Driving Policy Innovation for GHGs Emissions Mitigation and Sustainable Development

I'm a postdoctoral researcher at Tsinghua University's Institute of Energy, Environment and Economy, supported by the prestigious Shuimu Tsinghua Scholarship within the Department of Nuclear and New Energy Technology. Under the mentorship of Pr. Zhang Da, my work focuses on designing smart policy frameworks for greenhouse gas (GHG) emissions mitigation. By integrating advanced artificial intelligence (AI), mathematical modeling and remote sensing technologies, I aim to provide actionable, data-driven insights to guide policymakers in implementing effective strategies to reduce GHG emissions—particularly methane—in the fossil fuel industry. This approach represents a critical step forward in the global fight against climate change, bridging the gap between cutting-edge science and practical, scalable policy solutions.

I obtained a PhD degree in Mathematics and Computer Science at the Applied Mathematic Center (CMAP) of École Polytechnique and the Data Sciences, Signal and Image Processing Lab (DaSSIP) at ISEP under the supervision of Pr. Éric Moulines, Pr. Jérémie Sublime and Pr. Thomas Lauvaux.

My doctoral research, based on the use of deep learning and remote sensing technologies, focused on developping the first Dynamic and Intelligent Methane Emissions Inventory, capable of automatically tracking and analyzing emitting behaviors over time across multiple levels in the oil and gas industry. These emissions profiles enable the characterization of methane-emitting patterns, providing a robust, data-driven foundation for designing and implementing effective methane mitigation policies and regulations. This work, conducted in collaboration with the United Nations Environment Programme (UNEP), contributes directly to the International Methane Emissions Observatory (IMEO) and Oil and Gas Methane Partnership (OGMP), two flagship initiatives driving global methane reduction efforts.

In addition to my work on methane mitigation, I have developed the SDG-Meter, an advanced text classification tool under UNEP that automates the alignment of government project proposals with the 17 Sustainable Development Goals (SDGs). Recognized as the 9th best project in the prestigious IRCAI-UNESCO Top 100 AI solutions for sustainable development, this tool empowers policymakers to track and accelerate progress toward achieving the SDGs with greater precision and efficiency.

I'm also a core team member of  Climate Change AI (CCAI), a global non-profit that catalyzes impactful work at the intersection of climate change and machine learning.