Maria Fernanda González
María Fernanda is a serial entrepreneur and innovator. She has been creating technological Startups, and business solutions since 2010. She has a Ph.D in Quantum Physics (University of Barcelona, Spain), a Master in Numerical Methods (Polytechnic University of Cataluña, Spain), and MBA from IESE Business School. She has undergraduate studies in Theoretical Physics, Civil Engineering and Physical Oceanography in Colombia. María Fernanda is a specialist in creating business solutions based on Big Data and Machine Learning for large corporations, improving their annual turnover between 5 and 7 percent. In 2013 she was invited by the United States and nominated by Spain to represent the country in its prestigious IVLP (International Visitor Leadership Program). During her professional career she has received the support of several governments through different grants such as the Catalan Government, the Spanish Government, the American Government, the South Korean Government and the Dutch Government.
Her latest venture, of which she is the CEO, is called Planetai Space, which provides services to quantify the Natural Capital of our planet and the negative effects of air pollution on the environment.
Sessions
Air pollution has emerged as a critical global concern, exerting adverse impacts on natural ecosystems and exacerbating the pace of climate change. Despite the existence of mitigation strategies, the accurate quantification of methane emissions remains a formidable challenge, impeding progress towards meeting emission reduction targets set for 2030. This study is dedicated to addressing the urgent global issue of air pollution, with a particular focus on methane emissions, known for their significant contribution to climate change and associated environmental and health hazards. Conventional monitoring techniques have proven inadequate, leaving millions of abandoned oil wells unchecked in their methane emissions, thus demanding a comprehensive solution. In response, we present a novel technological advancement based on satellite data, to facilitate the precise measurement, detection, and ongoing monitoring of methane leaks. By harnessing breakthroughs in deep tech disciplines such as Earth observation integrated with machine learning, astrophysical methodologies, theoretical chemistry, and computational fluid dynamics, this technology enables the identification of methane leaks across diverse geographical locations worldwide.
Furthermore, this study underscores the critical importance of fostering collaboration and information exchange among stakeholders to optimize the effectiveness of emission reduction endeavors. Through its innovative approach and interdisciplinary collaboration, this work aspires to deliver a significant contribution towards mitigating climate change impacts and safeguarding natural resources for the benefit of future generations.