Daniel Estevez-Moya

I am a mathematician and physicist with a deep interest in nonlinear dynamics, complex systems, and machine learning. My academic path has taken me from a rigorous mathematical foundation to cutting-edge research in computational physics, always at the intersection of theory and computation.

Education

I studied Mathematics at the University of Havana (2013–2017), earning a Bachelor's degree. The program provided a broad and solid training in pure and applied mathematics, covering analysis, algebra, topology, probability, and mathematical physics. My undergraduate thesis explored the Minority Game through the lens of information theory.

I then pursued a Master's degree in Mathematical Sciences at the same university (2017–2020), specializing in Probability and Statistics. My coursework included stochastic processes, advanced probability theory, and dynamical systems. The master's thesis focused on nonlinear dynamics — specifically synchronization in coupled oscillator systems and bifurcation structures in circle maps.

I am currently completing my PhD at the Max Planck Institute for the Physics of Complex Systems in Dresden, Germany, under the supervision of Holger Kantz in the Nonlinear Dynamics and Time Series Analysis group. My research focuses on reservoir computing for short-term forecasting of chaotic dynamical systems, combining ideas from recurrent neural networks, dynamical systems theory, and time-series analysis.

Technical Skills

Programming is central to my work and something I genuinely enjoy. Python is my primary language, and I am highly proficient in the scientific and machine learning ecosystem: PyTorch, JAX, TensorFlow, Keras, Scikit-Learn, NumPy, SciPy, Pandas, and Matplotlib. I also work with Julia for high-performance numerical computing and have some experience with Rust.

Beyond scientific computing, I am comfortable with modern software engineering practices: version control with Git, CI/CD pipelines with GitHub Actions, containerization with Docker, and package publishing on PyPI. I have developed and maintain my own open-source Python library, ResDAG, for reservoir computing with directed acyclic graph architectures.

Research Interests

My research sits at the crossroads of nonlinear dynamics, machine learning, and time-series analysis. I am particularly interested in how recurrent neural network architectures can be understood and improved through the lens of dynamical systems theory. More broadly, I am drawn to problems where rigorous mathematics meets practical computation: chaotic forecasting, network topology, stochastic processes, and the design of principled machine learning methods.