Alexandre Hild Aono

Post-doc

Current Research

Main idea:

In the Highlander Lab, I focus on developing a multi-omic simulation framework for complex traits in plant breeding as part of the HiPerBreedSim project

Goals:

  • Develop a simulation framework for complex phenotypes that captures environmental variation by incorporating weather and environmental data, along with advanced spatial modelling strategies and GxE frameworks
  • Simulate hierarchical multi-omic architectures linking genetic effects to final phenotype through non-linear molecular interactions
  • Build a generic complex-trait model integrating the weather/environmental data, molecular layer interactions, and potential causal sub-traits
  • Adapt the framework for high-throughput phenotyping simulations and image-based inference
  • Develop an R package with a user-friendly, formula-based interface
alexandre_profile

Background

Two to three phrases about your background:

I am a computer scientist with a strong passion for solving biological problems. This interest led me to pursue a PhD in Genetics and Molecular Biology (University of Campinas/Brazil) with a focus on Bioinformatics. My early research centered on developing computational strategies for analyzing omics data and generating molecular insights associated with phenotypic variation. Over time, my focus shifted toward designing and implementing computational methods to support modern breeding programs. After completing my PhD, I worked as a postdoctoral researcher at the Swedish University of Agricultural Sciences, where I developed predictive breeding strategies for genomics and high-throughput phenotyping data in potato and barley breeding programs.

I am specifically interested in:

  • Predictive breeding and quantitative genetics
  • Methods for omics data analysis, integration, and prediction
  • Strategies for modelling complex biological networks
  • High-throughput phenotyping, phenotypic prediction, and image processing for breeding applications

Programming language and software:

  • Programming languages: C/C++, Python, Java, R, Perl
  • Bioinformatics tools for genomics, transcriptomics, and multi-omics data processing, analysis, and simulation
  • Libraries and frameworks for data science and statistical modelling, machine learning and artificial intelligence, complex network analysis, and image processing

Hobbies and Personal Interests

Reading & Movies
Arts & Crafts
Volunteering

Social Media

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