Jan Medina-López

Jan Medina-López

Tech Lead | AI & ML Systems Engineer | Distributed Data Platforms | Production AI Infrastructure

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A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.

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Implementation of an algorithm to compute the strong apparent distance of bivariate codes

Published in: Journal of Physics: Conference Series (2019)

Abstract preview: AbstractWe implement and analyze an algorithm to compute the strong apparent distance of bivariate codes, a quantity linked to lower bounds on minimum distance in abelian-code families. Building on the extension from classical BCH bounds to apparent-distance formulations, the work operationalizes the hypermatrix-bas...

Abstract

We implement and analyze an algorithm to compute the strong apparent distance of bivariate codes, a quantity linked to lower bounds on minimum distance in abelian-code families. Building on the extension from classical BCH bounds to apparent-distance formulations, the work operationalizes the hypermatrix-based procedure introduced for strong apparent distance computation. Our implementation translates the theoretical construction into an executable workflow suitable for experimentation and reproducibility. This provides a practical tool for researchers working on coding-theory bounds and algorithmic evaluation of structured codes.

Recommended citation: Bueno-Carreño, D. H., & López, J. M. (2019, January). Implementation of an algorithm to compute the strong apparent distance of bivariate codes. In Journal of Physics: Conference Series (Vol. 1160, No. 1, p. 012012). IOP Publishing. https://iopscience.iop.org/article/10.1088/1742-6596/1160/1/012012/meta

Estimating Formation Mechanisms and Degree Distributions in Mixed Attachment Networks

Published in: Journal of Physics A: Mathematical and Theoretical (2019)

Abstract preview: AbstractWe propose a probabilistic framework to estimate how random and preferential attachment jointly drive growth in evolving directed networks. The method formalizes a time-varying likelihood and establishes conditions under which local maxima exist despite changing connection probabilities during network expans...

Abstract

We propose a probabilistic framework to estimate how random and preferential attachment jointly drive growth in evolving directed networks. The method formalizes a time-varying likelihood and establishes conditions under which local maxima exist despite changing connection probabilities during network expansion. Under these conditions, an expectation-maximization approach yields convergent parameter estimates and recovers the contribution of each attachment mechanism. We also derive analytical in-degree distributions and show, through simulation, agreement between theory and observed stationary behavior. The result is a practical inference framework for understanding and modeling network formation in real-world complex systems.

Recommended citation: Medina, J. A., Finke, J., & Rocha, C. (2019). Estimating formation mechanisms and degree distributions in mixed attachment networks. Journal of Physics A: Mathematical and Theoretical, 52(9), 095001. https://iopscience.iop.org/article/10.1088/1751-8121/aaffeb

Use of Arabidopsis thaliana as a model to understand specific carcinogenic events: Comparison of the molecular machinery associated with cancer-hallmarks in plants and humans

Published in: Heliyon (2023)

Abstract preview: AbstractThis study evaluates Arabidopsis thaliana as a complementary model for specific carcinogenic processes using the cancer-hallmarks framework as a systems-level comparison between plants and humans. Instead of restricting analysis to isolated pathways, we integrate functional, network, and machine-learning per...

Abstract

This study evaluates Arabidopsis thaliana as a complementary model for specific carcinogenic processes using the cancer-hallmarks framework as a systems-level comparison between plants and humans. Instead of restricting analysis to isolated pathways, we integrate functional, network, and machine-learning perspectives to identify conserved mechanisms relevant to neoplastic transformation. The results highlight five cancer hallmarks with overlapping biological processes across species and propose candidate regulators and modules for further validation. Our findings support Arabidopsis as a focused comparative model for selected cancer properties and provide a structured route to prioritize mechanistic studies in translational cancer research.

Recommended citation: Clavijo-Buriticá, D. C., Sosa, C. C., Heredia, R. C., Mosquera, A. J., Álvarez, A., Medina, J., & Quimbaya, M. (2023). Use of Arabidopsis thaliana as a model to understand specific carcinogenic events: Comparison of the molecular machinery associated with cancer-hallmarks in plants and humans. Heliyon, 9(4). https://doi.org/10.1016/j.heliyon.2023.e15367

Dynamics of network growth and evolution: integrating non-constant edge growth, mixed attachment and reciprocity

Published in: Pramana (2025)

Abstract preview: AbstractThis work analyzes directed network evolution through a unified model that combines non-constant edge growth, mixed attachment, and reciprocity. By integrating these mechanisms, we characterize how local connection rules produce global structural patterns over time and how growth regimes alter long-term topo...

Abstract

This work analyzes directed network evolution through a unified model that combines non-constant edge growth, mixed attachment, and reciprocity. By integrating these mechanisms, we characterize how local connection rules produce global structural patterns over time and how growth regimes alter long-term topology. The framework provides both analytical and computational tools to study temporal network behavior under realistic, non-static assumptions. These results strengthen modeling and inference for evolving real-world systems where attachment dynamics and reciprocal interactions co-exist.

Recommended citation: Medina-López, J., & Ruiz, D. (2025). Dynamics of network growth and evolution: integrating non-constant edge growth, mixed attachment and reciprocity. Pramana, 99(3), 102. https://link.springer.com/article/10.1007/s12043-025-02807-9

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