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Visiting PhD Researcher at Caltech - Causal AI, Economics, Sustainability

I am seeking Summer 2026 internships in Data Science and Applied AI, Quant Research, and/or Energy.

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

At RESET-LAB, I lead a project with Lawrence Berkeley National Laboratory on household finance and energy economics, working with a 13+ TB Experian credit report dataset covering 10M U.S. households (2010–2023). Using synthetic control and staggered difference-in-differences, I study the household financial impacts of residential solar adoption. My recent work on racial and ethnic gaps in solar PV diffusion is published in Energy Policy (2025).

I am also a Visiting PhD Researcher at the California Institute of Technology (2024 & 2025) in HSS (Economics & Computer Science), hosted by Prof. Hannah Druckenmiller. At Caltech, I develop conditional average treatment effect estimation methods for image-based treatments, integrating Transformer Architecture, CNNs, and Autoencoders into R-Learner and Causal Forest for large-scale multimodal policy datasets.

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

My research spans energy & environmental economics, household finance, and causal ML, focusing on continuous treatments, spatial spillovers, and unstructured data (images, graphs, text) to support decision-making under uncertainty.

Technical Skills

Programming & ML: Python (PyTorch, scikit-learn, Pandas, NumPy), R, SQL , STATA, MATLAB
Deep Learning: Vision Transformers, CNNs, Autoencoders, LLMs (HuggingFace, LangChain, LoRA/PEFT)
Causal ML & Econometrics: EconML, DoWhy, R-Learner, Causal Forests, Difference-in-Differences, Regression Discontinuity, A/B Testing, Synthetic Control
Quantitative Finance: Backtesting (Backtrader), Time-series Forecasting (ARIMA, VAR, GARCH), Stochastic Processes (GBM, OU), Monte Carlo Simulation, Alpha Signal Development
Data Engineering: Spark, Databricks, Distributed Pipelines, Large-scale Data Preproccessing (130M+ obs; 6k Vars; 13+ TB)
Systems & Cloud: CUDA, Slurm, Linux/Bash, Docker, Kubernetes, AWS, GCP, Git
Geo/Spatial: Gepandas, Rasterio, Tifffile, GDAL, ArcGIS, QGIS, Google Earth Engine

Service & Activities

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