H-Index & Metrics Best Publications

H-Index & Metrics

Discipline name H-index Citations Publications World Ranking National Ranking
Computer Science D-index 89 Citations 50,563 224 World Ranking 270 National Ranking 163

Research.com Recognitions

Awards & Achievements

2011 - ACM Fellow For contributions to reasoning and decision-making under uncertainty.

2001 - Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) For significant contributions to reasoning and learning under uncertainty.

Overview

What is he best known for?

The fields of study he is best known for:

  • Artificial intelligence
  • Statistics
  • Machine learning

David Heckerman mostly deals with Artificial intelligence, Machine learning, Bayesian network, Genetics and Immunology. His work carried out in the field of Artificial intelligence brings together such families of science as Speech recognition and Pattern recognition. David Heckerman has included themes like Data mining, Bayesian linear regression, Spambot, Variable-order Bayesian network and Email spam in his Machine learning study.

His Bayesian network research integrates issues from Equivalence, Modularity, Graphical model and Posterior probability, Bayesian probability. His Bayesian probability research is multidisciplinary, incorporating perspectives in Decision tree and Predictive analytics. His Human leukocyte antigen research incorporates elements of Virology, Viral load, Immune system, Epitope and CTL*.

His most cited work include:

  • Empirical analysis of predictive algorithms for collaborative filtering (4368 citations)
  • Learning Bayesian Networks: The Combination of Knowledge and Statistical Data (3169 citations)
  • A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD (2859 citations)

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

David Heckerman mainly focuses on Artificial intelligence, Machine learning, Bayesian network, Data mining and Virology. His Artificial intelligence study frequently draws parallels with other fields, such as Pattern recognition. As part of his studies on Machine learning, David Heckerman often connects relevant areas like Posterior probability.

His Bayesian network research is multidisciplinary, incorporating elements of Variable-order Bayesian network and Theoretical computer science. His research ties Collaborative filtering and Data mining together. David Heckerman combines subjects such as Epitope, Human leukocyte antigen, CTL* and Immunology with his study of Virology.

He most often published in these fields:

  • Artificial intelligence (30.51%)
  • Machine learning (17.91%)
  • Bayesian network (17.72%)

What were the highlights of his more recent work (between 2012-2021)?

  • Artificial intelligence (30.51%)
  • Bayesian network (17.72%)
  • Genetics (12.40%)

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

His scientific interests lie mostly in Artificial intelligence, Bayesian network, Genetics, Virology and Bayesian probability. His Artificial intelligence research includes themes of Machine learning, Contrast and Pattern recognition. His studies examine the connections between Machine learning and genetics, as well as such issues in Debiasing, with regards to Instrumental variable.

David Heckerman has researched Bayesian network in several fields, including Probabilistic logic, Theoretical computer science, Equivalence and Applied mathematics. His work deals with themes such as Epitope, Human leukocyte antigen and CTL*, which intersect with Virology. His Bayesian probability research includes elements of Software and Conditional probability distribution.

Between 2012 and 2021, his most popular works were:

  • Influence of HLA-C Expression Level on HIV Control (270 citations)
  • Selection bias at the heterosexual HIV-1 transmission bottleneck (179 citations)
  • Epigenome-wide association studies without the need for cell-type composition (176 citations)

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

  • Statistics
  • Artificial intelligence
  • Machine learning

His primary areas of study are Genetics, Virology, Epitope, Computational biology and Immune system. His work in Virology addresses issues such as Human leukocyte antigen, which are connected to fields such as CD8 and Mutation. His Computational biology study incorporates themes from Genetic association and Genomics.

His Immune system course of study focuses on HIV vaccine and Conserved sequence, Sequence and In silico. In his study, which falls under the umbrella issue of Data mining, Decision tree and Bayesian network is strongly linked to Collaborative filtering. His Softmax function study introduces a deeper knowledge of Artificial intelligence.

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

Empirical analysis of predictive algorithms for collaborative filtering

John S. Breese;David Heckerman;Carl Kadie.
uncertainty in artificial intelligence (1998)

6327 Citations

A Tutorial on Learning with Bayesian Networks.

David Heckerman.
Innovations in Bayesian Networks (2008)

4457 Citations

Learning Bayesian Networks: The Combination of Knowledge and Statistical Data

David Heckerman;Dan Geiger;David M. Chickering.
Machine Learning (1995)

4287 Citations

A hexanucleotide repeat expansion in C9ORF72 is the cause of chromosome 9p21-linked ALS-FTD

Alan E. Renton;Elisa Majounie;Adrian James Waite;Javier Simón-Sánchez;Javier Simón-Sánchez.
Neuron (2011)

3265 Citations

Inductive learning algorithms and representations for text categorization

Susan Dumais;John Platt;David Heckerman;Mehran Sahami.
conference on information and knowledge management (1998)

2243 Citations

A Bayesian Approach to Filtering Junk E-Mail

Mehran Sahami;Susan Dumais;David Heckerman;Eric Horvitz.
national conference on artificial intelligence (1998)

2238 Citations

Efficient Control of Population Structure in Model Organism Association Mapping

Hyun Min Kang;Noah A. Zaitlen;Claire M. Wade;Claire M. Wade;Andrew Kirby;Andrew Kirby.
Genetics (2008)

1452 Citations

CD8+ T-cell responses to different HIV proteins have discordant associations with viral load

Photini Kiepiela;Kholiswa Ngumbela;Christina Thobakgale;Dhanwanthie Ramduth.
Nature Medicine (2007)

1092 Citations

The lumière project: Bayesian user modeling for inferring the goals and needs of software users

Eric Horvitz;Jack Breese;David Heckerman;David Hovel.
uncertainty in artificial intelligence (1998)

1048 Citations

Bayesian Networks for Data Mining

David Heckerman.
Data Mining and Knowledge Discovery (1997)

1032 Citations

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