My name is Olya (Ola) Skulovich, I am near completion of my Ph.D. in Earth and Environmental Engineering at Columbia University. During my Ph.D. program, I worked with remote sensing data and utilized machine learning to create long-term consistent soil moisture and vegetation optical depth datasets. In particular, my work included analyzing, regridding, and deseasonalizing remote sensing data from SMAP, SMOS, AMSR-E, and AMSR-2 satellite missions to prepare the data for machine learning. On the methodological side, I developed, tested, and fine-tuned Deep and Convolutional neural networks and built a unique transfer learning training scheme to merge the patched remote sensing data into a consistent dataset. The soil moisture dataset and the corresponding paper (Scientific Data – Nature family journal, https://doi.org/10.1038/s41597-023-02053-x ) have been published. The soil moisture dataset is the only consistent quality dataset available globally, covering 18 years, explicitly targeting soil moisture extremes and anomalies. The vegetation optical depth dataset is the only L-band dataset that spawns back to 2002. After developing the datasets, my research was focused on analyzing trends and variability of soil moisture, including spatiotemporal statistical analysis and identifying regions of different dynamics.  A part of my research was dedicated to modifying and analyzing the process-based carbon cycle model (CARDAMOM). It is a model that simulates carbon fluxes and pools by assimilating data using the Metropolis-Hastings Markov chain Monte Carlo method. My part of the project included developing two new model modules for assimilating solar-induced fluorescence and vegetation optical depth data, including developing model formulation, incorporating the modules in the main model (C and Python), adjusting model uncertainties, likelihood functions, and ecological dynamical constraints, as well as analyzing the updated model’s performance, information content effect, effect on constraining respiration flux and carbon pools. In addition to that, I participated as a collaborator in several research projects investigating the effects of soil moisture and land-atmosphere feedback on European and Siberian droughts and the spatiotemporal relationship between soil moisture dynamics and vegetation productivity.  I presented the results of my research at the American Geophysical Union (AGU) Fall Meetings in 2020, 2022, and 2023, the USMILE Kickoff Meeting 2020, USMILE Meeting 2022, and LEMONTREE Science Meeting: Soil Moisture Stress 2023. I take pride in building a compelling story from scientific findings and enjoy communicating my research to various stakeholders, creating captivating presentations, and engaging public speaking.