Daniel Hardesty Lewis
Data Scientist & Researcher
Developing interpretable ML systems and scalable computational infrastructure.
I'm a researcher at Columbia University focused on explainable AI and distributionally robust machine learning for high-stakes financial applications.
Previously, I spent 5+ years at the Texas Advanced Computing Center scaling climate and flood models on world-leading supercomputers, leading projects for the $40M Texas Disaster Information System.
My research has been published in ACM TIST, received a mention in the AAAI Presidential Address, and contributed to the Bagnold Medal in geomorphology.
I founded Summit Geospatial to deliver the highest quality terrain data in Texas, and I'm building PoliBOM (Top 5% YC applicant) for tariff intelligence.
About
I apply high-performance computing techniques to terrain and flood models, developing reproducible and scalable workflows from source data all the way out to web services—at scales as small as a parcel and as large as countries.
My current research focuses on developing attribution methods that remain faithful under distribution shift for high-stakes financial applications, combining insights from variational inference and robust optimization.
Education
M.S. Urban Planning
Columbia University · Expected 2026
B.S. Pure Mathematics
University of Texas at Austin · 2017
Certificate, Scientific Computation
University of Texas at Austin · 2017
Languages
Experience
Research Assistant · Columbia University
Working under Dir. Financial Engineering Ali Hirsa on latent factor models achieving better than commercial R² on backtested holdout prediction. Developing SHAP explainability methods for financial deep learning.
Founder · Summit Geospatial
Developed the highest quality seamless elevation data for Texas. Targeting licensing to AI labs including OpenAI and Cohere.
Co-founder · PoliBOM
Top 5% YC applicant. Built tariff intelligence platform that influenced KPMG's Tariff Modeller launch.
Senior Data Scientist & Technical Lead · Texas Advanced Computing Center
Led principal project for $40M TDIS disaster resiliency initiative. Scaled climate models on world-leading supercomputers with million-node jobs. Contributed research towards Prof. Passalacqua's Bagnold Medal.
Data Scientist & Research Engineer · Texas Advanced Computing Center
Competed in DARPA World Modelers program. Research mentioned in AAAI Presidential Address by Prof. Yolanda Gil. Deep learning consulting for Petrobras.
Co-instructor · University of Texas at Austin
Taught graduate courses: Machine Learning for the Geosciences, Intelligent Systems for the Geosciences, Scientific Computation (C++, CUDA, HPC, optimization).
Projects
Individual home price forecasting using VAE architecture. Achieved 12% MAPE against Zillow's 8.4% in Manhattan—the hardest US market. Interest from a16z & MetaProp.
Led development of core elevation data layer for web-based spatial data system supporting resilient decision-making at state and local levels. Part of $40M GLO initiative.
Developed computationally efficient methods to produce high-resolution (1m) flood inundation maps from National Water Model outputs for emergency response personnel.
Integrated climate, hydrology, agriculture, and socioeconomic models for DARPA World Modelers. Published in ACM TIST and mentioned in AAAI Presidential Address.
Flood Hazard Assessment
Partnered with US Army Corps of Engineers to statistically model compound flood hazards in coastal Texas, integrating high-resolution topographic data with hydrological models.
Publications
Peer-Reviewed Articles
Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making
Gil, Y., Garijo, D., Khider, D., Knoblock, C.A., et al. (incl. D. Hardesty Lewis)
ACM Transactions on Interactive Intelligent Systems (TIST), 11(2)
An Intelligent Interface for Integrating Climate, Hydrology, Agriculture, and Socioeconomic Models
Garijo, D., et al. (incl. D. Hardesty Lewis)
ACM IUI'19
A Semantic Model Catalog to Support Comparison and Reuse
Garijo, D., et al. (incl. D. Hardesty Lewis)
9th International Congress on Environmental Modelling and Software
Selected Presentations
Estimating Inundation Extent and Depth from National Water Model Outputs and High Resolution Topographic Data
Presented to NOAA
Vector and Raster GIS Processing with Python in Jupyter Notebooks
TACC Institute of Planet Texas 2050
From MODFLOW-96 to MODFLOW-2005, ParFlow, and Others
American Geophysical Union Fall Meeting
Skills
Programming Languages
Libraries & Frameworks
Database Systems
Scientific Software
Cloud & Infrastructure
ML & Statistics
Contact
I'm always interested in discussing research collaborations, consulting opportunities, or just connecting with others working on interpretable ML, HPC, or geospatial computing.
Currently based in New York City, pursuing my M.S. at Columbia University.