People

Michael Alexander Ramírez Sierra pursued his Ph.D. in Bioinformatics at Goethe-Universität Frankfurt am Main, Germany, specializing in theoretical biophysics and computational biology under the guidance of Dr. Thomas R. Sokolowski and Prof. Dr. Franziska Matthäus. His doctoral research focused on developing high-dimensional, spatially resolved mathematical models to investigate early embryonic development and cell-fate decision-making. By employing AI-powered, simulation-based inference methods, he parameterized complex biological models to generate predictive insights, demonstrating how stochasticity and cell-cell interactions contribute to robust and reproducible developmental outcomes. This work lays the groundwork for potentially creating digital twins of developmental systems, advancing the understanding of biological complexity and resilience.

He underwent rigorous training that established the foundation for his interdisciplinary approach. He holds an M.Sc. in Mathematical Modelling from Uniwersytet Śląski w Katowicach, Poland, and an M.Sc. in Mathematical Engineering from Università degli Studi dell’Aquila, Italy. His academic journey began with a B.Sc. in Electronics Engineering from Universidad de Ibagué, Colombia.

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I come from Italy where I graduated from the Master’s program in Quantitative and Computational Biology (University of Trento) and I am now a PhD student in Biophysics.
My work aims to use Molecular Dynamics simulations to understand the structural dynamics and organization of proteins that play an important role in health and disease, including IRE1 and the HIV protein Env.

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I am currently a PhD student since 2023, focusing on the development of deep generative models for molecular dynamics and enhanced sampling.
My work involves leveraging diffusion models and flow matching, with an emphasis on grounding these methods in the known physics and symmetries of molecular systems.
Prior to my PhD, I worked as a data scientist specializing in using deep learning techniques for money laundering detection.
In addition to my research, I am passionate about self-organizing systems, neural cellular automata, and creating deep learning methods for artistic applications.

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PhD Student at the CovinoLab since 2021.
Graduated in Physics at the University of Pisa (Bachelor’s degree, 2017), and in Quantitative and Computational Biology at the University of Trento (Master’s, 2020). His academic career is leading him North. With a knack for developing methods, aims to channel his talent towards good scientific practices. Currently using Artificial-Intelligence to investigate complex processes in biological systems. Hopes to update this description soon with some exciting results.

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Francesco Carnovale holds a bachelor’s in Physics (University of Milan “La statale”) and a master’s in Quantitative Computational Biology (University of Trento).
His master’s project involved the study of Interleukin 22 receptor transmembrane domains and their interaction with the membrane lipid environment, with a focus on cholesterol-specific interactions.
He joined the SBP (Statistical and Biological Physics group) at University of Trento as a PhD student in Nov. 2022 taking part to the European project “MIMOSA” whose final aim is to extend Atom Probe Tomography technique to biological system to study proteins structures and conformations.
His contribution to the project consists in studying protein-laser interaction with the aid of ab-initio calculation (more specifically TD-DFT simulations) and protein conformational changes, due to interaction with a silica embedding matrix, with the aid of MD simulations.
Since then he has also continued to work on his master project thanks to an active collaboration with PI’s lab. His experience spans across physics, biophysics, programming and high performance computing (HPC), with a focus on MD simulations and ab-initio techniques.