World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
91
Citations
36759
World Ranking
579
National Ranking
308

Research.com Recognitions

  • 2007 - Fellow of Alfred P. Sloan Foundation
  • 2006 - Hellman Fellow
  • 2006 - ACM Grace Murray Hopper Award For the design of a system capable of learning a high-quality grammar for English directly from text.

Overview

Daniel Klein is affiliated with the University of California, Berkeley in the United States. Their research primarily spans the field of Computer Science, with a total of 149 publications documented. Within this domain, their work is notably concentrated in the subfields of Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems, Signal Processing, and Cognitive Neuroscience.

The scientist's research topics include Topic Modeling, Natural Language Processing Techniques, Multimodal Machine Learning Applications, Speech and Dialogue Systems, Text Readability and Simplification, Information and Cyber Security, and Speech Recognition and Synthesis.

Daniel Klein's frequent publication venues include:

  • arXiv (Cornell University)
  • Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • bioRxiv (Cold Spring Harbor Laboratory)

Their recent papers provide insight into the scope and impact of their research. Selected works include:

  • Multilingual Alignment of Contextual Word Representations, 2020, arXiv (Cornell University)
  • Constrained Language Models Yield Few-Shot Semantic Parsers, 2021, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Calibrate Before Use: Improving Few-Shot Performance of Language Models, 2021, arXiv (Cornell University)
  • Train Large, Then Compress: Rethinking Model Size for Efficient Training and Inference of Transformers, 2020, arXiv (Cornell University)
  • Discovering Latent Knowledge in Language Models Without Supervision, 2022, arXiv (Cornell University)

Collaborations have been a significant aspect of Daniel Klein's research activities. Frequent co-authors include Ruiqi Zhong, Gal Engelberg, Giancarlo Guizzardi, Eve Fleisig, and Kevin Yang, each with multiple joint publications.

Throughout their career, Daniel Klein has received several awards, including the ACM Grace Murray Hopper Award in 2006, which recognized the design of a system capable of learning a high-quality grammar for English directly from text. The same year, they were named a Hellman Fellow. Additionally, in 2007, they were named a Fellow of the Alfred P. Sloan Foundation.

Best Publications

  • Feature-rich part-of-speech tagging with a cyclic dependency network

    Kristina Toutanova;Dan Klein;Christopher D. Manning;Yoram Singer

  • Accurate Unlexicalized Parsing

    Dan Klein;Christopher D. Manning

  • Abstractions for software architecture and tools to support them

    M. Shaw;R. DeLine;D.V. Klein;T.L. Ross

  • Neural Module Networks

    Jacob Andreas;Marcus Rohrbach;Trevor Darrell;Dan Klein

  • Fast Exact Inference with a Factored Model for Natural Language Parsing

    Dan Klein;Christopher D Manning

  • Learning Accurate, Compact, and Interpretable Tree Annotation

    Slav Petrov;Leon Barrett;Romain Thibaux;Dan Klein

  • From Instance-level Constraints to Space-Level Constraints: Making the Most of Prior Knowledge in Data Clustering

    Dan Klein;Sepandar D. Kamvar;Christopher D. Manning

  • Improved Inference for Unlexicalized Parsing

    Slav Petrov;Dan Klein

  • Learning Dependency-Based Compositional Semantics

    Percy Liang;Michael Jordan;Dan Klein

  • Corpus-Based Induction of Syntactic Structure: Models of Dependency and Constituency

    Dan Klein;Christopher Manning

  • Constituency Parsing with a Self-Attentive Encoder

    Nikita Kitaev;Dan Klein

  • Learning to Compose Neural Networks for Question Answering

    Jacob Andreas;Marcus Rohrbach;Trevor Darrell;Dan Klein

  • Alignment by Agreement

    Percy Liang;Ben Taskar;Dan Klein

  • Foiling the cracker: A survey of, and improvements to, password security

    D.V. Klein

  • Spectral learning

    Sepandar D. Kamvar;Dan Klein;Christopher D. Manning

  • Abstract Syntax Networks for Code Generation and Semantic Parsing

    Maxim Rabinovich;Mitchell Stern;Dan Klein

  • Learning Bilingual Lexicons from Monolingual Corpora

    Aria Haghighi;Percy Liang;Taylor Berg-Kirkpatrick;Dan Klein

  • Speaker-Follower Models for Vision-and-Language Navigation

    Daniel Fried;Ronghang Hu;Volkan Cirik;Anna Rohrbach

  • An End-to-End Discriminative Approach to Machine Translation

    Percy Liang;Alexandre Bouchard-Côté;Dan Klein;Ben Taskar

  • Calibrate Before Use: Improving Few-shot Performance of Language Models

    Zihao Zhao;Eric Wallace;Shi Feng;Dan Klein

  • Learning Semantic Correspondences with Less Supervision

    Percy Liang;Michael Jordan;Dan Klein

  • Modular multitask reinforcement learning with policy sketches

    Jacob Andreas;Dan Klein;Sergey Levine

  • Calibrate Before Use: Improving Few-Shot Performance of Language Models

    Tony Z. Zhao;Eric Wallace;Shi Feng;Dan Klein

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