H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 164 Citations 201,905 612 World Ranking 5 National Ranking 3

Research.com Recognitions

Awards & Achievements

2020 - IEEE John von Neumann Medal “For contributions to machine learning and data science.”

2015 - David E. Rumelhart Prize for Contributions to the Theoretical Foundations of Human Cognition

2012 - SIAM Fellow For contributions to machine learning, in particular variational approaches to statistical inference.

2011 - Fellow of the American Academy of Arts and Sciences

2010 - ACM Fellow For contributions to the theory and application of machine learning.

2010 - Member of the National Academy of Engineering For contributions to the foundations and applications of machine learning.

2010 - Member of the National Academy of Sciences

2009 - ACM AAAI Allen Newell Award For fundamental advances in machine learning, particularly his groundbreaking work on graphical models and nonparametric Bayesian statistics, the broad application of this work across computer science, statistics, and the biological sciences.

2007 - Fellow of the American Statistical Association (ASA)

2006 - Fellow of the American Association for the Advancement of Science (AAAS)

2005 - IEEE Fellow For contributions to probabilistic graphical models and neural information processing systems.

2002 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to reasoning under uncertainty, machine learning, and human motor control.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

His primary areas of investigation include Artificial intelligence, Machine learning, Algorithm, Mathematical optimization and Artificial neural network. His Artificial intelligence research is multidisciplinary, incorporating elements of Data mining and Pattern recognition. His work carried out in the field of Data mining brings together such families of science as Probabilistic latent semantic analysis and Nonparametric statistics.

The study incorporates disciplines such as Training set and Bayesian probability in addition to Machine learning. His Algorithm research integrates issues from Approximate inference, Correlation clustering, Cluster analysis, Fuzzy clustering and Reproducing kernel Hilbert space. His studies deal with areas such as Estimator, Kernel method, Applied mathematics and Nonlinear system as well as Mathematical optimization.

His most cited work include:

  • Latent dirichlet allocation (24198 citations)
  • On Spectral Clustering: Analysis and an algorithm (6439 citations)
  • Adaptive mixtures of local experts (3263 citations)

What are the main themes of his work throughout his whole career to date?

Michael I. Jordan spends much of his time researching Artificial intelligence, Algorithm, Machine learning, Mathematical optimization and Applied mathematics. Michael I. Jordan has included themes like Data mining and Pattern recognition in his Artificial intelligence study. His research on Algorithm frequently connects to adjacent areas such as Cluster analysis.

His work on Machine learning is being expanded to include thematically relevant topics such as Set. Michael I. Jordan works in the field of Mathematical optimization, namely Minimax. His Inference research is multidisciplinary, incorporating perspectives in Graphical model and Markov chain Monte Carlo.

He most often published in these fields:

  • Artificial intelligence (29.15%)
  • Algorithm (17.23%)
  • Machine learning (16.62%)

What were the highlights of his more recent work (between 2018-2021)?

  • Algorithm (17.23%)
  • Applied mathematics (13.15%)
  • Artificial intelligence (29.15%)

In recent papers he was focusing on the following fields of study:

Michael I. Jordan focuses on Algorithm, Applied mathematics, Artificial intelligence, Mathematical optimization and Machine learning. The concepts of his Algorithm study are interwoven with issues in Entropy, Upper and lower bounds, False discovery rate and Mirror descent. Michael I. Jordan interconnects Discretization, Convergence, Distribution and Symplectic geometry in the investigation of issues within Applied mathematics.

His study in Artificial intelligence is interdisciplinary in nature, drawing from both Domain and Set. His research investigates the link between Mathematical optimization and topics such as Reinforcement learning that cross with problems in State, Function approximation, Markov decision process, Discrete mathematics and Polynomial. His Machine learning study frequently links to adjacent areas such as Adversarial system.

Between 2018 and 2021, his most popular works were:

  • Theoretically Principled Trade-off between Robustness and Accuracy (348 citations)
  • Theoretically Principled Trade-off between Robustness and Accuracy (204 citations)
  • Transferable Representation Learning with Deep Adaptation Networks (116 citations)

In his most recent research, the most cited papers focused on:

  • Artificial intelligence
  • Statistics
  • Machine learning

His primary areas of investigation include Artificial intelligence, Machine learning, Applied mathematics, Gradient descent and Algorithm. Artificial intelligence and Domain are frequently intertwined in his study. His Machine learning study combines topics in areas such as Adversarial system, Statistical inference, Normalization and Range.

His Applied mathematics research includes elements of Linear approximation, Acceleration, Infinitesimal, Automatic differentiation and Discretization. His Gradient descent study integrates concerns from other disciplines, such as Stochastic gradient descent, Convergence, Saddle point, Convex optimization and Stationary point. His Algorithm research is multidisciplinary, relying on both Mirror descent, Proximal point method, Multiclass classification, Minimax and Upper and lower bounds.

This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.

Best Publications

Latent dirichlet allocation

David M. Blei;Andrew Y. Ng;Michael I. Jordan.
Journal of Machine Learning Research (2003)

34253 Citations

On Spectral Clustering: Analysis and an algorithm

Andrew Y. Ng;Michael I. Jordan;Yair Weiss.
neural information processing systems (2001)

8999 Citations

Adaptive mixtures of local experts

Robert A. Jacobs;Michael I. Jordan;Steven J. Nowlan;Geoffrey E. Hinton.
Neural Computation (1991)

4393 Citations

Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes

Yee W. Teh;Michael I. Jordan;Matthew J. Beal;David M. Blei.
neural information processing systems (2004)

4363 Citations

Graphical Models, Exponential Families, and Variational Inference

Martin J. Wainwright;Michael I. Jordan.
(2008)

3902 Citations

Hierarchical mixtures of experts and the EM algorithm

Michael I. Jordan;Robert A. Jacobs.
Neural Computation (1994)

3579 Citations

An Internal Model for Sensorimotor Integration

Daniel M. Wolpert;Zoubin Ghahramani;Michael I. Jordan.
Science (1995)

3307 Citations

Distance Metric Learning with Application to Clustering with Side-Information

Eric P. Xing;Michael I. Jordan;Stuart J Russell;Andrew Y. Ng.
neural information processing systems (2002)

3303 Citations

Hierarchical Dirichlet Processes

Yee Whye Teh;Michael I. Jordan;Matthew J. Beal;David M. Blei.
Journal of the American Statistical Association (2006)

2968 Citations

An introduction to variational methods for graphical models

Michael I. Jordan;Zoubin Ghahramani;Tommi S. Jaakkola;Lawrence K. Saul.
Machine Learning (1999)

2959 Citations

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