Marzieh Eidi is an early-career mathematician fascinated by the deep mathematical structures that shape data science and machine learning. Her work explores the dynamic interplay between geometry, topology, spectral analysis, and stochastic processes, seeking to bridge abstract theory with real-world learning and analysis. At the heart of her research is a passion for building unified frameworks that connect discrete and generalized curvature notions (such as Ollivier and Forman Ricci curvature), homology theories (Morse, Floer, Conley, Forman), (smooth and discrete) Hodge Laplacian, and stochastic processes into a coherent and integrated perspective. Alongside this theoretical vision, she also enjoys applying these ideas to data analysis and learning methods, where mathematical insights can illuminate structure, improve performance, and reveal new patterns in complex systems such as biological networks.