I am a Ph.D. Candidate in Applied Mathematics and Statistics at Johns Hopkins University, advised by Mateo Díaz and Soledad Villar. My research sits at the intersection of optimal transport, Riemannian optimization, and manifold learning. Beyond research, I teach and mentor. My interests include cycling, artisanal coffee, and anime/TV shows.
I obtained an M.Sc. in Applied Mathematics and Statistics from Johns Hopkins University in 2024, and an M.Sc. in Mathematics from the Universidad Nacional de Colombia in 2020, where I worked on spectral geometry and noncommutative geometry under the supervision of Prof. Sylvie Paycha and Prof. Carolina Neira Jiménez. During my time there, I completed a research internship at the Institute for Mathematics of the Universität Potsdam. I worked as a Machine Learning Engineer at Vozy, a Latin American startup specializing in conversational AI. I also completed research internships at the Johns Hopkins University Applied Physics Laboratory and the Inter-American Development Bank.
I'm looking for academic postdoc and/or industry research positions. Send me an email! (See CV)
Presented research on estimation of Morse indices from samples at the INABAG School+Conference in applied algebraic geometry in Siena, Italy.
I'm interning at the Interamerican Development Bank in the Systemic Risk Group working on Graph Models and Network Analysis applied to financial systems.
Presenting work on estimation of Morse information on manifolds at the Statistics and Data Science Workshop, Universidad de Los Andes.
Developed a systemic risk framework that models direct and cascading dependencies across external drivers and institutional risk networks. Built a regularized graph autoregressive forecasting model that combines temporal lags with higher-order network effects.
Uncovering latent factors in data by mapping onto optimized orthogonal reference frames in the Stiefel manifold, combining optimal transport with Riemannian optimization to address rotational ambiguity in high-dimensional factor models.
Mentored three high school interns at Johns Hopkins University in developing autonomous drone navigation, implementing computer vision and path optimization algorithms for efficient flight.
Advances density estimation by framing it as a supervised learning problem, enabling real-time inference through universal approximation—research conducted at the Johns Hopkins University Applied Physics Laboratory.
Voice biometrics authentication system, developed using PyTorch and TensorFlow, that surpassed contemporary state-of-the-art benchmarks. Integrated into Vozy's client authentication framework with real-time inference capabilities.
Exploring the intersection of geometry and statistical analysis to uncover hidden patterns in high-dimensional data structures.
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