolio/index.html -
David Mazieres
Professor of Computer Science
BioMazieres investigates ways to improve the security of operating systems, file systems, and distributed systems. In addition, he has worked on large-scale peer-to-peer systems and e-mail privacy.
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Jay McClelland
Lucie Stern Professor in the Social Sciences, Professor of Psychology and, by courtesy, of Linguistics and of Computer Science
On Leave from 04/01/2024 To 06/30/2024Current Research and Scholarly InterestsMy research addresses topics in perception and decision making; learning and memory; language and reading; semantic cognition; and cognitive development. I view cognition as emerging from distributed processing activity of neural populations, with learning occurring through the adaptation of connections among neurons. A new focus of research in the laboratory is mathematical cognition and reasoning in humans and contemporary AI systems based on neural networks.
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Brando Miranda
Ph.D. Student in Computer Science, admitted Autumn 2022
BioBio
Brando Miranda is a current Ph.D. Student at Stanford University under the supervision of Professor Sanmi Koyejo in the department of Computer Science. Previously he has been a graduate student at University of Illinois Urbana-Champaign, Research Assistant at MIT’s Center for Brain Minds and Machines (CBMM), and graduate student at the Massachusetts Institute of Technology (MIT). Miranda’s research interests lie in the field of meta-learning, foundation models for theorem proving, and human & brain inspired Artificial Intelligence (AI). Miranda completed his Master of Engineering in Electrical Engineering and Computer Science under the supervision of Professor Tomaso Poggio – where he did research on Deep Learning Theory. Miranda has been the recipient of several awards, including Most Cited Paper Certificate awarded by International Journal of Automation & Computing (IJAC), two Honorable Mention with the Ford Foundation Fellowship, Computer Science Excellence Saburo Muroga Endowed Fellow, Stanford School of Engineering fellowship, and is currently an EDGE Scholar at Stanford University.
About me (Informal)
I am a scientist and an engineer that is interested in moving forward the powerful and beautiful field of A.I. closer to true Artificial General Intelligence (AGI). I believe an important direction is understanding how to combine cognitive and neuro-inspired models, specially investigating how reasoning and learning work together. In addition, I also believe being able to adapt to new tasks using prior experience and knowledge is crucial for AGI to occur. Consequently, I decided to pursue a Ph.D in AI and machine learning. I currently work on meta-learning and machine learning (ML) for Theorem Proving (TP) at Stanford University. -
Azalia Mirhoseini
Assistant Professor of Computer Science
BioAzalia Mirhoseini is an Assistant Professor in the Computer Science Department at Stanford University. Professor Mirhoseini's research interest is in developing capable, reliable, and efficient AI systems for solving high-impact, real-world problems. Her work includes generalized learning-based methods for decision-making problems in systems and chip design, self-improving AI models through interactions with the world, and scalable deep learning optimization. Prior to Stanford, she spent several years in industry AI labs, including Anthropic and Google Brain. At Anthropic, she worked on advancing the capabilities and reliability of large language models. At Google Brain, she co-founded the ML for Systems team, with a focus on automating and optimizing computer systems and chip design. She received her BSc degree in Electrical Engineering from Sharif University of Technology and her PhD in Electrical and Computer Engineering from Rice University. Her work has been recognized through the MIT Technology Review’s 35 Under 35 Award, the Best ECE Thesis Award at Rice University, publications in flagship venues such as Nature, and coverage by various media outlets, including MIT Technology Review, IEEE Spectrum, The Verge, The Times, ZDNet, VentureBeat, and WIRED.