Scott Staniewicz

Scott Staniewicz

Experience

• Oversaw product launch and application development of first commercial InSAR offering.
• Developed real-time/onboard GPU-compatible SAR focusing, InSAR, and AMTI algorithms.

• Led the Algorithm Development Team for the OPERA Sentinel-1 North American Surface Displacement product, the largest public InSAR-derived ground displacement data set.
• Architected and deployed scalable cloud infrastructure using AWS Batch to process hundreds of terabytes of Sentinel-1 SAR data, enabling automated InSAR time series analysis across North America with robust production-ready software.
• Managed cross-functional collaboration between Algorithm Development, Project Science, and Software Data System teams while directing 6 team members, overseeing multi-product budgets, and ensuring on-time delivery of project milestones.

• Generated a new surface deformation data set with millimeter-level accuracy and 100-meter spatial resolution over the entirety of West Texas.
• Developed a new method for mitigating atmospheric noise in Interferometric Synthetic Aperture Radar (InSAR) data.
• Created a computer vision algorithm for automatic detection of InSAR surface deformation signals.
• Implemented open source tools in Python and Julia for InSAR data processing.

• Architected Python codebase for marketing campaigns generating $700k+ in monthly revenue.
• Managed team of 6, developed team strategy with CEO and executive board.
• Analyzed large-scale (10s of TB) datasets in SQL, Spark, and MapReduce.

• Created algorithms to combine multiple sensors using Kalman filtering for robust navigation.
• Published two technical reports on signal processing features of GPS receivers.
• Designed experiments to test robustness of GNSS equipment under varying interference conditions.

Education

• Ph.D. in Aerospace Engineering, University of Texas at Austin (August 2022), GPA: 3.97/4.0
• B.S. in Electrical Engineering and Mathematics, Tufts University (May 2013), Summa Cum Laude

Technical Skills

Programming

Python, C++, Rust, SQL, Julia, JavaScript, AWS cloud computing, JAX, PyTorch, Spark

Domain expertise

Synthetic aperture radar and interferometric synthetic aperture radar, global navigation satellite systems, digital signal processing, optimization theory, deep learning, computer vision, large-scale geospatial data processing

Honors and Awards

• NSF Graduate Research Fellowship Program, Honorable Mention, 2019
• University of Texas Graduate Continuing Fellowship, 2020-2021
• SXSW SpaceCRAFT Autonomous Navigation/Exploration Challenge, 2nd place, 2019
• Capital One Baseball Academic All-America, Division III First Team, 2013

Selected Publications

• Staniewicz, S., Mirzaee, S., Fattahi, H., Oliver-Cabrera, T., Havazli, E., Gunter, G., … & Bekaert, D. (2026). Near-real-time InSAR phase estimation for large-scale surface displacement monitoring. IEEE Transactions on Geoscience and Remote Sensing.
• Staniewicz, S. J., & Chen, J. (2025). Automatic detection of InSAR deformation and tropospheric noise features using computer vision: A case study over West Texas. Journal of Geophysical Research: Solid Earth, 130(7), e2024JB029614.
• Staniewicz, S. J., Mirzaee, S., Gunter, G. M., Oliver-Cabrera, T., Havazli, E., & Fattahi, H. (2024). Dolphin: A Python package for large-scale InSAR PS/DS processing. Journal of Open Source Software, 9(103), 6997.
• Staniewicz, S., Chen, J., Lee, H., Olson, J., Savvaidis, A., Reedy, R., Breton, C., Rathje, E., & Hennings, P. (2020). InSAR reveals complex surface deformation patterns over an 80,000 km² oil-producing region in the Permian Basin. Geophysical Research Letters, 47(21), e2020GL090151.