Heitor Baldo

Heitor Baldo

PhD in Bioinformatics (Mathematical Neuroscience)

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About me

Hi! My name is Heitor Baldo and I hold a BS in Mathematics and an MS in Applied and Computational Mathematics both from the University of Campinas and a PhD in Bioinformatics (Mathematical Neuroscience) from the University of São Paulo. Currently, I’m a postdoctoral researcher at Leipzig University. I have experience in the areas of mathematics, applied mathematics, computer science, and bioinformatics, with an emphasis on mathematical neuroscience. More specifically, I am interested in the mathematical foundations of methods coming from various areas of pure and applied mathematics, such as abstract algebra, combinatorics, algebraic topology and geometry, discrete geometry, graph theory, category theory, complex systems, and complexity science, and how these methods, together with probabilistic, statistical, and computational methods, can be useful in mathematical neuroscience and mathematical biology.

Read my posts about science and other topics on my Substack

Research Interests

Mathematical Theories

  • Computational algebraic topology and geometry;
  • Discrete and combinatorial geometry (discrete (higher-order) structures; discrete curvatures; finite geometries);
  • Graph theory (quantitative (hyper)graph theory and network statistics; spectral (hyper)graph theory; (hyper)graph matching);
  • Matroid theory (oriented matroids; (hyper)graphic matroids; tropical matroids);
  • Category theory (categorification; monoidal categories; operads);
  • Complexity science / complex systems (complexity measures; quantitative emergence; graph celullar automata; agent-based modeling);

Applications

  • (Hyper)graphs / (hyper)graphic matroids in chemical reaction network theory;
  • Topological data analysis in RNA transcriptome;
  • Modeling DNA and RNA dynamics via graph theory and matroid theory;
  • Graph theoretical analysis and topological data analysis for neuroscience (brain connectivity networks) / connectomics and neurogenetics;
  • Neural rings and combinatorial neural codes;
  • Neural manifolds and Stiefel manifolds for neural data analysis;
  • Topological / geometric deep learning.

Master’s Thesis and PhD Thesis

Other Information