Shahab Mousavi

Shahab is a PhD candidate in Management Science & Engineering at Stanford University, where he applies his computer science background to tackle the climate challenge through energy modeling, wildfire mitigation, supply chain decarbonization, and AI data center optimization. His research sits at the intersection of artificial intelligence, energy systems, and environmental policy, focusing on real-world applications that can drive meaningful climate impact through innovative technology solutions. He currently works on deep decarbonization pathways as part of Stanford's Energy Modeling Forum EMF37 team, develops AI-driven carbon accounting frameworks and analyzes the techno-economics of measurement, reporting, and verification (MRV) solutions for carbon removal projects through the Sustainable Finance Initiative, and explores AI applications for wildfire management and energy optimization for data centers within the Bits & Watts Initiative. His recent work includes developing AI-powered knowledge graphs for supply chain emissions liability and investigating how data centers can be optimally sited and operated to minimize their carbon footprint, cooling water impacts, and grid strain. Previously, he served as Head Teaching Assistant for Andrew Ng's Deep Learning course (CS230), mentored undergraduate researchers, and founded Stanford's Canadian Student Organization. Outside of research, Shahab enjoys leading student communities, exploring entrepreneurship through Stanford's innovation programs, and bridging his dual engineering background from computer science and civil & environmental engineering.