World's Best Scientists 2026 revealed!

D-Index & Metrics

Biology and Biochemistry

D-Index
91
Citations
30500
World Ranking
2317
National Ranking
1240

Research.com Recognitions

  • 1995 - Fellow of the American Statistical Association (ASA)

Overview

Daniel Gianola is affiliated with the University of Wisconsin-Madison in the United States. Their research focuses primarily on the fields of engineering and materials science, with notable attention to subfields such as materials chemistry, mechanical engineering, biomedical engineering, aerospace engineering, and electrical and electronic engineering.

Their work spans several main topics, including:

  • Microstructure and mechanical properties
  • Aluminum alloys composites properties
  • Aluminum alloy microstructure properties
  • Metallic glasses and amorphous alloys
  • Nanoparticles nucleation surface interactions
  • Ion-surface interactions and analysis
  • High entropy alloys studies

Daniel Gianola has published frequently in a variety of scientific venues. The most common journals and platforms include:

  • arXiv (Cornell University)
  • Acta Materialia
  • Materials & Design
  • Ultramicroscopy
  • Scripta Materialia

Among their recent publications are:

  • "Multiplicity of dislocation pathways in a refractory multiprincipal element alloy," published in Science, 2020
  • "Temperature-dependent tensile behavior of the HfNbTaTiZr multi-principal element alloy," published in Acta Materialia, 2022
  • "Heterogeneous slip localization in an additively manufactured 316L stainless steel," published in International Journal of Plasticity, 2022
  • "Disordered interfaces enable high temperature thermal stability and strength in a nanocrystalline aluminum alloy," published in Acta Materialia, 2021
  • "Bulk nanocrystalline Al alloys with hierarchical reinforcement structures via grain boundary segregation and complexion formation," published in Acta Materialia, 2021

The scientist has also collaborated extensively with several frequent co-authors, including:

  • Tresa M. Pollock
  • Timothy J. Rupert
  • Jungho Shin
  • Nicolò Maria della Ventura
  • Matthew R. Begley

In recognition of their contributions, Daniel Gianola was named a Fellow of the American Statistical Association (ASA) in 1995.

Best Publications

  • Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics

    Daniel Sorensen;Daniel Gianola

  • Prediction of genetic values of quantitative traits in plant breeding using pedigree and molecular markers.

    José Crossa;Gustavo De Los Campos;Gustavo De Los Campos;Paulino Pérez;Daniel Gianola

  • Sire evaluation for ordered categorical data with a threshold model

    D Gianola;JL Foulley

  • Predicting Quantitative Traits With Regression Models for Dense Molecular Markers and Pedigree

    Gustavo de los Campos;Hugo Naya;Daniel Gianola;José Crossa

  • Experimental observations of stress-driven grain boundary migration.

    T. J. Rupert;T. J. Rupert;D. S. Gianola;D. S. Gianola;Y. Gan;K. J. Hemker

  • Stress-assisted discontinuous grain growth and its effect on the deformation behavior of nanocrystalline aluminum thin films

    D.S. Gianola;S. Van Petegem;M. Legros;S. Brandstetter

  • Additive Genetic Variability and the Bayesian Alphabet

    Daniel Gianola;Daniel Gianola;Daniel Gianola;Gustavo A. de los Campos;William G. Hill;Eduardo Manfredi

  • Theory and Analysis of Threshold Characters

    Daniel Gianola

  • In situ TEM observations of fast grain-boundary motion in stressed nanocrystalline aluminum films

    Marc Legros;Daniel S. Gianola;Kevin J. Hemker

  • Genomic-assisted prediction of genetic value with semiparametric procedures.

    Daniel Gianola;Daniel Gianola;Daniel Gianola;Rohan L. Fernando;Alessandra Stella

  • Bayesian Methods in Animal Breeding Theory

    Daniel Gianola;Rohan L. Fernando

  • Bayesian inference in threshold models using Gibbs sampling

    DA Sorensen;S Andersen;D Gianola;I Korsgaard

  • Priors in Whole-Genome Regression: The Bayesian Alphabet Returns

    Daniel Gianola

  • Reproducing Kernel Hilbert Spaces Regression Methods for Genomic Assisted Prediction of Quantitative Traits

    Daniel Gianola;Daniel Gianola;Daniel Gianola;Johannes B. C. H. M. van Kaam

  • Semi-parametric genomic-enabled prediction of genetic values using reproducing kernel Hilbert spaces methods

    Gustavo De Los Campos;Daniel Gianola;Guilherme J. M. Rosa;Kent A. Weigel

  • Genomic Heritability: What Is It?

    Gustavo de los Campos;Daniel Sorensen;Daniel Gianola

  • Predicting complex quantitative traits with Bayesian neural networks: a case study with Jersey cows and wheat

    Daniel Gianola;Hayrettin Okut;Kent A Weigel;Guilherme Jm Rosa

  • Structure-property relationships from universal signatures of plasticity in disordered solids

    Ekin Dogus Cubuk;Robert Ivancic;Samuel S. Schoenholz;Samuel S. Schoenholz;Danny Strickland

  • Bayesian analysis of mixed linear models via Gibbs sampling with an application to litter size in Iberian pigs

    CS Wang;JJ Rutledge;D Gianola

  • Predicting genetic predisposition in humans: the promise of whole-genome markers.

    Gustavo de los Campos;Daniel Gianola;David B. Allison

Frequent Co-Authors

Guilherme J. M. Rosa
Guilherme J. M. Rosa University of Wisconsin–Madison
Kent A. Weigel
Kent A. Weigel University of Wisconsin–Madison
Bjørg Heringstad
Bjørg Heringstad Norwegian University of Life Sciences
Rohan L. Fernando
Rohan L. Fernando Iowa State University
José Crossa
José Crossa International Maize and Wheat Improvement Center
Gunnar Klemetsdal
Gunnar Klemetsdal Norwegian University of Life Sciences
Kevin J. Hemker
Kevin J. Hemker Johns Hopkins University
Gustavo de los Campos
Gustavo de los Campos Michigan State University
Tresa M. Pollock
Tresa M. Pollock University of California, Santa Barbara
Henner Simianer
Henner Simianer University of Göttingen

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