I am first-year PhD candidate at the Department of Civil and Environmental Engineering at Politecnico di Milano. Prior to joining MIPORE research group, I was a Graduate Research Assistant in Remote Sensing of Water Cycle and Hydrometeorology (RSWatCH) group in the Department of Hydrology and Atmospheric Sciences (HAS) of University of Arizona under the supervision of Prof. Hoshin Gupta. Also, I was an undergrad at Sharif University of Technology (SUT), majoring in Civil Engineering with a minor in Math. Over my research career so far, I got supported by funding from NASA, Earth Dynamic Observatory of Arizona, and Marie Skłodowska-Curie European Industrial Doctorate (EID) programme as an Early Stage Researcher. I am interested in Physics-informed machine learning problems in general. More specifically, my research mainly focuses on principled techniques for extracting knowledge from the complex structures or networks and using it to build predictive models – in that way merging insights both from experimental data mining and machine learning. My PhD research within REMEDI project (ESR4), lies at the intersection of Probabilistic Machine Learning and Data Driven Scientific Computing with a focus on leveraging physics-inspired machine learning methods to explore and stochastically model Geo-Hydrologic dynamical systems. I am working on developing novel strategies to design learning machines that leverage the underlying physical laws and/or governing equations, to get insights on high-dimensional complex subsurface flow systems. I previously touched the base of Bayesian networks, and Gaussian process kernels.