School of Engineering
Showing 3,241-3,260 of 5,907 Results
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Ali Mani
Professor of Mechanical Engineering
BioAli Mani is a professor of Mechanical Engineering at Stanford University. He is a faculty affiliate of the Institute for Computational and Mathematical Engineering at Stanford. He received his PhD in Mechanical Engineering from Stanford in 2009. Prior to joining the faculty in 2011, he was an engineering research associate at Stanford and a senior postdoctoral associate at Massachusetts Institute of Technology in the Department of Chemical Engineering. His research group builds and utilizes large-scale high-fidelity numerical simulations, as well as methods of applied mathematics, to develop quantitative understanding of transport processes that involve strong coupling with fluid flow and commonly involve turbulence or chaos. His teaching includes the undergraduate engineering math classes and graduate courses on fluid mechanics and numerical analysis.
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Ramesh Manian
Ph.D. Student in Management Science and Engineering, admitted Autumn 2024
Masters Student in Management Science and Engineering, admitted Summer 2022BioRamesh is a Principal Program Manager at Microsoft, where he works to help Fortune 500 companies with digital transformation of their modern work processes. He was a member of the founding team of TIBCO, a provider of integration and analytics solutions. He has founded several other startups in robotics, AI, and education. Ramesh is a life-long learner with diverse interests and currently interested on educating himself in biology and quantum computing, in addition to working toward his MS degree in MS&E.
He also ran Station Cafe, an Italian restaurant, in San Carlos between 2010 and 2014. -
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
BioChristopher 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.
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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. -
Louis Mao
Masters Student in Computational and Mathematical Engineering, admitted Autumn 2026
BioI focus on bringing complex AI systems and data architectures into real-world production—from LLM evaluation frameworks and real-time streaming pipelines to computer vision applications. Passionate about scalable machine learning, agentic workflows, and efficient deployment. Always excited to connect over big data, system design, or practical AI applications!
