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Bio
I am interested in the application of statistical and machine learning techniques to complex trait genomic prediction and data integration, including the use of genome and phenome data. Phenotyping is a most challenging and limiting step in the immediate future, and I am willing to develop machine and deep learning tools to automatize phenotype collection and analysis.
The philosophy underlying my research interests is to help building a "data-driven agriculture and farming", i.e., to leverage technological innovations, such as deep learning techniques, to move in the direction of precision farming.
I am proficient in R, python, bash, linux administration. I have ample experience in numerous statistical and machine learning tools (Bayesian modeling, SVM, Random Forest, Boosting, K-means, among others), web scrapping, deep learning (CNN, MLP, Recurrent networks, embedding networks, hyperparameter optimization), genomic prediction and image analysis. All my software tools are available at https://github.com/lauzingaretti.
Research Interests
Papers共 36 篇Author StatisticsCo-AuthorSimilar Experts
By YearBy Citation主题筛选期刊级别筛选合作者筛选合作机构筛选
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引用量
主题
期刊级别
合作者
合作机构
GENETICS SELECTION EVOLUTIONno. 1 (2023): 29-12
Plant Phenomics (2022): 1-1
biorxiv(2022)
Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP) (2022)
biorxiv(2022)
Proceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP) (2022)
semanticscholar(2021)
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Author Statistics
#Papers: 36
#Citation: 420
H-Index: 10
G-Index: 20
Sociability: 5
Diversity: 0
Activity: 1
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- 学生
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