RESEARCH
Juan develops and applies dynamical models to infer gene regulatory interactions underlying cellular differentiation. By integrating high-dimensional single-cell transcriptomics and RNA-velocity inference within a Hopfield network formalism, he derives cell-type–specific interaction parameters that capture both the stability and trajectory of cell states. He developed scHopfield, a Python package that fits continuous Hopfield model parameters to infer cell-type specific regulatory interactions. Beyond this core method, Juan investigates the decomposition of Hopfield dynamics, revealing distinct mechanistic contributions to system behavior; also he analyzes small regulatory motifs within the Hopfield framework to understand their approximation limits and motif-specific dynamics. These efforts bridge mechanistic modeling and data-driven inference to generate predictive insights into cell-fate decisions and pinpoint key regulators for experimental validation.

BIOGRAPHY
Juan holds an M.Sc. (cum laude) in Mathematical Modelling (Erasmus Mundus MathMods) from the University of L’Aquila and the University of Hamburg, where he applied optimization algorithms to Proton-Therapy planning, and dual B.Sc. degrees (with honors) in Pure Mathematics and Engineering Physics from the National University of Colombia. As a KAUST VSRP Visiting Student Researcher, he started his worke in modeling differentiation processes using dynamical systems. He is currently a Ph.D. candidate in Applied Mathematics and Computational Sciences at KAUST under the supervision of Prof. Jesper Tegnér. During his doctoral studies, he has served as a teaching assistant for graduate courses in Numerical Optimization and Numerical Linear Algebra.