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I’m SayedMorteza Malaekeh, a Ph.D. student in Sustainable Systems Engineering at the University of Texas at Austin, also pursuing a Master’s in Economics. I am a graduate research assistant at the Rapid, Equitable, & Sustainable Energy Transitions Lab (RESET-LAB), supervised by Prof. Sergio Castellanos, and at the Lawrence Berkeley National Laboratory (LBNL).

At LBNL and RESET-LAB, I lead research on household finance and energy economics, analyzing a 13+ TB Experian credit dataset of 10M U.S. households from 2010–2023 for solar PV adopters (treatment group) and non-adopters (control group). Using methods such as synthetic control and staggered difference-in-differences, I estimate the household-level financial impacts of solar PV adoption, bridging energy transition research with household finance. In my recent work, I studied the diffusion of residential solar photovoltaics across racial and ethnic groups in the U.S., published in Energy Policy (2025), highlighting unequal adoption patterns and their implications for equitable energy transitions and policy-making.

I am also a Visiting PhD Researcher at the California Institute of Technology (2024 & 2025) in the Division of the Humanities and Social Sciences (Economics and Computer Science), hosted by Prof. Hannah Druckenmiller. At Caltech, I develop new methods for estimating conditional average treatment effects (CATE) with image-based treatments, integrating Vision Transformers, CNNs, and Autoencoders into R-Learner and Causal Forest. These methods are applied to large-scale multimodal datasets to address economics questions in environmental policy and decision making

I hold bachelor’s and master’s degrees in Civil and Water Resources Engineering (with highest distinction) with a minor in economics from Sharif University of Technology, and was an exchange graduate student in Applied Mathematics at Saint Petersburg State University.

My research lies at the intersection of environmental & energy economics and household finance. Methodologically, I focus on causal inference in complex settings (spatial spillover effect estimation, continuous treatments; unstructured data such as images, graphs, and text), leveraging machine learning and deep learning to support decision-making under uncertainty.

Service and Activities

• Program Committe: NeurIPS Workshop in GenAI for Health (2024 & 2025)
• Reviewer: NeurIPS Workshop in GenAI for Health, Agricultural Economics, Theoretical and Applied Climatology, Environmental Monitoring and Assessment
• Board of Directors, Persian Student Society, UT Austin
• Peer Mentor, UT Austin
• Teacher, Yarigaran Education Charity Group
• Admin/Basketball Analyst and Writer, 3Sanieh
• Varsity Basketball Athelete, Sharif University of Technology