Gabriele M
- Research Program Mentor
PhD candidate at University of Texas Austin (UT Austin)
Expertise
physics (theoretical and observational cosmology, astro-particle physics), mathematics and statistics
Bio
I am a theoretical cosmologist and Ph.D. candidate at the University of Texas at Austin, where I study how observations of the universe can reveal new fundamental physics. My work focuses on developing precise tools to extract signatures of neutrinos, dark matter, and primordial physics from cosmological data, particularly the cosmic microwave background and large-scale structure. I am especially motivated by the idea that the universe itself acts as a laboratory, allowing us to probe energy scales and particles far beyond the reach of terrestrial experiments. More broadly, I am passionate about connecting theory and data in ways that sharpen our understanding of the fundamental laws that shape cosmic evolution. Outside of research, I am a lifelong athlete and former professional 400m hurdler, and I still run every day. Sport has shaped the way I approach science — with discipline, persistence, and long-term vision. I also enjoy mentoring younger students, cooking Italian food, and building projects that make physics more accessible, such as interactive cosmology tools for high school and undergraduate students.Project ideas
Extending CMBverse: Visualizing Secondary Anisotropies of the CMB
The cosmic microwave background (CMB) not only encodes information about the early universe, but also carries subtle imprints from the late-time universe as photons travel toward us. In this project, the student will develop clear visualizations of one or more secondary anisotropy effects — gravitational lensing, the thermal Sunyaev–Zel’dovich (tSZ) effect, and the kinetic Sunyaev–Zel’dovich (kSZ) effect — and integrate them into the CMBverse website. The goal will be to produce high-quality plots that isolate and explain how each effect modifies the primary CMB signal, accompanied by concise, accessible explanations describing what physical processes generate these distortions and what they teach us about dark matter, dark energy, and structure formation. By the end of the project, the student will have contributed new educational research tools to a public-facing platform, while gaining experience in numerical modeling, scientific visualization, and translating technical physics into clear explanations. This project is well-suited for students interested in connecting theory, computation, and science communication.
Testing a New Dark Energy Parametrization with Mock Cosmological Data
Recent large-scale structure data have sparked renewed interest in the possibility that dark energy may evolve over cosmic time. In this project, the student will test the robustness of a newly proposed dark energy energy-density parametrization using mock datasets. The project will involve generating synthetic cosmological expansion data under controlled assumptions, performing Bayesian statistical inference to recover model parameters, and comparing the performance of the new parametrization against commonly used alternatives. The student will investigate questions such as: Does the new model introduce biases? Does it improve flexibility without overfitting? Under what conditions can evolving dark energy be reliably detected? This project serves as a hands-on introduction to statistical inference, model comparison, and the careful testing of theoretical proposals before applying them to real observational data.